system_properties/angle_ceramide_ext.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "091d6084",
   "metadata": {},
   "outputs": [],
   "source": [
    "import MDAnalysis as md\n",
    "from MDAnalysis import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from MDAnalysis.analysis.leaflet import LeafletFinder, optimize_cutoff\n",
    "from matplotlib.pyplot import figure\n",
    "from MDAnalysis.analysis.rdf import InterRDF\n",
    "import math\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f137f17d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3001"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim = md.Universe('npt_prod_1.gro', 'npt_prod_1-3.trr')\n",
    "len(sim.trajectory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "161ff0ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "NP_CS = sim.select_atoms(\"name C18S and resname NP24\")\n",
    "NP_CF = sim.select_atoms(\"name C18F and resname NP24\")\n",
    "NP_NF = sim.select_atoms(\"name NF and resname NP24\")\n",
    "AP_CS = sim.select_atoms(\"name C18S and resname AP24\")\n",
    "AP_CF = sim.select_atoms(\"name C18F and resname AP24\")\n",
    "AP_NF = sim.select_atoms(\"name NF and resname AP24\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "681f027d",
   "metadata": {},
   "outputs": [],
   "source": [
    "NP_angles=[];\n",
    "AP_angles=[];\n",
    "for ts in sim.trajectory:\n",
    "    i = 1\n",
    "    for a, b, c in zip(NP_CS.positions, NP_NF.positions, NP_CF.positions):\n",
    "        ba = a - b\n",
    "        bc = c - b\n",
    "        cosine_angle = np.dot(ba,bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))\n",
    "        ang = np.arccos(cosine_angle)\n",
    "        NP_angles.append(( sim.trajectory.time, i, np.degrees(ang) ))\n",
    "        i = i+1\n",
    "        \n",
    "    i = 1    \n",
    "    for a, b, c in zip(AP_CS.positions, AP_NF.positions, AP_CF.positions):\n",
    "        ba = a - b\n",
    "        bc = c - b\n",
    "        cosine_angle = np.dot(ba,bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))\n",
    "        ang = np.arccos(cosine_angle)\n",
    "        AP_angles.append(( sim.trajectory.time, i, np.degrees(ang) ))\n",
    "        i = i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "232db327",
   "metadata": {},
   "outputs": [],
   "source": [
    "NP_angles = np.array(NP_angles)\n",
    "NP = pd.DataFrame(dict(time=NP_angles[:,0], value=NP_angles[:,2], id=NP_angles[:,1], Ceramide='NP'))\n",
    "AP_angles = np.array(AP_angles)\n",
    "AP = pd.DataFrame(dict(time=AP_angles[:,0], value=AP_angles[:,2], id=AP_angles[:,1], Ceramide='AP'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ea6249c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = NP.append(AP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bafea8d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['text.color'] = 'black'\n",
    "plt.rcParams['axes.labelcolor'] = 'black'\n",
    "figure(num=None, figsize=(2.5,0.5), dpi=600)\n",
    "\n",
    "#plt.boxplot(NP.value, AP.value)\n",
    "\n",
    "PROPS = {\n",
    "    'boxprops':{'facecolor':'none', 'edgecolor':'blue'},\n",
    "    'medianprops':{'color':'blue'},\n",
    "    'whiskerprops':{'color':'blue'},\n",
    "    'capprops':{'color':'blue'},\n",
    "    'flierprops':{'markerfacecolor':'none','markeredgecolor':'deepskyblue', 'marker':'|', 'linewidth':'1.0'}\n",
    "}\n",
    "\n",
    "g = sns.boxplot(y=\"Ceramide\", x=\"value\", data=df,  linewidth=0.5, \n",
    "                fliersize=0.5, **PROPS)\n",
    "\n",
    "\n",
    "\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.xticks((np.arange(0, 181, step=60)),fontsize=6)\n",
    "plt.yticks(fontsize=6)\n",
    "#plt.legend()\n",
    "plt.xlim([-5, 185])\n",
    "\n",
    "plt.savefig('cer-ext_angle.png',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "367f1a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "163.4067509704453"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AP.value.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d772058b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155.48176414212273"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NP.value.mean()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}