{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Block averages (standard deviation, standard error)\n",
    "\n",
    "It is typically useful to divide an MD trajectory into non-overlapping segments, and to calculate properties for each segment individually.\n",
    "\n",
    "You can then calculate the average and standard deviation or standard error of any calculated quantity.\n",
    "\n",
    "This might be useful to determine if equilibration has been reached, or to get error bars on any calculated quantity.\n",
    "\n",
    "``lionanalysis`` includes a utility called ``blocklionanalysis.sh`` which is located in the ``utils/`` folder of the source tree. It divides the trajectory into N non-overlapping segments and runs the analysis for each segment. All outputs are saved for later analysis.\n",
    "\n",
    "To proceed, make sure ``blocklionanalysis.sh`` is in your PATH."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "olblock.sh -h\n",
      "    show help\n",
      "olblock.sh -d <dumpfile> -c <configfile> -n <numblocks> [-N <numframes>]  [-X <file-with-numframes>]\n",
      "olblock.sh -d <dumpfile> -c <configfile> -s <sizeblock> [-N <numframes>]  [-X <file-with-numframes>]\n",
      "    -n : give the number of blocks (program finds out size of each block)\n",
      "    -s : give the size of teach block (program finds out the number of blocks)\n",
      "    -t : run total as well\n",
      "    -N : specify the total number of frames in <dumpfile> (default is to find out, but can take some time for large dumpfiles)\n",
      "    -X : the <file-with-numframes> contains a single word with the number of frames in <dumpfile>\n"
     ]
    }
   ],
   "source": [
    "%%sh\n",
    "blocklionanalysis.sh -h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5001 frames in trajectories/NaOH.xyz\n",
      "#blocks: 4 ; #frames per block: 1250 (block 4: 1251 ); I will start at block 1 and stop at block 4\n",
      "olflags: \n",
      "AT BLOCK 1 (size 1250); startbyte 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: DumpFile trajectories/NaOH.xyz specified in config file.\n",
      "WARNING: I will NOT use this file but instead the one specified on command line: trajectories/NaOH.xyz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StartByte 0\n",
      "DumpFile trajectories/NaOH.xyz\n",
      "MinTimestep 0\n",
      "MaxTimestep 1250\n",
      "Suffix _4_1\n",
      "Overwrite\n",
      "Threads 1\n",
      "CoutFrequency 200\n",
      "DefineGroup Na ATOMICNUMBER Na\n",
      "DefineGroup O ATOMICNUMBER O\n",
      "RDF Na Na rdf_na_na.txt Resolution 0.2 MaxDist 6.0\n",
      "RDF Na O rdf_na_o.txt Resolution 0.1 MaxDist 6.0\n",
      "###########################################################\n",
      "Running on 1 threads on host .SCMCoffee1\n",
      "There are 3 groups and 5 actions.\n",
      "I am lionanalysis version 8:10M\n",
      "Opening dumpfile trajectories/NaOH.xyz at byte 0 : 0\n",
      "      -999       (1) (       0 ps) (0.000216961 s) (  0.0201 %)\n",
      "      -800       (200) (     199 ps) (0.091712952 s) (       4 %)\n",
      "      -600       (400) (     399 ps) (0.1808629 s) (       8 %)\n",
      "      -400       (600) (     599 ps) (0.26924992 s) (      12 %)\n",
      "      -200       (800) (     799 ps) (0.35710692 s) (      16 %)\n",
      "         0       (1000) (     999 ps) (0.44463396 s) (      20 %)\n",
      "       200       (1200) (    1199 ps) (0.53194404 s) (      24 %)\n",
      "Closing dumpfile trajectories/NaOH.xyz at byte 6260265\n",
      "Finished reading dump file... 1250 frames... Writing output....\n",
      "FIRST TIMESTEP: -999\n",
      "LAST TIMESTEP: 250\n",
      "Total time: 0.55458498 s; 0.00044366798 s/timestep\n",
      "Goodbye!\n",
      "AT BLOCK 2 (size 1250); startbyte 6260265\n",
      "StartByte 6260265\n",
      "DumpFile trajectories/NaOH.xyz\n",
      "MinTimestep 0\n",
      "MaxTimestep 1250\n",
      "Suffix _4_2\n",
      "Overwrite\n",
      "Threads 1\n",
      "CoutFrequency 200\n",
      "DefineGroup Na ATOMICNUMBER Na\n",
      "DefineGroup O ATOMICNUMBER O\n",
      "RDF Na Na rdf_na_na.txt Resolution 0.2 MaxDist 6.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: DumpFile trajectories/NaOH.xyz specified in config file.\n",
      "WARNING: I will NOT use this file but instead the one specified on command line: trajectories/NaOH.xyz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RDF Na O rdf_na_o.txt Resolution 0.1 MaxDist 6.0\n",
      "###########################################################\n",
      "Running on 1 threads on host .SCMCoffee1\n",
      "There are 3 groups and 5 actions.\n",
      "I am lionanalysis version 8:10M\n",
      "Opening dumpfile trajectories/NaOH.xyz at byte 6260265 : 6260265\n",
      "      -999       (1) (       0 ps) (0.000211 s) (      25 %)\n",
      "      -800       (200) (     199 ps) (0.088485003 s) (      29 %)\n",
      "      -600       (400) (     399 ps) (0.177001 s) (      33 %)\n",
      "      -400       (600) (     599 ps) (0.26565289 s) (      37 %)\n",
      "      -200       (800) (     799 ps) (0.35426211 s) (      41 %)\n",
      "         0       (1000) (     999 ps) (0.44262195 s) (      45 %)\n",
      "       200       (1200) (    1199 ps) (0.53090692 s) (      49 %)\n",
      "Closing dumpfile trajectories/NaOH.xyz at byte 12518905\n",
      "Finished reading dump file... 1250 frames... Writing output....\n",
      "FIRST TIMESTEP: -999\n",
      "LAST TIMESTEP: 250\n",
      "Total time: 0.55390191 s; 0.00044312153 s/timestep\n",
      "Goodbye!\n",
      "AT BLOCK 3 (size 1250); startbyte 12518905\n",
      "StartByte 12518905\n",
      "DumpFile trajectories/NaOH.xyz\n",
      "MinTimestep 0\n",
      "MaxTimestep 1250\n",
      "Suffix _4_3\n",
      "Overwrite\n",
      "Threads 1\n",
      "CoutFrequency 200\n",
      "DefineGroup Na ATOMICNUMBER Na\n",
      "DefineGroup O ATOMICNUMBER O\n",
      "RDF Na Na rdf_na_na.txt Resolution 0.2 MaxDist 6.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: DumpFile trajectories/NaOH.xyz specified in config file.\n",
      "WARNING: I will NOT use this file but instead the one specified on command line: trajectories/NaOH.xyz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RDF Na O rdf_na_o.txt Resolution 0.1 MaxDist 6.0\n",
      "###########################################################\n",
      "Running on 1 threads on host .SCMCoffee1\n",
      "There are 3 groups and 5 actions.\n",
      "I am lionanalysis version 8:10M\n",
      "Opening dumpfile trajectories/NaOH.xyz at byte 12518905 : 12518905\n",
      "      -999       (1) (       0 ps) (0.000208855 s) (      50 %)\n",
      "      -800       (200) (     199 ps) (0.088582993 s) (      54 %)\n",
      "      -600       (400) (     399 ps) (0.17657089 s) (      58 %)\n",
      "      -400       (600) (     599 ps) (0.26465487 s) (      62 %)\n",
      "      -200       (800) (     799 ps) (0.35317087 s) (      66 %)\n",
      "         0       (1000) (     999 ps) (0.44119096 s) (      70 %)\n",
      "       200       (1200) (    1199 ps) (0.52900982 s) (      74 %)\n",
      "Closing dumpfile trajectories/NaOH.xyz at byte 18777733\n",
      "Finished reading dump file... 1250 frames... Writing output....\n",
      "FIRST TIMESTEP: -999\n",
      "LAST TIMESTEP: 250\n",
      "Total time: 0.55171084 s; 0.00044136868 s/timestep\n",
      "Goodbye!\n",
      "AT BLOCK 4 (size 1251); startbyte 18777733\n",
      "StartByte 18777733\n",
      "DumpFile trajectories/NaOH.xyz\n",
      "MinTimestep 0\n",
      "Suffix _4_4\n",
      "Overwrite\n",
      "Threads 1\n",
      "CoutFrequency 200\n",
      "DefineGroup Na ATOMICNUMBER Na\n",
      "DefineGroup O ATOMICNUMBER O\n",
      "RDF Na Na rdf_na_na.txt Resolution 0.2 MaxDist 6.0\n",
      "RDF Na O rdf_na_o.txt Resolution 0.1 MaxDist 6.0\n",
      "###########################################################\n",
      "Running on 1 threads on host .SCMCoffee1\n",
      "There are 3 groups and 5 actions.\n",
      "I am lionanalysis version 8:10M\n",
      "Opening dumpfile trajectories/NaOH.xyz at byte 18777733 : 18777733\n",
      "      -999       (1) (       0 ps) (0.000233889 s) (      75 %)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: DumpFile trajectories/NaOH.xyz specified in config file.\n",
      "WARNING: I will NOT use this file but instead the one specified on command line: trajectories/NaOH.xyz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      -800       (200) (     199 ps) (0.089896917 s) (      79 %)\n",
      "      -600       (400) (     399 ps) (0.17751789 s) (      83 %)\n",
      "      -400       (600) (     599 ps) (0.266675 s) (      87 %)\n",
      "      -200       (800) (     799 ps) (0.35485387 s) (      91 %)\n",
      "         0       (1000) (     999 ps) (0.44257784 s) (      95 %)\n",
      "       200       (1200) (    1199 ps) (0.53165197 s) (      99 %)\n",
      "Closing dumpfile trajectories/NaOH.xyz at byte -1\n",
      "Finished reading dump file... 1251 frames... Writing output....\n",
      "FIRST TIMESTEP: -999\n",
      "LAST TIMESTEP: 251\n",
      "Total time: 0.55490279 s; 0.00044356738 s/timestep\n",
      "Goodbye!\n"
     ]
    }
   ],
   "source": [
    "%%sh\n",
    "cat > ba1.config <<EOF\n",
    "Overwrite\n",
    "Threads 1\n",
    "CoutFrequency 200\n",
    "\n",
    "DefineGroup Na ATOMICNUMBER Na\n",
    "DefineGroup O ATOMICNUMBER O\n",
    "RDF Na Na rdf_na_na.txt Resolution 0.2 MaxDist 6.0\n",
    "RDF Na O rdf_na_o.txt Resolution 0.1 MaxDist 6.0\n",
    "EOF\n",
    "# specify config file, trajectory, and number of blocks/segments\n",
    "blocklionanalysis.sh -c ba1.config -d trajectories/NaOH.xyz -n 4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the input file we specified to store the RDF in rdf_na_o.txt. The block averaging has created four output files, rdf_na_o.txt_4_1, ..., rdf_na_o.txt_4_4. The first number is the number of blocks, and the second the block index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "from common import rdf2df\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "rdf_files = list(Path(\".\").glob(\"rdf_na_o.txt_*_*\"))\n",
    "dfs = [rdf2df(x) for x in rdf_files]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "for df in dfs:\n",
    "    df.plot(x=0, y=1, ax=ax, xlabel=\"r (ang.)\", ylabel=\"g(r)\")\n",
    "\n",
    "ax.legend(rdf_files);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With seaborn it is easy to plot the 95% confidence interval as a shaded area:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "aggregated_df = pd.concat(dfs)\n",
    "\n",
    "sns.lineplot(aggregated_df, x=\"#r\", y=\"RDF\");\n",
    "# sns.lineplot(aggregated_df, x=\"#r\", y=\"RDF\", err_style=\"bars\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "openmm",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}