{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading µs-ALEX data from Photon-HDF5 with h5py\n",
    "\n",
    "*In this notebook we show how to read a µs-ALEX smFRET measurement stored in*\n",
    "*[Photon-HDF5 format](http://photon-hdf5.readthedocs.org/)*\n",
    "*using python and a few common scientific libraries (numpy, **h5py**, matplotlib).*\n",
    "*Specifically, we show how to load timestamps, build an alternation histogram*\n",
    "*and select photons in the donor and acceptor excitation periods.*\n",
    "\n",
    "*See also a [similar notebook](Reading µs-ALEX data from Photon-HDF5.ipynb) using* \n",
    "*[pytables](http://www.pytables.org/) instead of [h5py](http://www.h5py.org/).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division, print_function  # only needed on py2\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Utility functions\n",
    "\n",
    "Here we define an utility function to print HDF5 file contents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_children(group):\n",
    "    \"\"\"Print all the sub-groups in `group` and leaf-nodes children of `group`.\n",
    "\n",
    "    Parameters:\n",
    "        data_file (h5py HDF5 file object): the data file to print\n",
    "    \"\"\"\n",
    "    for name, value in group.items():\n",
    "        if isinstance(value, h5py.Group):\n",
    "            content = '(Group)'\n",
    "        else:\n",
    "            content = value[()]\n",
    "        print(name)\n",
    "        print('    Content:     %s' % content)\n",
    "        print('    Description: %s\\n' % value.attrs['TITLE'].decode())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Open the data file\n",
    "\n",
    "Let assume we have a Photon-HDF5 file at the following location:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename = '../data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can open the file, as a normal HDF5 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "h5file = h5py.File(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The object `h5file` is a pytables file reference. The root group is accessed with `h5file.root`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Print the content\n",
    "\n",
    "Let's start by taking a look at the file content:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acquisition_duration\n",
      "    Content:     600.0\n",
      "    Description: Measurement duration in seconds.\n",
      "\n",
      "description\n",
      "    Content:     b'us-ALEX measurement of a doubly-labeled ssDNA sample.'\n",
      "    Description: A user-defined comment describing the data file.\n",
      "\n",
      "identity\n",
      "    Content:     (Group)\n",
      "    Description: Information about the Photon-HDF5 data file.\n",
      "\n",
      "photon_data\n",
      "    Content:     (Group)\n",
      "    Description: Group containing arrays of photon-data.\n",
      "\n",
      "provenance\n",
      "    Content:     (Group)\n",
      "    Description: Information about the original data file.\n",
      "\n",
      "sample\n",
      "    Content:     (Group)\n",
      "    Description: Information about the measured sample.\n",
      "\n",
      "setup\n",
      "    Content:     (Group)\n",
      "    Description: Information about the experimental setup.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_children(h5file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the typical Photon-HDF5 structure. In particular the field `description` provides a short description of the measurement and `acquisition_duration` tells that the acquisition lasted 600 seconds.\n",
    "\n",
    "As an example let's take a look at the content of the `sample` group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buffer_name\n",
      "    Content:     b'TE50 + 0.5M GndCl'\n",
      "    Description: A descriptive name for the buffer.\n",
      "\n",
      "dye_names\n",
      "    Content:     b'Cy3B, ATTO647N'\n",
      "    Description: String containing a comma-separated list of dye or fluorophore names.\n",
      "\n",
      "num_dyes\n",
      "    Content:     2\n",
      "    Description: Number of different dyes present in the samples.\n",
      "\n",
      "sample_name\n",
      "    Content:     b'20dt ssDNA oligo doubly labeled with Cy3B and Atto647N'\n",
      "    Description: A descriptive name for the sample.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_children(h5file['sample'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we define a shortcut to the `photon_data` group to save some typing later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "photon_data = h5file['photon_data']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Reading the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we make sure the file contains the right type of measurement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'smFRET-usALEX'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "photon_data['measurement_specs']['measurement_type'][()].decode()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, tha's what we espect. \n",
    "\n",
    "Now we can load all the timestamps (including timestamps unit) and detectors arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "timestamps = photon_data['timestamps'][:]\n",
    "timestamps_unit = photon_data['timestamps_specs']['timestamps_unit'][()]\n",
    "detectors = photon_data['detectors'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of photons: 2683962\n",
      "Timestamps unit:   1.25e-08 seconds\n",
      "Detectors:         [0 1]\n"
     ]
    }
   ],
   "source": [
    "print('Number of photons: %d' % timestamps.size)\n",
    "print('Timestamps unit:   %.2e seconds' % timestamps_unit)\n",
    "print('Detectors:         %s' % np.unique(detectors))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may want to check the excitation wavelengths used in the measurement. This information is found in the setup group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  5.32000000e-07,   6.35000000e-07])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5file['setup']['excitation_wavelengths'][:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's load the definitions of donor/acceptor channel and excitation periods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Donor CH: 0     Acceptor CH: 1\n"
     ]
    }
   ],
   "source": [
    "donor_ch = photon_data['measurement_specs']['detectors_specs']['spectral_ch1'][()]\n",
    "acceptor_ch = photon_data['measurement_specs']['detectors_specs']['spectral_ch2'][()]\n",
    "print('Donor CH: %d     Acceptor CH: %d' % (donor_ch, acceptor_ch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ALEX period:     4000  \n",
      "Offset:          700      \n",
      "Donor period:    [2180 3900]      \n",
      "Acceptor period: [ 200 1800]\n"
     ]
    }
   ],
   "source": [
    "alex_period = photon_data['measurement_specs']['alex_period'][()]\n",
    "donor_period = photon_data['measurement_specs']['alex_excitation_period1'][()]\n",
    "offset = photon_data['measurement_specs']['alex_offset'][()]\n",
    "acceptor_period = photon_data['measurement_specs']['alex_excitation_period2'][()]\n",
    "print('ALEX period:     %d  \\nOffset:         %4d      \\nDonor period:    %s      \\nAcceptor period: %s' % \\\n",
    "      (alex_period, offset, donor_period, acceptor_period))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These numbers define the donor and acceptor alternation periods as shown below:\n",
    "\n",
    "$$2180 < \\widetilde{t} < 3900 \\qquad \\textrm{donor period}$$\n",
    "\n",
    "$$200 < \\widetilde{t} < 1800 \\qquad \\textrm{acceptor period}$$\n",
    "\n",
    "where $\\widetilde{t}$ represent the (`timestamps` - `offset`) **MODULO** `alex_period`.\n",
    "\n",
    "For more information\n",
    "please refer to the [measurements_specs section](http://photon-hdf5.readthedocs.org/en/latest/phdata.html#measurement-specs)\n",
    "of the *Reference Documentation*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Plotting the alternation histogram\n",
    "\n",
    "Let start by separating timestamps from donor and acceptor channels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "timestamps_donor = timestamps[detectors == donor_ch]\n",
    "timestamps_acceptor = timestamps[detectors == acceptor_ch]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the data has been loaded we can plot an alternation histogram using *matplotlib*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5mZmZFcAJ1szMrABOsGZmZgVwgjUzMyuAE6yZmVkBnGDNzMwK4ARrZmZWgKZJ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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c825940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.hist((timestamps_acceptor - offset) % alex_period, bins=100, alpha=0.8, color='red', label='donor')\n",
    "ax.hist((timestamps_donor - offset) % alex_period, bins=100, alpha=0.8, color='green', label='acceptor')\n",
    "ax.axvspan(donor_period[0], donor_period[1], alpha=0.3, color='green')\n",
    "ax.axvspan(acceptor_period[0], acceptor_period[1], alpha=0.3, color='red')\n",
    "ax.set_xlabel('(timestamps - offset) MOD alex_period')\n",
    "ax.set_title('ALEX histogram')\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Timestamps in different excitation periods\n",
    "\n",
    "We conclude by showing, as an example, how to create arrays of timestamps containing only donor or acceptor exitation photons.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "timestamps_mod = (timestamps - offset) % alex_period\n",
    "donor_excitation = (timestamps_mod < donor_period[1])*(timestamps_mod > donor_period[0])\n",
    "acceptor_excitation = (timestamps_mod < acceptor_period[1])*(timestamps_mod > acceptor_period[0])\n",
    "timestamps_Dex = timestamps[donor_excitation]\n",
    "timestamps_Aex = timestamps[acceptor_excitation]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9EJeZmVmX9EX36WRgZs7PBA6slF8aES9ExFJgCTBR0ghgi4i4NSICuLBSx8zM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c8257b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.hist((timestamps_Dex - offset) % alex_period, bins=np.arange(0, alex_period, 40), alpha=0.8, color='green', label='D_ex')\n",
    "ax.hist((timestamps_Aex - offset) % alex_period, bins=np.arange(0, alex_period, 40), alpha=0.8, color='red', label='A_ex')\n",
    "ax.set_xlabel('(timestamps - offset) MOD alex_period')\n",
    "ax.set_title('ALEX histogram (selected periods only)')\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "137px",
    "width": "252px"
   },
   "navigate_menu": true,
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