{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [PyBroMo](http://tritemio.github.io/PyBroMo/) - 2. Generate smFRET data, including mixtures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<small><i>\n",
    "This notebook is part of <a href=\"http://tritemio.github.io/PyBroMo\" target=\"_blank\">PyBroMo</a> a \n",
    "python-based single-molecule Brownian motion diffusion simulator \n",
    "that simulates confocal smFRET\n",
    "experiments.\n",
    "</i></small>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Overview*\n",
    "\n",
    "*In this notebook we show how to generated smFRET data files from the diffusion trajectories*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the software\n",
    "\n",
    "Import all the relevant libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import tables\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pybromo as pbm\n",
    "print('Numpy version:', np.__version__)\n",
    "print('PyTables version:', tables.__version__)\n",
    "print('PyBroMo version:', pbm.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create smFRET data-files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a file for a single FRET efficiency\n",
    "\n",
    "In this section we show how to save a single smFRET data file. In the next section we will perform the same steps in a loop to generate a sequence of smFRET data files.\n",
    "\n",
    "Here we load a diffusion simulation opening a file to save\n",
    "timstamps in *write* mode. Use `'a'` (i.e. *append*) to keep \n",
    "previously simulated timestamps for the given diffusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S = pbm.ParticlesSimulation.from_datafile('0168', mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S.particles.diffusion_coeff_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#S = pbm.ParticlesSimulation.from_datafile('0168')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate timestamps of smFRET\n",
    "\n",
    "### Example1: single FRET population\n",
    "\n",
    "Define the simulation parameters with the following syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "params = dict(\n",
    "    em_rates = (200e3,),    # Peak emission rates (cps) for each population (D+A)\n",
    "    E_values = (0.75,),     # FRET efficiency for each population\n",
    "    num_particles = (20,),  # Number of particles in each population\n",
    "    bg_rate_d = 1500,       # Poisson background rate (cps) Donor channel\n",
    "    bg_rate_a = 800,        # Poisson background rate (cps) Acceptor channel\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the object that will run the simulation and print a summary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mix_sim = pbm.TimestapSimulation(S, **params)\n",
    "mix_sim.summarize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the simualtion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rs = np.random.RandomState(1234)\n",
    "mix_sim.run(rs=rs, overwrite=False, skip_existing=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save simulation to a smFRET [Photon-HDF5](http://photon-hdf5.org) file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mix_sim.save_photon_hdf5(identity=dict(author='John Doe', \n",
    "                                       author_affiliation='Planet Mars'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 2: 2 FRET populations\n",
    "\n",
    "To simulate 2 population we just define the parameters with \n",
    "one value per population, except for the Poisson background \n",
    "rate that is a single value for each channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "params = dict(\n",
    "    em_rates = (200e3, 180e3),   # Peak emission rates (cps) for each population (D+A)\n",
    "    E_values = (0.75, 0.35),     # FRET efficiency for each population\n",
    "    num_particles = (20, 15),    # Number of particles in each population\n",
    "    bg_rate_d = 1500,       # Poisson background rate (cps) Donor channel\n",
    "    bg_rate_a = 800,        # Poisson background rate (cps) Acceptor channel\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mix_sim = pbm.TimestapSimulation(S, **params)\n",
    "mix_sim.summarize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rs = np.random.RandomState(1234)\n",
    "mix_sim.run(rs=rs, overwrite=False, skip_existing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mix_sim.save_photon_hdf5()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Burst analysis\n",
    "\n",
    "The generated Photon-HDF5 files can be analyzed by any smFRET burst\n",
    "analysis program. Here we show an example using the opensource\n",
    "[FRETBursts](https://github.com/tritemio/FRETBursts/) program:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import fretbursts as fb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "filepath = list(Path('./').glob('smFRET_*'))\n",
    "filepath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d = fb.loader.photon_hdf5(str(filepath[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d.A_em"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fb.dplot(d, fb.timetrace);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d.calc_bg(fun=fb.bg.exp_fit, tail_min_us='auto', F_bg=1.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d.bg_dd, d.bg_ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d.burst_search(F=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d.num_bursts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ds = d.select_bursts(fb.select_bursts.size, th1=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ds.num_bursts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fb.dplot(d, fb.timetrace, bursts=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fb.dplot(ds, fb.hist_fret, pdf=False)\n",
    "plt.axvline(0.75);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **NOTE:** Unless you simulated a diffusion of 30s or more the previous histogram will be very poor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fb.bext.burst_data(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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