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   "cell_type": "markdown",
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   "source": [
    "# Convert pt3 files to Photon-HDF5\n",
    "\n",
    "\n",
    "# Prepare the data file\n",
    "\n",
    "\n",
    "Before starting, you need to get a data file to be converted to Photon-HDF5.\n",
    "You can use one of our example data files available\n",
    "[on figshare](https://figshare.com/articles/data_files_for_phconvert/5421565).\n",
    "\n",
    "Specify the input data file in the following cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'data/DNA_FRET_0.5nM.pt3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "try: \n",
    "    with open(filename): pass\n",
    "    print('Data file found, you can proceed.')\n",
    "except IOError:\n",
    "    print('ATTENTION: Data file not found, please check the filename.\\n'\n",
    "          '           (current value \"%s\")' % filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import phconvert as phc\n",
    "print('phconvert version: ' + phc.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d, meta = phc.loader.nsalex_pq(filename,\n",
    "                               donor = 2,\n",
    "                               acceptor = 1,\n",
    "                               alex_period_donor = (0, 2000),\n",
    "                               alex_period_acceptor = (2000, 3200),\n",
    "                               excitation_wavelengths = (470e-9, 635e-9),\n",
    "                               detection_wavelengths = (525e-9, 690e-9),\n",
    "                               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta['hardware_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "detectors = d['photon_data']['detectors']\n",
    "\n",
    "print(\"Detector    Counts\")\n",
    "print(\"--------   --------\")\n",
    "for det, count in zip(*np.unique(detectors, return_counts=True)):\n",
    "    print(\"%8d   %8d\" % (det, count))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove the overflow counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nanotimes = d['photon_data']['nanotimes']\n",
    "detectors = d['photon_data']['detectors']\n",
    "timestamps = d['photon_data']['timestamps']\n",
    "\n",
    "not_overflow = d['photon_data']['nanotimes'] != 0\n",
    "\n",
    "detectors = detectors[not_overflow]\n",
    "timestamps = timestamps[not_overflow]\n",
    "nanotimes = nanotimes[not_overflow]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Detector    Counts\")\n",
    "print(\"--------   --------\")\n",
    "for det, count in zip(*np.unique(detectors, return_counts=True)):\n",
    "    print(\"%8d   %8d\" % (det, count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d['photon_data']['nanotimes'] = nanotimes\n",
    "d['photon_data']['detectors'] = detectors\n",
    "d['photon_data']['timestamps'] = timestamps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "phc.plotter.alternation_hist(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "author = 'Biswajit'\n",
    "author_affiliation = 'Leiden University'\n",
    "description = 'A demonstrative pt3 data file.'\n",
    "sample_name = 'Copper Azurin in 1mM Ascorbate'\n",
    "dye_names = 'ATTO655'\n",
    "buffer_name = 'HEPES pH7 with 100 mM NaCl'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add meta data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d['description'] = description\n",
    "\n",
    "d['sample'] = dict(\n",
    "    sample_name=sample_name,\n",
    "    dye_names=dye_names,\n",
    "    buffer_name=buffer_name,\n",
    "    num_dyes = len(dye_names.split(',')))\n",
    "\n",
    "d['identity'] = dict(\n",
    "    author=author,\n",
    "    author_affiliation=author_affiliation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove some empty groups that may cause errors on saving\n",
    "_ = meta.pop('dispcurve', None)\n",
    "_ = meta.pop('imghdr', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d['user'] = {'picoquant': meta}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save to Photon-HDF5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "phc.hdf5.save_photon_hdf5(d, overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Photon-HDF5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = d['_data_file'].filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "h5data = phc.hdf5.load_photon_hdf5(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "phc.hdf5.dict_from_group(h5data.identity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "phc.hdf5.dict_from_group(h5data.setup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pprint(phc.hdf5.dict_from_group(h5data.photon_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "h5data._v_file.close()"
   ]
  }
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