molecules_density_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.cluster import DBSCAN\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "import csv\n",
    "import os\n",
    "import pandas as pd\n",
    "import timeit\n",
    "import math\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.colors as colors\n",
    "from textwrap import wrap\n",
    "from mpl_toolkits.mplot3d.art3d import Poly3DCollection, Line3DCollection\n",
    "from mpl_toolkits.mplot3d import proj3d\n",
    "from itertools import product, combinations\n",
    "import sys\n",
    "from tqdm import tqdm\n",
    "#matplotlib.use('Qt5Agg')\n",
    "start = timeit.default_timer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['d:\\\\Projects\\\\molecules article',\n",
       " 'c:\\\\Users\\\\giorg\\\\miniconda3\\\\envs\\\\algonauts_test\\\\python39.zip',\n",
       " 'c:\\\\Users\\\\giorg\\\\miniconda3\\\\envs\\\\algonauts_test\\\\DLLs',\n",
       " 'c:\\\\Users\\\\giorg\\\\miniconda3\\\\envs\\\\algonauts_test\\\\lib',\n",
       " 'c:\\\\Users\\\\giorg\\\\miniconda3\\\\envs\\\\algonauts_test',\n",
       " '',\n",
       " 'c:\\\\Users\\\\giorg\\\\miniconda3\\\\envs\\\\algonauts_test\\\\lib\\\\site-packages',\n",
       " 'c:\\\\Users\\\\giorg\\\\miniconda3\\\\envs\\\\algonauts_test\\\\lib\\\\site-packages\\\\win32',\n",
       " 'c:\\\\Users\\\\giorg\\\\miniconda3\\\\envs\\\\algonauts_test\\\\lib\\\\site-packages\\\\win32\\\\lib',\n",
       " 'c:\\\\Users\\\\giorg\\\\miniconda3\\\\envs\\\\algonauts_test\\\\lib\\\\site-packages\\\\Pythonwin']"
      ]
     },
     "execution_count": 503,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys \n",
    "sys.path "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_risultati = \"./FINALE/\"\n",
    "path_dati = './Dati/'\n",
    "cartella_e_nome_output = \"Replica1\"\n",
    "#path_cartella = path_dati + 'Solo_H2O\\\\' + cartella_e_nome_output #cartella contenente i files.pdb\n",
    "path_cartella = path_dati + 'H2O_NaCl\\\\' + cartella_e_nome_output #cartella contenente i files.pdb\n",
    "tipo_di_ambiente = \"NaCl\"\n",
    "#COSTANTI FONDAMENTALI\n",
    "dimensioni_scatola = [39.606,39.606,50.]\n",
    "numero_di_frame = 3000\n",
    "distanza_max_clusterizzazione = 4\n",
    "numero_minimo_elementi = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nuovi_indici(vecchi_indici):\n",
    "    indici_o_nuovi = np.array(vecchi_indici + (vecchi_indici*2))\n",
    "    indici_h1_nuovi = np.array(indici_o_nuovi + 1)\n",
    "    indici_h2_nuovi = np.array(indici_o_nuovi + 2)\n",
    "    return indici_o_nuovi, indici_h1_nuovi, indici_h2_nuovi\n",
    "\n",
    "def periodicita(L = dimensioni_scatola[0]): #L larghezza della scatola\n",
    "    total = 0\n",
    "    for d in range(data_solo_ossigeno.shape[1]):\n",
    "        pd = pdist(data_solo_ossigeno[:, d].reshape(-1, 1))\n",
    "        if (d != 2):\n",
    "            pd[pd > (0.5 * L)] -= L\n",
    "        total += pd ** 2\n",
    "\n",
    "    total = np.sqrt(total)\n",
    "    square = squareform(total)\n",
    "    return square\n",
    "\n",
    "def outofbox():\n",
    "    # TEST PER VALORI DI |X,Y| MAGGIORI DI 19.803 - OVVERO L/2\n",
    "    data_solo_ossigeno[:, :2] = np.where((data_solo_ossigeno[:, :2] > 19.803) & (((data_solo_ossigeno[:, :2] // 19.803) % 2) == 1), (-(19.803) + (data_solo_ossigeno[:, :2] % 19.803)), data_solo_ossigeno[:, :2])\n",
    "    data_solo_ossigeno[:, :2] = np.where((data_solo_ossigeno[:, :2] > 19.803) & (((data_solo_ossigeno[:, :2] // 19.803) % 2) == 0), data_solo_ossigeno[:, :2] % 19.803, data_solo_ossigeno[:, :2])\n",
    "    # test per valori di x,y minori di -20\n",
    "    data_solo_ossigeno[:, :2] = np.where((data_solo_ossigeno[:, :2] < (-19.803)) & (((data_solo_ossigeno[:, :2] // (-19.803)) % 2) == 1),\n",
    "                                         [+19.803 + (data_solo_ossigeno[:, :2] % (-19.803))], data_solo_ossigeno[:, :2])\n",
    "    data_solo_ossigeno[:, :2] = np.where((data_solo_ossigeno[:, :2] < (-19.803)) & (((data_solo_ossigeno[:, :2] // (-19.803)) % 2) == 0), data_solo_ossigeno[:, :2] % (-19.803),\n",
    "                                         data_solo_ossigeno[:, :2])\n",
    "    return data_solo_ossigeno\n",
    "\n",
    "\n",
    "def file_len(fname):\n",
    "    with open(fname) as f:\n",
    "        for i, l in enumerate(f):\n",
    "            pass\n",
    "    return i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [],
   "source": [
    "colonne_nomi=np.array([\"0\",\"1\",\"2\",\"3\",\"4\",\"x\",\"y\",\"z\"]) #colonne del dataframe .pdb\n",
    "colonne_elemento_e_coordinate = [\"2\",\"x\",\"y\",\"z\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Simulazione_Celsius24_Milliatmosfere7.500_5Layers4Mobili_300+300Msteps.pdb', 'Simulazione_Celsius24_Milliatmosfere8.750_5Layers4Mobili_300+300Msteps.pdb', 'Simulazione_Celsius24_Milliatmosfere9.000_5Layers4Mobili_300+300Msteps.pdb']\n"
     ]
    }
   ],
   "source": [
    "files_pdb = os.listdir(path_cartella)  #crea una lista contenente tutti i nomi dei file\n",
    "print(files_pdb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./Dati/H2O_NaCl\\Replica1\\Simulazione_Celsius24_Milliatmosfere7.500_5Layers4Mobili_300+300Msteps.pdb\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\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",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>REMARK</td>\n",
       "      <td>MODEL</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MODEL</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1</td>\n",
       "      <td>NA</td>\n",
       "      <td>SLT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.330</td>\n",
       "      <td>-18.789</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1</td>\n",
       "      <td>NA</td>\n",
       "      <td>SLT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.988</td>\n",
       "      <td>-18.789</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1</td>\n",
       "      <td>NA</td>\n",
       "      <td>SLT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.501</td>\n",
       "      <td>-15.960</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0      1    2    3    4       x       y    z\n",
       "0  REMARK  MODEL    1  NaN  NaN     NaN     NaN  NaN\n",
       "1   MODEL      1  NaN  NaN  NaN     NaN     NaN  NaN\n",
       "2    ATOM      1   NA  SLT  1.0  12.330 -18.789  0.0\n",
       "3    ATOM      1   NA  SLT  1.0  17.988 -18.789  0.0\n",
       "4    ATOM      1   NA  SLT  1.0   9.501 -15.960  0.0"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 0\n",
    "path = path_cartella + f\"\\{files_pdb[x]}\"\n",
    "print(path)\n",
    "dataframe = pd.DataFrame()\n",
    "dataframe = pd.read_csv(path, delim_whitespace=True, names=colonne_nomi, low_memory=False, keep_default_na=False, na_values=[''])\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3127818</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1</td>\n",
       "      <td>OW</td>\n",
       "      <td>ACQ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.640</td>\n",
       "      <td>-16.217</td>\n",
       "      <td>8.536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127819</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1</td>\n",
       "      <td>HW</td>\n",
       "      <td>ACQ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.866</td>\n",
       "      <td>-15.708</td>\n",
       "      <td>8.913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127820</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1</td>\n",
       "      <td>HW</td>\n",
       "      <td>ACQ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.539</td>\n",
       "      <td>-17.188</td>\n",
       "      <td>8.753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127821</th>\n",
       "      <td>ENDMDL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127822</th>\n",
       "      <td>END</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0    1    2    3    4       x       y      z\n",
       "3127818    ATOM    1   OW  ACQ  1.0  14.640 -16.217  8.536\n",
       "3127819    ATOM    1   HW  ACQ  1.0  13.866 -15.708  8.913\n",
       "3127820    ATOM    1   HW  ACQ  1.0  14.539 -17.188  8.753\n",
       "3127821  ENDMDL  NaN  NaN  NaN  NaN     NaN     NaN    NaN\n",
       "3127822     END  NaN  NaN  NaN  NaN     NaN     NaN    NaN"
      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [],
   "source": [
    "troncone_numero_ = \"2\"\n",
    "dataframe.loc[1:, \"1\"] = dataframe.loc[1:, \"1\"].apply(pd.to_numeric, errors='coerce')\n",
    "posizione_model = np.append([0], np.where(dataframe.loc[1:, \"1\"] > 1))\n",
    "posizione_model += 1\n",
    "numero_di_linee_totali = file_len(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NX     1176000\n",
       "CX     1176000\n",
       "NA      294000\n",
       "CL      294000\n",
       "HW      121214\n",
       "OW       60607\n",
       "NaN       6001\n",
       "1            1\n",
       "Name: 2, dtype: int64"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe[\"2\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "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",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>modello</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>REMARK</td>\n",
       "      <td>MODEL</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MODEL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>SLT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.330</td>\n",
       "      <td>-18.789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>SLT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.988</td>\n",
       "      <td>-18.789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>SLT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.501</td>\n",
       "      <td>-15.960</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0      1    2    3    4       x       y    z  modello\n",
       "0  REMARK  MODEL    1  NaN  NaN     NaN     NaN  NaN      NaN\n",
       "1   MODEL    1.0  NaN  NaN  NaN     NaN     NaN  NaN      NaN\n",
       "2    ATOM    1.0   NA  SLT  1.0  12.330 -18.789  0.0      NaN\n",
       "3    ATOM    1.0   NA  SLT  1.0  17.988 -18.789  0.0      NaN\n",
       "4    ATOM    1.0   NA  SLT  1.0   9.501 -15.960  0.0      NaN"
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# crea una nuova colonna chiamata frame piena di NaN\n",
    "dataframe[\"modello\"] = np.nan\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3000"
      ]
     },
     "execution_count": 513,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# crea un dataframe vuoto con le colonne \"atom\", \"x\", \"y\", \"z\", \"frame\"\n",
    "len(posizione_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>modello</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3126789</th>\n",
       "      <td>MODEL</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3126790</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>SLT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.330</td>\n",
       "      <td>-18.789</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3126791</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>SLT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.988</td>\n",
       "      <td>-18.789</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3126792</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>SLT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.501</td>\n",
       "      <td>-15.960</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3126793</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>SLT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.159</td>\n",
       "      <td>-15.960</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127816</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HW</td>\n",
       "      <td>ACQ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.914</td>\n",
       "      <td>1.407</td>\n",
       "      <td>-7.618</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127817</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HW</td>\n",
       "      <td>ACQ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.660</td>\n",
       "      <td>2.621</td>\n",
       "      <td>-8.415</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127818</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OW</td>\n",
       "      <td>ACQ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.640</td>\n",
       "      <td>-16.217</td>\n",
       "      <td>8.536</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127819</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HW</td>\n",
       "      <td>ACQ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.866</td>\n",
       "      <td>-15.708</td>\n",
       "      <td>8.913</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127820</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HW</td>\n",
       "      <td>ACQ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.539</td>\n",
       "      <td>-17.188</td>\n",
       "      <td>8.753</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1032 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0       1    2    3    4       x       y      z  modello\n",
       "3126789  MODEL  3000.0  NaN  NaN  NaN     NaN     NaN    NaN      NaN\n",
       "3126790   ATOM     1.0   NA  SLT  1.0  12.330 -18.789  0.000      NaN\n",
       "3126791   ATOM     1.0   NA  SLT  1.0  17.988 -18.789  0.000      NaN\n",
       "3126792   ATOM     1.0   NA  SLT  1.0   9.501 -15.960  0.000      NaN\n",
       "3126793   ATOM     1.0   NA  SLT  1.0  15.159 -15.960  0.000      NaN\n",
       "...        ...     ...  ...  ...  ...     ...     ...    ...      ...\n",
       "3127816   ATOM     1.0   HW  ACQ  1.0   1.914   1.407 -7.618      NaN\n",
       "3127817   ATOM     1.0   HW  ACQ  1.0   2.660   2.621 -8.415      NaN\n",
       "3127818   ATOM     1.0   OW  ACQ  1.0  14.640 -16.217  8.536      NaN\n",
       "3127819   ATOM     1.0   HW  ACQ  1.0  13.866 -15.708  8.913      NaN\n",
       "3127820   ATOM     1.0   HW  ACQ  1.0  14.539 -17.188  8.753      NaN\n",
       "\n",
       "[1032 rows x 9 columns]"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe[posizione_model[-1] : -2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(posizione_model) - 1):\n",
    "    dataframe.loc[posizione_model[i] + 1 : posizione_model[i+1] - 2, 'modello'] = i+1\n",
    "\n",
    "# assegna il valore numero_di_frame da posizione_model[-1] a indice -2\n",
    "dataframe.loc[posizione_model[-1] +1 : dataframe.index[-2], 'modello'] = float(numero_di_frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>modello</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3127818</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OW</td>\n",
       "      <td>ACQ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.640</td>\n",
       "      <td>-16.217</td>\n",
       "      <td>8.536</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127819</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HW</td>\n",
       "      <td>ACQ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.866</td>\n",
       "      <td>-15.708</td>\n",
       "      <td>8.913</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127820</th>\n",
       "      <td>ATOM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HW</td>\n",
       "      <td>ACQ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.539</td>\n",
       "      <td>-17.188</td>\n",
       "      <td>8.753</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127821</th>\n",
       "      <td>ENDMDL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127822</th>\n",
       "      <td>END</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0    1    2    3    4       x       y      z  modello\n",
       "3127818    ATOM  1.0   OW  ACQ  1.0  14.640 -16.217  8.536   3000.0\n",
       "3127819    ATOM  1.0   HW  ACQ  1.0  13.866 -15.708  8.913   3000.0\n",
       "3127820    ATOM  1.0   HW  ACQ  1.0  14.539 -17.188  8.753   3000.0\n",
       "3127821  ENDMDL  NaN  NaN  NaN  NaN     NaN     NaN    NaN   3000.0\n",
       "3127822     END  NaN  NaN  NaN  NaN     NaN     NaN    NaN      NaN"
      ]
     },
     "execution_count": 516,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>atom</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>frame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NA</td>\n",
       "      <td>12.330</td>\n",
       "      <td>-18.789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NA</td>\n",
       "      <td>17.988</td>\n",
       "      <td>-18.789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NA</td>\n",
       "      <td>9.501</td>\n",
       "      <td>-15.960</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NA</td>\n",
       "      <td>15.159</td>\n",
       "      <td>-15.960</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NA</td>\n",
       "      <td>12.330</td>\n",
       "      <td>-13.131</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  atom       x       y    z  frame\n",
       "0   NA  12.330 -18.789  0.0    1.0\n",
       "1   NA  17.988 -18.789  0.0    1.0\n",
       "2   NA   9.501 -15.960  0.0    1.0\n",
       "3   NA  15.159 -15.960  0.0    1.0\n",
       "4   NA  12.330 -13.131  0.0    1.0"
      ]
     },
     "execution_count": 517,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_indices = [posizione_model[i]+1 for i in range(len(posizione_model)-1)] + [posizione_model[-1] + 1]\n",
    "end_indices = [posizione_model[i+1]-2 for i in range(len(posizione_model)-1)] + [dataframe.index[-3]]\n",
    "filtered_indices = [idx for start, end in zip(start_indices, end_indices) for idx in range(start, end+1)]\n",
    "dataframe = dataframe.loc[filtered_indices]\n",
    "dataframe = dataframe[['2', 'x', 'y', 'z', 'modello']]\n",
    "# rename columns as 'atom', 'x', 'y', 'z', 'frame'\n",
    "dataframe.columns = ['atom', 'x', 'y', 'z', 'frame']\n",
    "# reset index\n",
    "dataframe.reset_index(drop=True, inplace=True)\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hydrogen "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Far Distance (0 - 14 A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>atom</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>frame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>HW</td>\n",
       "      <td>-12.253</td>\n",
       "      <td>17.270</td>\n",
       "      <td>-8.552</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>HW</td>\n",
       "      <td>-13.136</td>\n",
       "      <td>16.061</td>\n",
       "      <td>-7.900</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>HW</td>\n",
       "      <td>13.876</td>\n",
       "      <td>-13.601</td>\n",
       "      <td>8.221</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>HW</td>\n",
       "      <td>12.495</td>\n",
       "      <td>-14.466</td>\n",
       "      <td>8.319</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>HW</td>\n",
       "      <td>1.492</td>\n",
       "      <td>-1.545</td>\n",
       "      <td>-8.840</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    atom       x       y      z  frame\n",
       "981   HW -12.253  17.270 -8.552    1.0\n",
       "982   HW -13.136  16.061 -7.900    1.0\n",
       "984   HW  13.876 -13.601  8.221    1.0\n",
       "985   HW  12.495 -14.466  8.319    1.0\n",
       "987   HW   1.492  -1.545 -8.840    1.0"
      ]
     },
     "execution_count": 518,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mantain only rows with atom == 'HW'\n",
    "dataframe_h = dataframe[dataframe['atom'] == 'HW']\n",
    "dataframe_h.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atom</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>frame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3121814</th>\n",
       "      <td>HW</td>\n",
       "      <td>0.713</td>\n",
       "      <td>4.024</td>\n",
       "      <td>-9.046</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3121816</th>\n",
       "      <td>HW</td>\n",
       "      <td>1.914</td>\n",
       "      <td>1.407</td>\n",
       "      <td>-7.618</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3121817</th>\n",
       "      <td>HW</td>\n",
       "      <td>2.660</td>\n",
       "      <td>2.621</td>\n",
       "      <td>-8.415</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3121819</th>\n",
       "      <td>HW</td>\n",
       "      <td>13.866</td>\n",
       "      <td>-15.708</td>\n",
       "      <td>8.913</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3121820</th>\n",
       "      <td>HW</td>\n",
       "      <td>14.539</td>\n",
       "      <td>-17.188</td>\n",
       "      <td>8.753</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        atom       x       y      z   frame\n",
       "3121814   HW   0.713   4.024 -9.046  3000.0\n",
       "3121816   HW   1.914   1.407 -7.618  3000.0\n",
       "3121817   HW   2.660   2.621 -8.415  3000.0\n",
       "3121819   HW  13.866 -15.708  8.913  3000.0\n",
       "3121820   HW  14.539 -17.188  8.753  3000.0"
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_h.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HW    121214\n",
       "Name: atom, dtype: int64"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_h[\"atom\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting in positive and negative values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe_positive = dataframe_h.loc[dataframe['z'] > 0]\n",
    "dataframe_negative = dataframe_h.loc[dataframe['z'] < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Effettua calcoli:\n",
    "1. Trova l'indice di distanza da assegnare a ogni idrogeno\n",
    "2. elimina gli idrogeni più distanti di 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\giorg\\AppData\\Local\\Temp\\ipykernel_6016\\658969078.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe_positive['d'] = dataframe_positive['z'].apply(calculate_d)\n",
      "C:\\Users\\giorg\\AppData\\Local\\Temp\\ipykernel_6016\\658969078.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe_negative['d'] = dataframe_negative['z'].apply(calculate_d)\n"
     ]
    }
   ],
   "source": [
    "def calculate_d(z):\n",
    "    d_values = np.arange(7.625, 14.125, 0.125)\n",
    "    for d in d_values:\n",
    "        if abs(z) <= d:\n",
    "            return d\n",
    "    return d_values[-1]\n",
    "\n",
    "# Aggiunta della colonna \"d\" nei dataframe\n",
    "dataframe_positive['d'] = dataframe_positive['z'].apply(calculate_d)\n",
    "dataframe_negative['d'] = dataframe_negative['z'].apply(calculate_d)\n",
    "dataframe_positive = dataframe_positive.loc[abs(dataframe_positive['z']) <= 14]\n",
    "dataframe_negative = dataframe_negative.loc[abs(dataframe_negative['z']) <= 14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atom</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>frame</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>291074</th>\n",
       "      <td>HW</td>\n",
       "      <td>-15.020</td>\n",
       "      <td>19.886</td>\n",
       "      <td>13.876</td>\n",
       "      <td>282.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2499777</th>\n",
       "      <td>HW</td>\n",
       "      <td>15.135</td>\n",
       "      <td>8.993</td>\n",
       "      <td>13.922</td>\n",
       "      <td>2398.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        atom       x       y       z   frame     d\n",
       "291074    HW -15.020  19.886  13.876   282.0  14.0\n",
       "2499777   HW  15.135   8.993  13.922  2398.0  14.0"
      ]
     },
     "execution_count": 523,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_positive[dataframe_positive['d'] == 14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Raggruppamento per frame e d, calcolo del conteggio\n",
    "grouped_positive = dataframe_positive.groupby(['frame', 'd']).size().reset_index(name='count')\n",
    "grouped_negative = dataframe_negative.groupby(['frame', 'd']).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>32222</th>\n",
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       "    <tr>\n",
       "      <th>32223</th>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>32224</th>\n",
       "      <td>3000.0</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>32225</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>32226</th>\n",
       "      <td>3000.0</td>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "        frame      d  count\n",
       "32222  3000.0  8.875      1\n",
       "32223  3000.0  9.000      3\n",
       "32224  3000.0  9.125      3\n",
       "32225  3000.0  9.250      2\n",
       "32226  3000.0  9.500      1"
      ]
     },
     "execution_count": 525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_negative.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frame      d  count\n",
       "0    1.0  7.750      1\n",
       "1    1.0  7.875      1\n",
       "2    1.0  8.000      1\n",
       "3    1.0  8.125      1\n",
       "4    1.0  8.250      1"
      ]
     },
     "execution_count": 526,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_positive.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creazione del dataframe di riferimento con tutte le combinazioni di frame e d\n",
    "frames1 = np.arange(1.0, 3001.0, 1.0)\n",
    "d_values1 = np.arange(7.625, 14.125, 0.125)\n",
    "df_reference = pd.DataFrame([(frame, d) for frame in frames1 for d in d_values1], columns=['frame', 'd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_negative_fill = pd.merge(grouped_negative, df_reference, how='right', on=['frame', 'd'])\n",
    "grouped_positive_fill = pd.merge(grouped_positive, df_reference, how='right', on=['frame', 'd'])\n",
    "grouped_negative_fill['count'].fillna(0, inplace=True)\n",
    "grouped_positive_fill['count'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
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      ],
      "text/plain": [
       "   frame      d  count\n",
       "0    1.0  7.625    0.0\n",
       "1    1.0  7.750    1.0\n",
       "2    1.0  7.875    1.0\n",
       "3    1.0  8.000    1.0\n",
       "4    1.0  8.125    1.0"
      ]
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     "metadata": {},
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   ],
   "source": [
    "grouped_positive_fill.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.407712</th>\n",
       "      <td>0.025667</td>\n",
       "      <td>0.022333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.532712</th>\n",
       "      <td>0.037000</td>\n",
       "      <td>0.018667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.657712</th>\n",
       "      <td>0.034667</td>\n",
       "      <td>0.019667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.782712</th>\n",
       "      <td>0.025333</td>\n",
       "      <td>0.013000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.907712</th>\n",
       "      <td>0.020667</td>\n",
       "      <td>0.010667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.032712</th>\n",
       "      <td>0.017667</td>\n",
       "      <td>0.008000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.157712</th>\n",
       "      <td>0.010333</td>\n",
       "      <td>0.006333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.282712</th>\n",
       "      <td>0.006000</td>\n",
       "      <td>0.006000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.407712</th>\n",
       "      <td>0.004667</td>\n",
       "      <td>0.001667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.532712</th>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.002333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.657712</th>\n",
       "      <td>0.002333</td>\n",
       "      <td>0.000333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.782712</th>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.000667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.907712</th>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.000667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.032712</th>\n",
       "      <td>0.001667</td>\n",
       "      <td>0.000667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.157712</th>\n",
       "      <td>0.001333</td>\n",
       "      <td>0.000667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.282712</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.407712</th>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.532712</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.657712</th>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.782712</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.907712</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.032712</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.157712</th>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.000333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Average N of H (positive z)  Average N of H (negative z)\n",
       "1.782712                     0.605667                     0.565667\n",
       "1.907712                     0.766000                     0.683333\n",
       "2.032712                     1.113000                     1.006333\n",
       "2.157712                     1.382667                     1.143667\n",
       "2.282712                     1.416667                     1.197667\n",
       "2.407712                     1.346000                     1.145667\n",
       "2.532712                     1.269000                     1.096333\n",
       "2.657712                     1.321667                     1.081333\n",
       "2.782712                     1.400667                     1.171333\n",
       "2.907712                     1.528000                     1.247333\n",
       "3.032712                     1.635000                     1.355333\n",
       "3.157712                     1.702667                     1.396333\n",
       "3.282712                     1.658333                     1.371000\n",
       "3.407712                     1.403000                     1.231000\n",
       "3.532712                     1.072667                     0.944667\n",
       "3.657712                     0.694667                     0.630000\n",
       "3.782712                     0.414667                     0.344000\n",
       "3.907712                     0.237000                     0.186333\n",
       "4.032712                     0.157333                     0.098333\n",
       "4.157712                     0.107667                     0.077667\n",
       "4.282712                     0.094667                     0.048667\n",
       "4.407712                     0.078000                     0.050667\n",
       "4.532712                     0.071667                     0.046667\n",
       "4.657712                     0.057000                     0.036667\n",
       "4.782712                     0.056667                     0.033667\n",
       "4.907712                     0.053333                     0.032333\n",
       "5.032712                     0.044000                     0.030000\n",
       "5.157712                     0.047667                     0.027333\n",
       "5.282712                     0.040333                     0.028667\n",
       "5.407712                     0.025667                     0.022333\n",
       "5.532712                     0.037000                     0.018667\n",
       "5.657712                     0.034667                     0.019667\n",
       "5.782712                     0.025333                     0.013000\n",
       "5.907712                     0.020667                     0.010667\n",
       "6.032712                     0.017667                     0.008000\n",
       "6.157712                     0.010333                     0.006333\n",
       "6.282712                     0.006000                     0.006000\n",
       "6.407712                     0.004667                     0.001667\n",
       "6.532712                     0.003333                     0.002333\n",
       "6.657712                     0.002333                     0.000333\n",
       "6.782712                     0.003000                     0.000667\n",
       "6.907712                     0.002000                     0.000667\n",
       "7.032712                     0.001667                     0.000667\n",
       "7.157712                     0.001333                     0.000667\n",
       "7.282712                     0.001000                     0.000333\n",
       "7.407712                     0.000667                     0.000000\n",
       "7.532712                     0.000000                     0.000333\n",
       "7.657712                     0.000333                     0.000000\n",
       "7.782712                     0.000000                     0.000000\n",
       "7.907712                     0.000000                     0.000000\n",
       "8.032712                     0.000000                     0.000000\n",
       "8.157712                     0.000667                     0.000333"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_negative = pd.merge(grouped_positive_fill, grouped_negative_fill, how='inner', on=['frame', 'd'])\n",
    "positive_negative_grouped = positive_negative.groupby('d')[['count_x', 'count_y']].mean()\n",
    "positive_negative_grouped\n",
    "\n",
    "# Rinomina delle colonne del nuovo dataframe\n",
    "positive_negative_grouped.columns = ['Average N of H (positive z)', 'Average N of H (negative z)']\n",
    "\n",
    "# Creazione degli indici come valori da 7.625 a 14\n",
    "d_values = np.arange(7.625, 14.125, 0.125)\n",
    "# subtract 1.78271213799185 to every value of d_values\n",
    "d_values -= 5.84228786200815\n",
    "positive_negative_grouped.index = d_values\n",
    "positive_negative_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_positive_density(positive_density):\n",
    "    positive_density /= 2\n",
    "    positive_density *= 18\n",
    "    positive_density /= 0.196079405\n",
    "    positive_density /= 6.02214e23\n",
    "    positive_density *= 1e21\n",
    "    return positive_density\n",
    "\n",
    "positive_negative_grouped['Average Density (positive z)'] = positive_negative_grouped['Average N of H (positive z)'].apply(calculate_positive_density)\n",
    "positive_negative_grouped['Average Density (negative z)'] = positive_negative_grouped['Average N of H (negative z)'].apply(calculate_positive_density)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average N of H (positive z)</th>\n",
       "      <th>Average N of H (negative z)</th>\n",
       "      <th>Average Density (positive z)</th>\n",
       "      <th>Average Density (negative z)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.782712</th>\n",
       "      <td>0.605667</td>\n",
       "      <td>0.565667</td>\n",
       "      <td>0.046163</td>\n",
       "      <td>0.043114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.907712</th>\n",
       "      <td>0.766000</td>\n",
       "      <td>0.683333</td>\n",
       "      <td>0.058383</td>\n",
       "      <td>0.052083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.032712</th>\n",
       "      <td>1.113000</td>\n",
       "      <td>1.006333</td>\n",
       "      <td>0.084831</td>\n",
       "      <td>0.076701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.157712</th>\n",
       "      <td>1.382667</td>\n",
       "      <td>1.143667</td>\n",
       "      <td>0.105385</td>\n",
       "      <td>0.087168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.282712</th>\n",
       "      <td>1.416667</td>\n",
       "      <td>1.197667</td>\n",
       "      <td>0.107976</td>\n",
       "      <td>0.091284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.407712</th>\n",
       "      <td>1.346000</td>\n",
       "      <td>1.145667</td>\n",
       "      <td>0.102590</td>\n",
       "      <td>0.087321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.532712</th>\n",
       "      <td>1.269000</td>\n",
       "      <td>1.096333</td>\n",
       "      <td>0.096721</td>\n",
       "      <td>0.083561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.657712</th>\n",
       "      <td>1.321667</td>\n",
       "      <td>1.081333</td>\n",
       "      <td>0.100735</td>\n",
       "      <td>0.082417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.782712</th>\n",
       "      <td>1.400667</td>\n",
       "      <td>1.171333</td>\n",
       "      <td>0.106757</td>\n",
       "      <td>0.089277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.907712</th>\n",
       "      <td>1.528000</td>\n",
       "      <td>1.247333</td>\n",
       "      <td>0.116462</td>\n",
       "      <td>0.095070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.032712</th>\n",
       "      <td>1.635000</td>\n",
       "      <td>1.355333</td>\n",
       "      <td>0.124617</td>\n",
       "      <td>0.103301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.157712</th>\n",
       "      <td>1.702667</td>\n",
       "      <td>1.396333</td>\n",
       "      <td>0.129774</td>\n",
       "      <td>0.106426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.282712</th>\n",
       "      <td>1.658333</td>\n",
       "      <td>1.371000</td>\n",
       "      <td>0.126395</td>\n",
       "      <td>0.104495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.407712</th>\n",
       "      <td>1.403000</td>\n",
       "      <td>1.231000</td>\n",
       "      <td>0.106934</td>\n",
       "      <td>0.093825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.532712</th>\n",
       "      <td>1.072667</td>\n",
       "      <td>0.944667</td>\n",
       "      <td>0.081757</td>\n",
       "      <td>0.072001</td>\n",
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       "    <tr>\n",
       "      <th>3.657712</th>\n",
       "      <td>0.694667</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.052946</td>\n",
       "      <td>0.048018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.782712</th>\n",
       "      <td>0.414667</td>\n",
       "      <td>0.344000</td>\n",
       "      <td>0.031605</td>\n",
       "      <td>0.026219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.907712</th>\n",
       "      <td>0.237000</td>\n",
       "      <td>0.186333</td>\n",
       "      <td>0.018064</td>\n",
       "      <td>0.014202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.032712</th>\n",
       "      <td>0.157333</td>\n",
       "      <td>0.098333</td>\n",
       "      <td>0.011992</td>\n",
       "      <td>0.007495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.157712</th>\n",
       "      <td>0.107667</td>\n",
       "      <td>0.077667</td>\n",
       "      <td>0.008206</td>\n",
       "      <td>0.005920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.282712</th>\n",
       "      <td>0.094667</td>\n",
       "      <td>0.048667</td>\n",
       "      <td>0.007215</td>\n",
       "      <td>0.003709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.407712</th>\n",
       "      <td>0.078000</td>\n",
       "      <td>0.050667</td>\n",
       "      <td>0.005945</td>\n",
       "      <td>0.003862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.532712</th>\n",
       "      <td>0.071667</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>0.005462</td>\n",
       "      <td>0.003557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.657712</th>\n",
       "      <td>0.057000</td>\n",
       "      <td>0.036667</td>\n",
       "      <td>0.004344</td>\n",
       "      <td>0.002795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.782712</th>\n",
       "      <td>0.056667</td>\n",
       "      <td>0.033667</td>\n",
       "      <td>0.004319</td>\n",
       "      <td>0.002566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.907712</th>\n",
       "      <td>0.053333</td>\n",
       "      <td>0.032333</td>\n",
       "      <td>0.004065</td>\n",
       "      <td>0.002464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.032712</th>\n",
       "      <td>0.044000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.003354</td>\n",
       "      <td>0.002287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.157712</th>\n",
       "      <td>0.047667</td>\n",
       "      <td>0.027333</td>\n",
       "      <td>0.003633</td>\n",
       "      <td>0.002083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.282712</th>\n",
       "      <td>0.040333</td>\n",
       "      <td>0.028667</td>\n",
       "      <td>0.003074</td>\n",
       "      <td>0.002185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.407712</th>\n",
       "      <td>0.025667</td>\n",
       "      <td>0.022333</td>\n",
       "      <td>0.001956</td>\n",
       "      <td>0.001702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.532712</th>\n",
       "      <td>0.037000</td>\n",
       "      <td>0.018667</td>\n",
       "      <td>0.002820</td>\n",
       "      <td>0.001423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.657712</th>\n",
       "      <td>0.034667</td>\n",
       "      <td>0.019667</td>\n",
       "      <td>0.002642</td>\n",
       "      <td>0.001499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.782712</th>\n",
       "      <td>0.025333</td>\n",
       "      <td>0.013000</td>\n",
       "      <td>0.001931</td>\n",
       "      <td>0.000991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.907712</th>\n",
       "      <td>0.020667</td>\n",
       "      <td>0.010667</td>\n",
       "      <td>0.001575</td>\n",
       "      <td>0.000813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.032712</th>\n",
       "      <td>0.017667</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.001347</td>\n",
       "      <td>0.000610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.157712</th>\n",
       "      <td>0.010333</td>\n",
       "      <td>0.006333</td>\n",
       "      <td>0.000788</td>\n",
       "      <td>0.000483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.282712</th>\n",
       "      <td>0.006000</td>\n",
       "      <td>0.006000</td>\n",
       "      <td>0.000457</td>\n",
       "      <td>0.000457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.407712</th>\n",
       "      <td>0.004667</td>\n",
       "      <td>0.001667</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>0.000127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.532712</th>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.002333</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>0.000178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.657712</th>\n",
       "      <td>0.002333</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.782712</th>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.907712</th>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.032712</th>\n",
       "      <td>0.001667</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.157712</th>\n",
       "      <td>0.001333</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.282712</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.407712</th>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.532712</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.657712</th>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.782712</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.907712</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.032712</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.157712</th>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Average N of H (positive z)  Average N of H (negative z)  \\\n",
       "1.782712                     0.605667                     0.565667   \n",
       "1.907712                     0.766000                     0.683333   \n",
       "2.032712                     1.113000                     1.006333   \n",
       "2.157712                     1.382667                     1.143667   \n",
       "2.282712                     1.416667                     1.197667   \n",
       "2.407712                     1.346000                     1.145667   \n",
       "2.532712                     1.269000                     1.096333   \n",
       "2.657712                     1.321667                     1.081333   \n",
       "2.782712                     1.400667                     1.171333   \n",
       "2.907712                     1.528000                     1.247333   \n",
       "3.032712                     1.635000                     1.355333   \n",
       "3.157712                     1.702667                     1.396333   \n",
       "3.282712                     1.658333                     1.371000   \n",
       "3.407712                     1.403000                     1.231000   \n",
       "3.532712                     1.072667                     0.944667   \n",
       "3.657712                     0.694667                     0.630000   \n",
       "3.782712                     0.414667                     0.344000   \n",
       "3.907712                     0.237000                     0.186333   \n",
       "4.032712                     0.157333                     0.098333   \n",
       "4.157712                     0.107667                     0.077667   \n",
       "4.282712                     0.094667                     0.048667   \n",
       "4.407712                     0.078000                     0.050667   \n",
       "4.532712                     0.071667                     0.046667   \n",
       "4.657712                     0.057000                     0.036667   \n",
       "4.782712                     0.056667                     0.033667   \n",
       "4.907712                     0.053333                     0.032333   \n",
       "5.032712                     0.044000                     0.030000   \n",
       "5.157712                     0.047667                     0.027333   \n",
       "5.282712                     0.040333                     0.028667   \n",
       "5.407712                     0.025667                     0.022333   \n",
       "5.532712                     0.037000                     0.018667   \n",
       "5.657712                     0.034667                     0.019667   \n",
       "5.782712                     0.025333                     0.013000   \n",
       "5.907712                     0.020667                     0.010667   \n",
       "6.032712                     0.017667                     0.008000   \n",
       "6.157712                     0.010333                     0.006333   \n",
       "6.282712                     0.006000                     0.006000   \n",
       "6.407712                     0.004667                     0.001667   \n",
       "6.532712                     0.003333                     0.002333   \n",
       "6.657712                     0.002333                     0.000333   \n",
       "6.782712                     0.003000                     0.000667   \n",
       "6.907712                     0.002000                     0.000667   \n",
       "7.032712                     0.001667                     0.000667   \n",
       "7.157712                     0.001333                     0.000667   \n",
       "7.282712                     0.001000                     0.000333   \n",
       "7.407712                     0.000667                     0.000000   \n",
       "7.532712                     0.000000                     0.000333   \n",
       "7.657712                     0.000333                     0.000000   \n",
       "7.782712                     0.000000                     0.000000   \n",
       "7.907712                     0.000000                     0.000000   \n",
       "8.032712                     0.000000                     0.000000   \n",
       "8.157712                     0.000667                     0.000333   \n",
       "\n",
       "          Average Density (positive z)  Average Density (negative z)  \n",
       "1.782712                      0.046163                      0.043114  \n",
       "1.907712                      0.058383                      0.052083  \n",
       "2.032712                      0.084831                      0.076701  \n",
       "2.157712                      0.105385                      0.087168  \n",
       "2.282712                      0.107976                      0.091284  \n",
       "2.407712                      0.102590                      0.087321  \n",
       "2.532712                      0.096721                      0.083561  \n",
       "2.657712                      0.100735                      0.082417  \n",
       "2.782712                      0.106757                      0.089277  \n",
       "2.907712                      0.116462                      0.095070  \n",
       "3.032712                      0.124617                      0.103301  \n",
       "3.157712                      0.129774                      0.106426  \n",
       "3.282712                      0.126395                      0.104495  \n",
       "3.407712                      0.106934                      0.093825  \n",
       "3.532712                      0.081757                      0.072001  \n",
       "3.657712                      0.052946                      0.048018  \n",
       "3.782712                      0.031605                      0.026219  \n",
       "3.907712                      0.018064                      0.014202  \n",
       "4.032712                      0.011992                      0.007495  \n",
       "4.157712                      0.008206                      0.005920  \n",
       "4.282712                      0.007215                      0.003709  \n",
       "4.407712                      0.005945                      0.003862  \n",
       "4.532712                      0.005462                      0.003557  \n",
       "4.657712                      0.004344                      0.002795  \n",
       "4.782712                      0.004319                      0.002566  \n",
       "4.907712                      0.004065                      0.002464  \n",
       "5.032712                      0.003354                      0.002287  \n",
       "5.157712                      0.003633                      0.002083  \n",
       "5.282712                      0.003074                      0.002185  \n",
       "5.407712                      0.001956                      0.001702  \n",
       "5.532712                      0.002820                      0.001423  \n",
       "5.657712                      0.002642                      0.001499  \n",
       "5.782712                      0.001931                      0.000991  \n",
       "5.907712                      0.001575                      0.000813  \n",
       "6.032712                      0.001347                      0.000610  \n",
       "6.157712                      0.000788                      0.000483  \n",
       "6.282712                      0.000457                      0.000457  \n",
       "6.407712                      0.000356                      0.000127  \n",
       "6.532712                      0.000254                      0.000178  \n",
       "6.657712                      0.000178                      0.000025  \n",
       "6.782712                      0.000229                      0.000051  \n",
       "6.907712                      0.000152                      0.000051  \n",
       "7.032712                      0.000127                      0.000051  \n",
       "7.157712                      0.000102                      0.000051  \n",
       "7.282712                      0.000076                      0.000025  \n",
       "7.407712                      0.000051                      0.000000  \n",
       "7.532712                      0.000000                      0.000025  \n",
       "7.657712                      0.000025                      0.000000  \n",
       "7.782712                      0.000000                      0.000000  \n",
       "7.907712                      0.000000                      0.000000  \n",
       "8.032712                      0.000000                      0.000000  \n",
       "8.157712                      0.000051                      0.000025  "
      ]
     },
     "execution_count": 532,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_negative_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {},
   "outputs": [],
   "source": [
    "# salva il dataset come file .csv\n",
    "positive_negative_grouped.to_csv(files_pdb[x].rstrip(\".pdb\") + \".csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Near Distance (< 2 A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>atom</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>frame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>HW</td>\n",
       "      <td>-12.253</td>\n",
       "      <td>17.270</td>\n",
       "      <td>-8.552</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>HW</td>\n",
       "      <td>-13.136</td>\n",
       "      <td>16.061</td>\n",
       "      <td>-7.900</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>HW</td>\n",
       "      <td>13.876</td>\n",
       "      <td>-13.601</td>\n",
       "      <td>8.221</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>HW</td>\n",
       "      <td>12.495</td>\n",
       "      <td>-14.466</td>\n",
       "      <td>8.319</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>HW</td>\n",
       "      <td>1.492</td>\n",
       "      <td>-1.545</td>\n",
       "      <td>-8.840</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    atom       x       y      z  frame\n",
       "981   HW -12.253  17.270 -8.552    1.0\n",
       "982   HW -13.136  16.061 -7.900    1.0\n",
       "984   HW  13.876 -13.601  8.221    1.0\n",
       "985   HW  12.495 -14.466  8.319    1.0\n",
       "987   HW   1.492  -1.545 -8.840    1.0"
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mantain only rows with atom == 'HW'\n",
    "dataframe_h = dataframe[dataframe['atom'] == 'HW']\n",
    "dataframe_h.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "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>atom</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>frame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3121814</th>\n",
       "      <td>HW</td>\n",
       "      <td>0.713</td>\n",
       "      <td>4.024</td>\n",
       "      <td>-9.046</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3121816</th>\n",
       "      <td>HW</td>\n",
       "      <td>1.914</td>\n",
       "      <td>1.407</td>\n",
       "      <td>-7.618</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3121817</th>\n",
       "      <td>HW</td>\n",
       "      <td>2.660</td>\n",
       "      <td>2.621</td>\n",
       "      <td>-8.415</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3121819</th>\n",
       "      <td>HW</td>\n",
       "      <td>13.866</td>\n",
       "      <td>-15.708</td>\n",
       "      <td>8.913</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3121820</th>\n",
       "      <td>HW</td>\n",
       "      <td>14.539</td>\n",
       "      <td>-17.188</td>\n",
       "      <td>8.753</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        atom       x       y      z   frame\n",
       "3121814   HW   0.713   4.024 -9.046  3000.0\n",
       "3121816   HW   1.914   1.407 -7.618  3000.0\n",
       "3121817   HW   2.660   2.621 -8.415  3000.0\n",
       "3121819   HW  13.866 -15.708  8.913  3000.0\n",
       "3121820   HW  14.539 -17.188  8.753  3000.0"
      ]
     },
     "execution_count": 500,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_h.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HW    121214\n",
       "Name: atom, dtype: int64"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_h[\"atom\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting in positive and negative values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe_positive = dataframe_h.loc[dataframe['z'] > 0]\n",
    "dataframe_negative = dataframe_h.loc[dataframe['z'] < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Effettua calcoli:\n",
    "1. Trova l'indice di distanza da assegnare a ogni idrogeno\n",
    "2. elimina gli idrogeni più distanti di 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\giorg\\AppData\\Local\\Temp\\ipykernel_6016\\571235199.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe_positive['d'] = dataframe_positive['z'].apply(calculate_d)\n",
      "C:\\Users\\giorg\\AppData\\Local\\Temp\\ipykernel_6016\\571235199.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe_negative['d'] = dataframe_negative['z'].apply(calculate_d)\n"
     ]
    }
   ],
   "source": [
    "def calculate_d(z):\n",
    "    d_values = np.arange(5.842, 14.125, 0.125)\n",
    "    for d in d_values:\n",
    "        if abs(z) <= d:\n",
    "            return d\n",
    "    return d_values[-1]\n",
    "\n",
    "# Aggiunta della colonna \"d\" nei dataframe\n",
    "dataframe_positive['d'] = dataframe_positive['z'].apply(calculate_d)\n",
    "dataframe_negative['d'] = dataframe_negative['z'].apply(calculate_d)\n",
    "dataframe_positive = dataframe_positive.loc[abs(dataframe_positive['z']) <= 14.]\n",
    "dataframe_negative = dataframe_negative.loc[abs(dataframe_negative['z']) <= 14.]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.967     75804\n",
       "9.092     72818\n",
       "8.842     72449\n",
       "9.217     62931\n",
       "8.717     62383\n",
       "8.592     51092\n",
       "9.342     50351\n",
       "8.467     43516\n",
       "8.092     42891\n",
       "11.092    42877\n",
       "11.217    42849\n",
       "8.217     42802\n",
       "10.967    42277\n",
       "11.342    42066\n",
       "11.467    41389\n",
       "8.342     41109\n",
       "10.842    41082\n",
       "11.592    40948\n",
       "7.967     40615\n",
       "11.717    39992\n",
       "11.842    39495\n",
       "12.092    39341\n",
       "11.967    39226\n",
       "12.342    39032\n",
       "10.717    39014\n",
       "9.467     38872\n",
       "12.217    38849\n",
       "12.467    38723\n",
       "12.717    38696\n",
       "12.592    38306\n",
       "12.967    37869\n",
       "12.842    37858\n",
       "13.092    37384\n",
       "13.217    37297\n",
       "13.342    36833\n",
       "10.592    36709\n",
       "13.467    36192\n",
       "13.592    35029\n",
       "13.717    34703\n",
       "10.467    33376\n",
       "7.842     33133\n",
       "13.842    33108\n",
       "13.967    32087\n",
       "9.592     30937\n",
       "10.342    30838\n",
       "10.217    27901\n",
       "9.717     26436\n",
       "10.092    25767\n",
       "9.842     24897\n",
       "9.967     24373\n",
       "7.717     23127\n",
       "7.592     12697\n",
       "14.092     8500\n",
       "7.467      4843\n",
       "7.342      1107\n",
       "7.217       151\n",
       "7.092        13\n",
       "6.967         1\n",
       "Name: d, dtype: int64"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count unique values of dataframe_positive['d']\n",
    "dataframe_positive['d'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atom</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>frame</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>HW</td>\n",
       "      <td>14.908</td>\n",
       "      <td>10.602</td>\n",
       "      <td>7.741</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>HW</td>\n",
       "      <td>10.222</td>\n",
       "      <td>-7.668</td>\n",
       "      <td>7.748</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1381</th>\n",
       "      <td>HW</td>\n",
       "      <td>25.226</td>\n",
       "      <td>12.427</td>\n",
       "      <td>7.814</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>HW</td>\n",
       "      <td>-6.787</td>\n",
       "      <td>13.743</td>\n",
       "      <td>7.823</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>HW</td>\n",
       "      <td>-5.269</td>\n",
       "      <td>-6.130</td>\n",
       "      <td>7.732</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10127381</th>\n",
       "      <td>HW</td>\n",
       "      <td>-17.886</td>\n",
       "      <td>16.879</td>\n",
       "      <td>7.786</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>7.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10127798</th>\n",
       "      <td>HW</td>\n",
       "      <td>-7.762</td>\n",
       "      <td>-12.116</td>\n",
       "      <td>7.743</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>7.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10127818</th>\n",
       "      <td>HW</td>\n",
       "      <td>-7.200</td>\n",
       "      <td>-6.170</td>\n",
       "      <td>7.780</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>7.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10127866</th>\n",
       "      <td>HW</td>\n",
       "      <td>-10.644</td>\n",
       "      <td>13.640</td>\n",
       "      <td>7.748</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>7.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10128179</th>\n",
       "      <td>HW</td>\n",
       "      <td>6.674</td>\n",
       "      <td>-9.422</td>\n",
       "      <td>7.724</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>7.842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33133 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         atom       x       y      z   frame      d\n",
       "1072       HW  14.908  10.602  7.741     1.0  7.842\n",
       "1314       HW  10.222  -7.668  7.748     1.0  7.842\n",
       "1381       HW  25.226  12.427  7.814     1.0  7.842\n",
       "1551       HW  -6.787  13.743  7.823     1.0  7.842\n",
       "2020       HW  -5.269  -6.130  7.732     1.0  7.842\n",
       "...       ...     ...     ...    ...     ...    ...\n",
       "10127381   HW -17.886  16.879  7.786  3000.0  7.842\n",
       "10127798   HW  -7.762 -12.116  7.743  3000.0  7.842\n",
       "10127818   HW  -7.200  -6.170  7.780  3000.0  7.842\n",
       "10127866   HW -10.644  13.640  7.748  3000.0  7.842\n",
       "10128179   HW   6.674  -9.422  7.724  3000.0  7.842\n",
       "\n",
       "[33133 rows x 6 columns]"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_positive[dataframe_positive['d'] == 7.842]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Raggruppamento per frame e d, calcolo del conteggio\n",
    "grouped_positive = dataframe_positive.groupby(['frame', 'd']).size().reset_index(name='count')\n",
    "grouped_negative = dataframe_negative.groupby(['frame', 'd']).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>frame</th>\n",
       "      <th>d</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>161706</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>13.592</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>161707</th>\n",
       "      <td>3000.0</td>\n",
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       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>161708</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>13.842</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>161709</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>13.967</td>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>161710</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>14.092</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         frame       d  count\n",
       "161706  3000.0  13.592     12\n",
       "161707  3000.0  13.717      6\n",
       "161708  3000.0  13.842     11\n",
       "161709  3000.0  13.967     13\n",
       "161710  3000.0  14.092      3"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_negative.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.592</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.717</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.842</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.967</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "   frame      d  count\n",
       "0    1.0  7.467      3\n",
       "1    1.0  7.592      1\n",
       "2    1.0  7.717      7\n",
       "3    1.0  7.842      9\n",
       "4    1.0  7.967     11"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_positive.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creazione del dataframe di riferimento con tutte le combinazioni di frame e d\n",
    "frames1 = np.arange(1.0, 3001.0, 1.0)\n",
    "d_values1 = np.arange(5.842, 14.125, 0.125)\n",
    "df_reference = pd.DataFrame([(frame, d) for frame in frames1 for d in d_values1], columns=['frame', 'd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_negative_fill = pd.merge(grouped_negative, df_reference, how='right', on=['frame', 'd'])\n",
    "grouped_positive_fill = pd.merge(grouped_positive, df_reference, how='right', on=['frame', 'd'])\n",
    "grouped_negative_fill['count'].fillna(0, inplace=True)\n",
    "grouped_positive_fill['count'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame</th>\n",
       "      <th>d</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.842</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.967</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.092</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.217</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.342</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frame      d  count\n",
       "0    1.0  5.842    0.0\n",
       "1    1.0  5.967    0.0\n",
       "2    1.0  6.092    0.0\n",
       "3    1.0  6.217    0.0\n",
       "4    1.0  6.342    0.0"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_positive_fill.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average N of H (positive z)</th>\n",
       "      <th>Average N of H (negative z)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.125</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.250</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.375</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.500</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.750</th>\n",
       "      <td>11.676333</td>\n",
       "      <td>8.631667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.875</th>\n",
       "      <td>11.567667</td>\n",
       "      <td>8.126333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.000</th>\n",
       "      <td>11.036000</td>\n",
       "      <td>7.503333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.125</th>\n",
       "      <td>10.695667</td>\n",
       "      <td>7.004333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.250</th>\n",
       "      <td>2.833333</td>\n",
       "      <td>1.822333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Average N of H (positive z)  Average N of H (negative z)\n",
       "0.000                     0.000000                     0.000000\n",
       "0.125                     0.000000                     0.000000\n",
       "0.250                     0.000000                     0.000000\n",
       "0.375                     0.000000                     0.000000\n",
       "0.500                     0.000000                     0.000000\n",
       "...                            ...                          ...\n",
       "7.750                    11.676333                     8.631667\n",
       "7.875                    11.567667                     8.126333\n",
       "8.000                    11.036000                     7.503333\n",
       "8.125                    10.695667                     7.004333\n",
       "8.250                     2.833333                     1.822333\n",
       "\n",
       "[67 rows x 2 columns]"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_negative = pd.merge(grouped_positive_fill, grouped_negative_fill, how='inner', on=['frame', 'd'])\n",
    "positive_negative_grouped = positive_negative.groupby('d')[['count_x', 'count_y']].mean()\n",
    "positive_negative_grouped\n",
    "\n",
    "# Rinomina delle colonne del nuovo dataframe\n",
    "positive_negative_grouped.columns = ['Average N of H (positive z)', 'Average N of H (negative z)']\n",
    "\n",
    "# Creazione degli indici come valori da 7.625 a 14\n",
    "d_values = np.arange(5.842, 14.125, 0.125)\n",
    "# subtract 1.78271213799185 to every value of d_values\n",
    "d_values -= 5.842\n",
    "positive_negative_grouped.index = d_values\n",
    "positive_negative_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_positive_density(positive_density):\n",
    "    positive_density /= 2\n",
    "    positive_density *= 18\n",
    "    positive_density /= 0.196079405\n",
    "    positive_density /= 6.02214e23\n",
    "    positive_density *= 1e21\n",
    "    return positive_density\n",
    "\n",
    "positive_negative_grouped['Average Density (positive z)'] = positive_negative_grouped['Average N of H (positive z)'].apply(calculate_positive_density)\n",
    "positive_negative_grouped['Average Density (negative z)'] = positive_negative_grouped['Average N of H (negative z)'].apply(calculate_positive_density)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>Average N of H (positive z)</th>\n",
       "      <th>Average N of H (negative z)</th>\n",
       "      <th>Average Density (positive z)</th>\n",
       "      <th>Average Density (negative z)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.125</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.250</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.375</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.500</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.750</th>\n",
       "      <td>11.676333</td>\n",
       "      <td>8.631667</td>\n",
       "      <td>0.889951</td>\n",
       "      <td>0.657892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.875</th>\n",
       "      <td>11.567667</td>\n",
       "      <td>8.126333</td>\n",
       "      <td>0.881669</td>\n",
       "      <td>0.619376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.000</th>\n",
       "      <td>11.036000</td>\n",
       "      <td>7.503333</td>\n",
       "      <td>0.841146</td>\n",
       "      <td>0.571892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.125</th>\n",
       "      <td>10.695667</td>\n",
       "      <td>7.004333</td>\n",
       "      <td>0.815206</td>\n",
       "      <td>0.533859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.250</th>\n",
       "      <td>2.833333</td>\n",
       "      <td>1.822333</td>\n",
       "      <td>0.215952</td>\n",
       "      <td>0.138895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Average N of H (positive z)  Average N of H (negative z)  \\\n",
       "0.000                     0.000000                     0.000000   \n",
       "0.125                     0.000000                     0.000000   \n",
       "0.250                     0.000000                     0.000000   \n",
       "0.375                     0.000000                     0.000000   \n",
       "0.500                     0.000000                     0.000000   \n",
       "...                            ...                          ...   \n",
       "7.750                    11.676333                     8.631667   \n",
       "7.875                    11.567667                     8.126333   \n",
       "8.000                    11.036000                     7.503333   \n",
       "8.125                    10.695667                     7.004333   \n",
       "8.250                     2.833333                     1.822333   \n",
       "\n",
       "       Average Density (positive z)  Average Density (negative z)  \n",
       "0.000                      0.000000                      0.000000  \n",
       "0.125                      0.000000                      0.000000  \n",
       "0.250                      0.000000                      0.000000  \n",
       "0.375                      0.000000                      0.000000  \n",
       "0.500                      0.000000                      0.000000  \n",
       "...                             ...                           ...  \n",
       "7.750                      0.889951                      0.657892  \n",
       "7.875                      0.881669                      0.619376  \n",
       "8.000                      0.841146                      0.571892  \n",
       "8.125                      0.815206                      0.533859  \n",
       "8.250                      0.215952                      0.138895  \n",
       "\n",
       "[67 rows x 4 columns]"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_negative_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [],
   "source": [
    "# salva il dataset come file .csv\n",
    "positive_negative_grouped.to_csv(\"./output/HNear_\" + files_pdb[x].rstrip(\".pdb\") + \".csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NaCl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atom</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>frame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>NX</td>\n",
       "      <td>9.591</td>\n",
       "      <td>-18.772</td>\n",
       "      <td>3.133</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>NX</td>\n",
       "      <td>15.141</td>\n",
       "      <td>-18.825</td>\n",
       "      <td>2.897</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>NX</td>\n",
       "      <td>12.359</td>\n",
       "      <td>-16.144</td>\n",
       "      <td>2.942</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>NX</td>\n",
       "      <td>17.950</td>\n",
       "      <td>-15.855</td>\n",
       "      <td>3.026</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>NX</td>\n",
       "      <td>9.638</td>\n",
       "      <td>-13.067</td>\n",
       "      <td>2.663</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    atom       x       y      z  frame\n",
       "196   NX   9.591 -18.772  3.133    1.0\n",
       "197   NX  15.141 -18.825  2.897    1.0\n",
       "198   NX  12.359 -16.144  2.942    1.0\n",
       "199   NX  17.950 -15.855  3.026    1.0\n",
       "200   NX   9.638 -13.067  2.663    1.0"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mantain only rows with atom == 'HW'\n",
    "dataframe_NaCl = dataframe[dataframe['atom'].isin(['CX', 'NX'])]\n",
    "dataframe_NaCl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atom</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>frame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10126261</th>\n",
       "      <td>CX</td>\n",
       "      <td>9.639</td>\n",
       "      <td>15.108</td>\n",
       "      <td>-5.843</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10126262</th>\n",
       "      <td>CX</td>\n",
       "      <td>15.185</td>\n",
       "      <td>15.227</td>\n",
       "      <td>-5.720</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10126263</th>\n",
       "      <td>CX</td>\n",
       "      <td>12.384</td>\n",
       "      <td>18.097</td>\n",
       "      <td>-5.972</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10126264</th>\n",
       "      <td>CX</td>\n",
       "      <td>18.089</td>\n",
       "      <td>17.977</td>\n",
       "      <td>-5.595</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10126265</th>\n",
       "      <td>CX</td>\n",
       "      <td>9.521</td>\n",
       "      <td>9.483</td>\n",
       "      <td>-5.969</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         atom       x       y      z   frame\n",
       "10126261   CX   9.639  15.108 -5.843  3000.0\n",
       "10126262   CX  15.185  15.227 -5.720  3000.0\n",
       "10126263   CX  12.384  18.097 -5.972  3000.0\n",
       "10126264   CX  18.089  17.977 -5.595  3000.0\n",
       "10126265   CX   9.521   9.483 -5.969  3000.0"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_NaCl.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NX    1176000\n",
       "CX    1176000\n",
       "Name: atom, dtype: int64"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_NaCl[\"atom\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting in positive and negative values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe_positive = dataframe_NaCl.loc[dataframe['z'] > 0]\n",
    "dataframe_negative = dataframe_NaCl.loc[dataframe['z'] < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Effettua calcoli:\n",
    "1. Trova l'indice di distanza da assegnare a ogni idrogeno\n",
    "2. elimina gli idrogeni più distanti di 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\giorg\\AppData\\Local\\Temp\\ipykernel_6016\\2096704374.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe_positive['d'] = dataframe_positive['z'].apply(calculate_d)\n",
      "C:\\Users\\giorg\\AppData\\Local\\Temp\\ipykernel_6016\\2096704374.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe_negative['d'] = dataframe_negative['z'].apply(calculate_d)\n"
     ]
    }
   ],
   "source": [
    "def calculate_d(z):\n",
    "    d_values = np.arange(0.125, 14.125, 0.125)\n",
    "    for d in d_values:\n",
    "        if abs(z) <= d:\n",
    "            return d\n",
    "    return d_values[-1]\n",
    "\n",
    "# Aggiunta della colonna \"d\" nei dataframe\n",
    "dataframe_positive['d'] = dataframe_positive['z'].apply(calculate_d)\n",
    "dataframe_negative['d'] = dataframe_negative['z'].apply(calculate_d)\n",
    "dataframe_positive = dataframe_positive.loc[abs(dataframe_positive['z']) <= 14]\n",
    "dataframe_negative = dataframe_negative.loc[abs(dataframe_negative['z']) <= 14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atom</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>frame</th>\n",
       "      <th>d</th>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [atom, x, y, z, frame, d]\n",
       "Index: []"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_positive[dataframe_positive['d'] == 14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th>196</th>\n",
       "      <td>NX</td>\n",
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       "      <td>-18.772</td>\n",
       "      <td>3.133</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.250</td>\n",
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       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>NX</td>\n",
       "      <td>15.141</td>\n",
       "      <td>-18.825</td>\n",
       "      <td>2.897</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>NX</td>\n",
       "      <td>12.359</td>\n",
       "      <td>-16.144</td>\n",
       "      <td>2.942</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>NX</td>\n",
       "      <td>17.950</td>\n",
       "      <td>-15.855</td>\n",
       "      <td>3.026</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>NX</td>\n",
       "      <td>9.638</td>\n",
       "      <td>-13.067</td>\n",
       "      <td>2.663</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    atom       x       y      z  frame      d\n",
       "196   NX   9.591 -18.772  3.133    1.0  3.250\n",
       "197   NX  15.141 -18.825  2.897    1.0  3.000\n",
       "198   NX  12.359 -16.144  2.942    1.0  3.000\n",
       "199   NX  17.950 -15.855  3.026    1.0  3.125\n",
       "200   NX   9.638 -13.067  2.663    1.0  2.750"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_positive.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Raggruppamento per frame e d, calcolo del conteggio\n",
    "grouped_positive_nx = dataframe_positive[dataframe_positive[\"atom\"] == \"NX\"].groupby(['frame', 'd']).size().reset_index(name='count')\n",
    "grouped_positive_cx = dataframe_positive[dataframe_positive[\"atom\"] == \"CX\"].groupby(['frame', 'd']).size().reset_index(name='count')\n",
    "grouped_negative_nx = dataframe_negative[dataframe_negative[\"atom\"] == \"NX\"].groupby(['frame', 'd']).size().reset_index(name='count')\n",
    "grouped_negative_cx = dataframe_negative[dataframe_negative[\"atom\"] == \"CX\"].groupby(['frame', 'd']).size().reset_index(name='count')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame</th>\n",
       "      <th>d</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.750</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.875</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.125</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.250</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frame      d  count\n",
       "0    1.0  2.750      5\n",
       "1    1.0  2.875     25\n",
       "2    1.0  3.000     47\n",
       "3    1.0  3.125     15\n",
       "4    1.0  3.250      5"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_positive_nx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame</th>\n",
       "      <th>d</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.750</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.875</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.125</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.250</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frame      d  count\n",
       "0    1.0  2.750      5\n",
       "1    1.0  2.875     37\n",
       "2    1.0  3.000     35\n",
       "3    1.0  3.125     19\n",
       "4    1.0  3.250      2"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_negative_nx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creazione del dataframe di riferimento con tutte le combinazioni di frame e d\n",
    "frames1 = np.arange(1.0, 3001.0, 1.0)\n",
    "d_values1 = np.arange(.125, 14.125, 0.125)\n",
    "df_reference = pd.DataFrame([(frame, d) for frame in frames1 for d in d_values1], columns=['frame', 'd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.125    3000\n",
       "5.750    3000\n",
       "3.000    3000\n",
       "3.125    3000\n",
       "2.875    3000\n",
       "6.000    3000\n",
       "5.875    3000\n",
       "5.625    3000\n",
       "2.750    2999\n",
       "6.250    2978\n",
       "3.250    2840\n",
       "6.375    2322\n",
       "5.500    1981\n",
       "6.500     978\n",
       "3.375     744\n",
       "2.625     288\n",
       "6.625     261\n",
       "5.375     131\n",
       "6.750      67\n",
       "3.500      47\n",
       "6.875      17\n",
       "3.625       3\n",
       "7.000       2\n",
       "Name: d, dtype: int64"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_positive_nx['d'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_negative_fill_nx = pd.merge(grouped_negative_nx, df_reference, how='right', on=['frame', 'd'])\n",
    "grouped_negative_fill_cx = pd.merge(grouped_negative_cx, df_reference, how='right', on=['frame', 'd'])\n",
    "\n",
    "grouped_positive_fill_nx = pd.merge(grouped_positive_nx, df_reference, how='right', on=['frame', 'd'])\n",
    "grouped_positive_fill_cx = pd.merge(grouped_positive_cx, df_reference, how='right', on=['frame', 'd'])\n",
    "\n",
    "grouped_negative_fill_nx['count'].fillna(0, inplace=True)\n",
    "grouped_positive_fill_nx['count'].fillna(0, inplace=True)\n",
    "\n",
    "grouped_negative_fill_cx['count'].fillna(0, inplace=True)\n",
    "grouped_positive_fill_cx['count'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>frame</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frame      d  count\n",
       "0    1.0  0.125    0.0\n",
       "1    1.0  0.250    0.0\n",
       "2    1.0  0.375    0.0\n",
       "3    1.0  0.500    0.0\n",
       "4    1.0  0.625    0.0"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_negative_fill_nx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_negative_nx = pd.merge(grouped_positive_fill_nx, grouped_negative_fill_nx, how='inner', on=['frame', 'd'])\n",
    "positive_negative_grouped_nx = positive_negative_nx.groupby('d')[['count_x', 'count_y']].mean()\n",
    "positive_negative_grouped_nx\n",
    "\n",
    "# Rinomina delle colonne del nuovo dataframe\n",
    "positive_negative_grouped_nx.columns = ['Average N of Na (positive z)', 'Average N of Na (negative z)']\n",
    "\n",
    "positive_negative_cx = pd.merge(grouped_positive_fill_cx, grouped_negative_fill_cx, how='inner', on=['frame', 'd'])\n",
    "positive_negative_grouped_cx = positive_negative_cx.groupby('d')[['count_x', 'count_y']].mean()\n",
    "positive_negative_grouped_cx\n",
    "\n",
    "# Rinomina delle colonne del nuovo dataframe\n",
    "positive_negative_grouped_cx.columns = ['Average N of Cl (positive z)', 'Average N of Cl (negative z)']\n",
    "\n",
    "# Creazione degli indici come valori da 7.625 a 14\n",
    "# d_values = np.arange(0.125, 14.125, 0.125)\n",
    "# # subtract 1.78271213799185 to every value of d_values\n",
    "# d_values -= 5.84228786200815\n",
    "# positive_negative_grouped.index = d_values\n",
    "# positive_negative_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average N of Cl (positive z)</th>\n",
       "      <th>Average N of Cl (negative z)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.125</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>0.250</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.375</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.500</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>0.625</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.500</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.625</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.750</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.875</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Average N of Cl (positive z)  Average N of Cl (negative z)\n",
       "d                                                                 \n",
       "0.125                            0.0                           0.0\n",
       "0.250                            0.0                           0.0\n",
       "0.375                            0.0                           0.0\n",
       "0.500                            0.0                           0.0\n",
       "0.625                            0.0                           0.0\n",
       "...                              ...                           ...\n",
       "13.500                           0.0                           0.0\n",
       "13.625                           0.0                           0.0\n",
       "13.750                           0.0                           0.0\n",
       "13.875                           0.0                           0.0\n",
       "14.000                           0.0                           0.0\n",
       "\n",
       "[112 rows x 2 columns]"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_negative_grouped_cx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average N of Cl (positive z)</th>\n",
       "      <th>Average N of Cl (negative z)</th>\n",
       "      <th>Average N of Na (positive z)</th>\n",
       "      <th>Average N of Na (negative z)</th>\n",
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       "    <tr>\n",
       "      <th>d</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th>0.125</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>0.375</th>\n",
       "      <td>0.0</td>\n",
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       "      <th>0.500</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>0.625</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>13.500</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.625</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.750</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.875</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Average N of Cl (positive z)  Average N of Cl (negative z)  \\\n",
       "d                                                                    \n",
       "0.125                            0.0                           0.0   \n",
       "0.250                            0.0                           0.0   \n",
       "0.375                            0.0                           0.0   \n",
       "0.500                            0.0                           0.0   \n",
       "0.625                            0.0                           0.0   \n",
       "...                              ...                           ...   \n",
       "13.500                           0.0                           0.0   \n",
       "13.625                           0.0                           0.0   \n",
       "13.750                           0.0                           0.0   \n",
       "13.875                           0.0                           0.0   \n",
       "14.000                           0.0                           0.0   \n",
       "\n",
       "        Average N of Na (positive z)  Average N of Na (negative z)  \n",
       "d                                                                   \n",
       "0.125                            0.0                           0.0  \n",
       "0.250                            0.0                           0.0  \n",
       "0.375                            0.0                           0.0  \n",
       "0.500                            0.0                           0.0  \n",
       "0.625                            0.0                           0.0  \n",
       "...                              ...                           ...  \n",
       "13.500                           0.0                           0.0  \n",
       "13.625                           0.0                           0.0  \n",
       "13.750                           0.0                           0.0  \n",
       "13.875                           0.0                           0.0  \n",
       "14.000                           0.0                           0.0  \n",
       "\n",
       "[112 rows x 4 columns]"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_negative = pd.merge(positive_negative_grouped_cx, positive_negative_grouped_nx, how='inner', on=['d'])\n",
    "positive_negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "# salva il dataset come file .csv\n",
    "positive_negative.to_csv(\"./output/NaCl_\" + files_pdb[x].rstrip(\".pdb\") + \".csv\")"
   ]
  }
 ],
 "metadata": {
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