Convert ns-ALEX HT3 files to Photon-HDF5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "filename = r'Pre.ht3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import phconvert as phc\n",
    "phc.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "author = 'Eitan Lerner'\n",
    "author_affiliation = 'UCLA'\n",
    "creator = 'Antonino Ingargiola'\n",
    "creator_affiliation = 'UCLA'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "comment = 'A demostrative smFRET-nsALEX measurement.'\n",
    "sample_name = 'something'\n",
    "dye_names = ['ATTO550', 'ATTO647N']\n",
    "buffer_name = 'TE50'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare data\n",
    "\n",
    "## Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d, meta = phc.loader.nsalex_ht3(filename,\n",
    "                                donor = 0,\n",
    "                                acceptor = 1,\n",
    "                                alex_period_donor = (150, 1500),\n",
    "                                alex_period_acceptor = (1540, 3050),\n",
    "                                excitation_wavelengths = (523e-9, 628e-9),\n",
    "                                detection_wavelengths = (580e-9, 680e-9),\n",
    "                                time_reversed = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(13, 4))\n",
    "phc.plotter.alternation_hist(d, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "detectors = d['photon_data']['detectors']\n",
    "print('Detector numbers: ', np.unique(detectors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.Series(detectors).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add author and sample info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d['comment'] = comment\n",
    "\n",
    "d['sample'] = dict(\n",
    "    sample_name=sample_name,\n",
    "    dye_names=[n.encode() for n in dye_names],\n",
    "    buffer_name=buffer_name,\n",
    "    num_dyes = len(dye_names))\n",
    "\n",
    "d['identity'] = dict(\n",
    "    author=author,\n",
    "    author_affiliation=author_affiliation,\n",
    "    creator=creator,\n",
    "    creator_affiliation=creator_affiliation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validate the Photon-HDF5 structure\n",
    "\n",
    "Before writing to disk, we assure the file structure follows the Photon-HDF5 format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "phc.hdf5.assert_valid_photon_hdf5(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save to Photon-HDF5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phc.hdf5.save_photon_hdf5(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#d['_data_file'].close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save PicoQuant metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "h5file = d['_data_file']\n",
    "h5file.create_group('/', 'user')\n",
    "pq_group = h5file.create_group('/user', 'picoquant')\n",
    "for key in meta: \n",
    "    if np.size(meta[key]) > 0:\n",
    "        h5file.create_table(pq_group, key, obj=meta[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#h5file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print HDF5 file content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phc.hdf5.print_children(h5file, '/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phc.hdf5.print_children(h5file, '/user/picoquant')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "metadata = phc.hdf5.dict_from_group(h5file.root.user.picoquant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "h5file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Photon-HDF5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename = d['_data_file'].filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h5data = phc.hdf5.load_photon_hdf5(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phc.hdf5.dict_from_group(h5data.identity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phc.hdf5.dict_from_group(h5data.setup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pprint(phc.hdf5.dict_from_group(h5data.photon_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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