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orbeckst/hole_hacking.ipynb

Last active July 28, 2016 19:06
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Example for using the MDAnalysis.analysis.hole module (http://www.mdanalysis.org/mdanalysis/documentation_pages/analysis/hole.html).
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hole_hacking.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Hole module "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.style.use('ggplot')\n",
"\n",
"import MDAnalysis as mda\n",
"import MDAnalysis.analysis.hole"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"MDAnalysis : INFO MDAnalysis 0.15.1-dev0 STARTED logging to 'MDAnalysis.log'\n",
"INFO:MDAnalysis:MDAnalysis 0.15.1-dev0 STARTED logging to 'MDAnalysis.log'\n"
]
}
],
"source": [
"mda.start_logging()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I am testing HOLE on a simple \"trajectory\": I submitted gramicidin A (1grm, test file `1gmr_single.pdb`) to the [ElNemo](http://www.sciences.univ-nantes.fr/elnemo/) elastic network server. From an ENM it computed a number of modes and provided multi-frame PDB files showing these modes. I just picked the first mode (which is labelled mode 7), which shows a twist motion along the pore axis. Note that this \"trajectory\" is exaggerated and does not correspond to real protein motion.\n",
"\n",
"The trajectory can be downloaded from https://www.dropbox.com/sh/c2d6i24oz00idqp/AACjPT_T1fnfo-qNB_ksFKEta?dl=0."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"u = mda.Universe(\"./1grm_elNemo_mode7.pdb.bz2\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the latest version of MDAnalysis (development or 0.16.0 when it comes out), this trajectory is included:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"DEBUG:MDAnalysis.core.AtomGroup:Universe.load_new(): loading /Volumes/Data/oliver/Biop/Library/python/mdanalysis/testsuite/MDAnalysisTests/data/1grm_elNemo_mode7.pdb.bz2...\n"
]
}
],
"source": [
"from MDAnalysis.tests.datafiles import MULTIPDB_HOLE\n",
"u = mda.Universe(MULTIPDB_HOLE)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running HOLE\n",
"You need to install `HOLE` first. Binaries are available from http://www.smartsci.uk/hole/ (unfortunately, it is not open source).\n",
"\n",
"Set up the `HOLEtraj` class; provide a path to the `hole` executable."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"H = MDAnalysis.analysis.hole.HOLEtraj(u, executable=\"~/hole2/exe/hole\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then run `hole` on the individual frames (this takes a while...)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 0 (orderparameter 0)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 0 (orderparameter 0)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpfAanxR.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpfAanxR.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpfAanxR.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpfAanxR.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (447 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 1 (orderparameter 1)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 1 (orderparameter 1)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpgwRTeN.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpgwRTeN.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpgwRTeN.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpgwRTeN.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (465 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 2 (orderparameter 2)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 2 (orderparameter 2)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpYqjASN.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpYqjASN.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpYqjASN.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpYqjASN.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (425 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 3 (orderparameter 3)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 3 (orderparameter 3)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpIMksnm.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpIMksnm.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpIMksnm.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpIMksnm.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (421 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 4 (orderparameter 4)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 4 (orderparameter 4)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmperYW9H.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmperYW9H.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmperYW9H.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmperYW9H.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (551 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 5 (orderparameter 5)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 5 (orderparameter 5)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpqAtZy0.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpqAtZy0.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpqAtZy0.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpqAtZy0.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (447 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 6 (orderparameter 6)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 6 (orderparameter 6)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpTxv6i_.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpTxv6i_.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpTxv6i_.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpTxv6i_.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (399 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 7 (orderparameter 7)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 7 (orderparameter 7)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpIGqMj9.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpIGqMj9.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpIGqMj9.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpIGqMj9.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (355 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 8 (orderparameter 8)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 8 (orderparameter 8)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpZTLZIc.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpZTLZIc.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpZTLZIc.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpZTLZIc.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (363 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 9 (orderparameter 9)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 9 (orderparameter 9)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpIUVOSh.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpIUVOSh.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpIUVOSh.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpIUVOSh.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (369 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n",
"MDAnalysis.analysis.hole: INFO HOLE analysis frame 10 (orderparameter 10)\n",
"INFO:MDAnalysis.analysis.hole:HOLE analysis frame 10 (orderparameter 10)\n",
"DEBUG:MDAnalysis.analysis.hole:path check: HOLE will not read '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' because it has more than 70 characters.\n",
"DEBUG:MDAnalysis.analysis.hole:path check: Using relative path: '/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/notebooks/hole-basics/simple2.rad' --> 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpxrVHOb.pdb'\n",
"INFO:MDAnalysis.analysis.hole:Setting up HOLE analysis for '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpxrVHOb.pdb'\n",
"MDAnalysis.analysis.hole: INFO Using radius file 'simple2.rad'\n",
"INFO:MDAnalysis.analysis.hole:Using radius file 'simple2.rad'\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CPOINT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CPOINT\n",
"MDAnalysis.analysis.hole: INFO HOLE will guess CVECT\n",
"INFO:MDAnalysis.analysis.hole:HOLE will guess CVECT\n",
"MDAnalysis.analysis.hole: INFO Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpxrVHOb.pdb' (trajectory: None)\n",
"INFO:MDAnalysis.analysis.hole:Starting HOLE on '/var/folders/qs/k07z49fh8xl6xd008k8wxhxr0000gp/T/tmpxrVHOb.pdb' (trajectory: None)\n",
"DEBUG:MDAnalysis.analysis.hole:/Volumes/Data/oliver/Biop/Library/hole2/exe/hole <(input) >hole.out\n",
"MDAnalysis.analysis.hole: INFO HOLE finished: output file 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:HOLE finished: output file 'hole.out'\n",
"MDAnalysis.analysis.hole: INFO Collecting HOLE profiles for run with id 1\n",
"INFO:MDAnalysis.analysis.hole:Collecting HOLE profiles for run with id 1\n",
"MDAnalysis.analysis.hole: INFO Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"INFO:MDAnalysis.analysis.hole:Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
"DEBUG:MDAnalysis.analysis.hole:Started reading data\n",
"DEBUG:MDAnalysis.analysis.hole:Collected HOLE profile for frame 0 (391 datapoints)\n",
"DEBUG:MDAnalysis.analysis.hole:Finished with frame 0, saved as './run_1/radii_1_0000.dat.gz'\n",
"MDAnalysis.analysis.hole: INFO Collected HOLE radius profiles for 1 frames\n",
"INFO:MDAnalysis.analysis.hole:Collected HOLE radius profiles for 1 frames\n"
]
}
],
"source": [
"H.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The trajectory contains 11 frames. We are typically interested in the *HOLE profile*, i.e., a plot of the pore radius as determined by HOLE against the reaction coordinate (often close to $z$ along the pore axis for simple channels).\n",
"\n",
"All profiles are stored as\n",
"```python\n",
"H.profiles\n",
"```\n",
"which is a `dict`. The keys are the frame numbers (or a user supplied reaction coordinate for the conformation). The values are record arrays with columns `frame` (the reaction coordinate for the conformation), `rxncoord` (the HOLE pore reaction coordinate), and `radius` (radius in Angstrom).\n",
"\n",
"\n",
"The `H.plot()` method plots all the profiles together, the `H.plot3D()` stacks them:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
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LarhEH6Jn4LRIvkpc5S201U9dQQE4S64glYjTHwhmbFdQULjwyLJM3d5Oyi+funoCaK3v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\ntN7XAMs6UMiwCeEnNV1n6XfRnOnHkCG2zXs7co4SHOKIQpAjSRKlNc0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9aMz04pgxvdsE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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bb3e690>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = H.plot()\n",
"ax.set_ylim(1, 4)\n",
"ax.set_xlim(-15, 25)\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.Axes3DSubplot at 0x10c370250>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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jutnBzgBTS8aEOugPMJXN1MvG9CAKAlF3wJTEEHaEmL0Yb5tEq6+3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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c767b90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"H.plot3D(rmax=4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Working with HOLE profiles\n",
"The output from HOLE is a pore profile $R(\\zeta)$ (radius $R$ vs pore coordinate $\\zeta$).\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pore profile data structure\n",
"All pore profiles are stored in a `dict` named `H.profiles` that is indexed by the frame number. Each pore profile itself is a array containig rows with *frame number* (all identical), *pore coordinate*, and *pore radius*. For more details, see the docs on [HOLE data structures](http://pythonhosted.org/MDAnalysis/documentation_pages/analysis/hole.html#data-structures)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"H.profiles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Accessing profiles on a per-frame basis\n",
"To get at individual profiles in we can iterate over `H.profiles`. However, as a convenience, just iterating over the `HOLEtraj` instance itself will return the profiles in *frame-sorted order* (thanks to [HOLEtraj.sorted_profiles_iter()](http://pythonhosted.org/MDAnalysis/documentation_pages/analysis/hole.html#MDAnalysis.analysis.hole.HOLEtraj.sorted_profiles_iter)).\n",
"\n",
"For example, getting the minimum radius "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"frame 0: min(R) = 1.61053\n",
"frame 1: min(R) = 1.56725\n",
"frame 2: min(R) = 1.53256\n",
"frame 3: min(R) = 1.42478\n",
"frame 4: min(R) = 1.2435\n",
"frame 5: min(R) = 1.19875\n",
"frame 6: min(R) = 1.2961\n",
"frame 7: min(R) = 1.44253\n",
"frame 8: min(R) = 1.51073\n",
"frame 9: min(R) = 1.52824\n",
"frame 10: min(R) = 1.56024\n"
]
}
],
"source": [
"for frame, profile in H:\n",
" print(\"frame {}: min(R) = {}\".format(frame, profile.radius.min()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(For a slightly more advanced [analysis of minimum pore radius as function of a order parameter](http://nbviewer.jupyter.org/gist/orbeckst/a0c856f58059112538591af108df6d59#Analysis-of-the-minimum-pore-radius) see the [Advanced HOLE Hacking Notebook](http://nbviewer.jupyter.org/gist/orbeckst/a0c856f58059112538591af108df6d59).)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extracting data to a file\n",
"Sometimes you want to have the pore profiles in a simple data file. Here we are using the popular [XMGRACE](http://plasma-gate.weizmann.ac.il/Grace/) XY format where each row will contain\n",
"```\n",
" pore_coordinate pore_radius\n",
"```\n",
"and datasets for frames are separated by\n",
"```\n",
"&\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open(\"profiles.xvg\", \"w\") as xvg:\n",
" for frame, profile in H:\n",
" for _, zeta, radius in profile:\n",
" xvg.write(\"{0} {1}\\n\".format(zeta, radius))\n",
" xvg.write(\"&\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The corresponding output file looks like this:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"21.387 10.41399\n",
"21.487 10.46226\n",
"21.587 11.06096\n",
"21.687 12.11576\n",
"21.787 13.51356\n",
"21.887 14.94337\n",
"21.987 16.99936\n",
"&\n",
"-22.01283 21.9034\n",
"-21.91283 19.06584\n",
"-21.81283 16.65472\n",
"-21.71283 14.91447\n",
"-21.61283 13.49857\n",
"-21.51283 11.55906\n",
"-21.41283 10.27238\n",
"-21.31283 9.97615\n",
"-21.21283 9.9346\n",
"-21.11283 9.84064\n",
"-21.01283 9.74689\n",
"-20.91283 9.65261\n",
"\n"
]
}
],
"source": [
"print(\"\".join(open(\"profiles.xvg\").readlines()[440:460]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One can use the above code to write the profiles in any format; for instance, it would be easy to write each frame's profile to a separate file by moving the `with` statement *inside* the outer `for` loop."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating an \"average\" HOLE profile "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At the moment (MDAnalysis 0.15.0) does not have a method to compute an average HOLE profile. However, in the following I demonstrate how this can be accomplished using the [numkit.timeseries.regularized_function](http://gromacswrapper.readthedocs.io/en/latest/numkit/timeseries.html#numkit.timeseries.regularized_function) (a function found in [GromacsWrapper](http://gromacswrapper.readthedocs.io/)) but we can also use it independently (I copied it from [numkit/timeseries.py](https://github.com/Becksteinlab/GromacsWrapper/blob/develop/numkit/timeseries.py#L519)):"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy\n",
"from itertools import izip\n",
"\n",
"def regularized_function(x, y, func, bins=100, range=None):\n",
" \"\"\"Compute *func()* over data aggregated in bins.\n",
" ``(x,y) --> (x', func(Y'))`` with ``Y' = {y: y(x) where x in x' bin}``\n",
" First the data is collected in bins x' along x and then *func* is\n",
" applied to all data points Y' that have been collected in the bin.\n",
" .. function:: func(y) -> float\n",
" *func* takes exactly one argument, a numpy 1D array *y* (the\n",
" values in a single bin of the histogram), and reduces it to one\n",
" scalar float.\n",
" .. Note:: *x* and *y* must be 1D arrays.\n",
" :Arguments:\n",
" x\n",
" abscissa values (for binning)\n",
" y\n",
" ordinate values (func is applied)\n",
" func\n",
" a numpy ufunc that takes one argument, func(Y')\n",
" bins\n",
" number or array\n",
" range\n",
" limits (used with number of bins)\n",
" :Returns:\n",
" F,edges\n",
" function and edges (``midpoints = 0.5*(edges[:-1]+edges[1:])``)\n",
" (This function originated as\n",
" :func:`recsql.sqlfunctions.regularized_function`.)\n",
" \"\"\"\n",
" _x = numpy.asarray(x)\n",
" _y = numpy.asarray(y)\n",
"\n",
" if len(_x.shape) != 1 or len(_y.shape) != 1:\n",
" raise TypeError(\"Can only deal with 1D arrays.\")\n",
"\n",
" # setup of bins (taken from numpy.histogram)\n",
" if (range is not None):\n",
" mn, mx = range\n",
" if (mn > mx):\n",
" raise AttributeError('max must be larger than min in range parameter.')\n",
"\n",
" if not numpy.iterable(bins):\n",
" if range is None:\n",
" range = (_x.min(), _x.max())\n",
" mn, mx = [float(mi) for mi in range]\n",
" if mn == mx:\n",
" mn -= 0.5\n",
" mx += 0.5\n",
" bins = numpy.linspace(mn, mx, bins+1, endpoint=True)\n",
" else:\n",
" bins = numpy.asarray(bins)\n",
" if (numpy.diff(bins) < 0).any():\n",
" raise ValueError('bins must increase monotonically.')\n",
"\n",
" sorting_index = numpy.argsort(_x)\n",
" sx = _x[sorting_index]\n",
" sy = _y[sorting_index]\n",
"\n",
" # boundaries in SORTED data that demarcate bins; position in bin_index is the bin number\n",
" bin_index = numpy.r_[sx.searchsorted(bins[:-1], 'left'),\n",
" sx.searchsorted(bins[-1], 'right')]\n",
"\n",
" # naive implementation: apply operator to each chunk = sy[start:stop] separately\n",
" #\n",
" # It's not clear to me how one could effectively block this procedure (cf\n",
" # block = 65536 in numpy.histogram) because there does not seem to be a\n",
" # general way to combine the chunks for different blocks, just think of\n",
" # func=median\n",
" F = numpy.zeros(len(bins)-1) # final function\n",
" F[:] = [func(sy[start:stop]) for start,stop in izip(bin_index[:-1],bin_index[1:])]\n",
" return F,bins"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The strategy is to collect all data and the histogram the radii over the HOLE reactioncoordinate:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rxncoord = np.concatenate([profile.rxncoord for frame, profile in H.sorted_profiles_iter()])\n",
"radii = np.concatenate([profile.radius for frame, profile in H.sorted_profiles_iter()])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now use `regularized_function`.\n",
"\n",
"Normally I would import it as\n",
"```python\n",
"from numkit.timeseries import regularized_function\n",
"```\n",
"but for this notebook it is not necessary to install GromacsWrapper (`pip install gromacswrapper`...)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function to reduce the data in each bin is either just the mean (`np.mean`) or the standard deviation (`np.std`). (For more ideas what else to use see the numkit notes on [coarse graining timeseries](http://gromacswrapper.readthedocs.io/en/latest/numkit/timeseries.html#coarse-graining-time-series).)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mean_r, q = regularized_function(rxncoord, radii, np.mean, bins=100)\n",
"std_r, q = regularized_function(rxncoord, radii, np.std, bins=100)\n",
"zeta = 0.5*(q[1:] + q[:-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that `regularized_function()` returns the reduced data in each bin $i$ and the bin edges (the bin $i$ is defined as $\\{q: q_i \\leq q < q_{i+1}\\}$). To plot over the bin midpoints, we compute them as $\\zeta_i = \\frac{1}{2}(q_i + q_{i+1})$."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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iYnE6+YDFd/n5HFYrh88nwecTdRmJ4iA9U279fhZ5TaLCP4IgUkPEM6SE8Yxg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\nm3HRYIwxZjMuGowxxmzGRYMxxpjN/geIA7SyitdX+wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c789110>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = plt.subplot(111)\n",
"ax.fill_between(zeta, mean_r - std_r, mean_r + std_r, color=\"blue\", alpha=0.3)\n",
"ax.plot(zeta, mean_r, color=\"blue\", lw=2)\n",
"ax.set_xlabel(r\"pore coordinate $\\zeta$ ($\\AA$)\")\n",
"ax.set_ylabel(r\"HOLE radius $R$ ($\\AA$)\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a plot of the mean pore radius (solid line) with the bands showing the standard deviation of the radius over the trajectory."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Further reading\n",
"For more advanced uses see\n",
"* notebook: [Using MDAnalysis and HOLE to create pore profiles along structural order parameters](http://nbviewer.jupyter.org/gist/orbeckst/a0c856f58059112538591af108df6d59)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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