{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# File to be converted.\n",
    "input_filename = 'data/0023uLRpitc_NTP_20dT_0.5GndCl.sm'\n",
    "output_path = None  # Use None for saving in the same folder as the SM file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Default configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Used when the metadata does not contain the field identity\n",
    "default_identity = dict(\n",
    "    author='Weiss Lab',\n",
    "    author_affiliation='UCLA',\n",
    "    creator='Antonino Ingargiola',\n",
    "    creator_affiliation='UCLA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Used when the metadata does not contain the field setup\n",
    "default_setup = dict(\n",
    "    num_pixels = 2,\n",
    "    num_spots = 1,\n",
    "    num_spectral_ch = 2,\n",
    "    num_polarization_ch = 1,\n",
    "    num_split_ch = 1,\n",
    "    modulated_excitation = True,\n",
    "    lifetime = False,\n",
    "    excitation_cw = [True, True],\n",
    "    excitation_alternated=[True, True],\n",
    "    excitation_wavelengths=[532e-9, 635e-9],\n",
    "    detection_wavelengths=[580e-9, 660e-9]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Used when the metadata does not contain the field measurement_specs\n",
    "def_measurement_specs = dict(\n",
    "    measurement_type = 'smFRET-usALEX',\n",
    "    alex_period = 4000,\n",
    "    alex_offset = 700,\n",
    "    alex_excitation_period1 = (2180, 3900),\n",
    "    alex_excitation_period2 = (200, 1800),\n",
    "    detectors_specs = dict(spectral_ch1 = [0],\n",
    "                           spectral_ch2 = [1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import yaml\n",
    "\n",
    "import phconvert as phc\n",
    "print('phconvert version: ' + phc.__version__)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resolve paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_filename = Path(input_filename)\n",
    "input_filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert input_filename.is_file(), 'Input SM file not found. Check the file name.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if output_path is None:\n",
    "    output_path = input_filename.parent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_filename = Path(output_path, input_filename.stem + '.hdf5')\n",
    "out_filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the output data folder if necessary\n",
    "#out_filename.parent.mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_filename = input_filename.with_suffix('.yml')\n",
    "assert meta_filename.is_file(), 'Metadata YAML file not found.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(str(meta_filename)) as f:\n",
    "    metadata = yaml.load(f)\n",
    "metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocess timestamps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_with_defaults(input_dict, default_dict):\n",
    "    for k in default_dict:\n",
    "        if k not in input_dict:\n",
    "            input_dict[k] = default_dict[k]\n",
    "    return input_dict\n",
    "            \n",
    "\n",
    "def sm_load_photon_data(filename, metadata, def_measurement_specs):\n",
    "    \"\"\"Load photon_data from a .sm us-ALEX file into a metadata dict.\n",
    "    \"\"\"\n",
    "    metadata = metadata.copy()\n",
    "    timestamps, detectors, labels = phc.smreader.load_sm(str(filename),\n",
    "                                                         return_labels=True)\n",
    "    measurement_specs = metadata.pop('measurement_specs', def_measurement_specs)\n",
    "    update_with_defaults(measurement_specs, def_measurement_specs)\n",
    "            \n",
    "    photon_data = dict(\n",
    "        timestamps = timestamps,\n",
    "        timestamps_specs = dict(timestamps_unit=12.5e-9),\n",
    "        detectors = detectors,\n",
    "        measurement_specs = measurement_specs)\n",
    "\n",
    "    acquisition_duration = (timestamps[-1] - timestamps[0]) * 12.5e-9\n",
    "    \n",
    "    provenance = dict(\n",
    "        filename=str(filename), \n",
    "        software='LabVIEW Data Acquisition usALEX')\n",
    "    \n",
    "    metadata.update(\n",
    "        _filename = str(filename),\n",
    "        acquisition_duration = round(acquisition_duration),\n",
    "        photon_data = photon_data,\n",
    "        provenance = provenance)\n",
    "    return metadata\n",
    "\n",
    "\n",
    "def fill_with_defaults(metadata, default_setup, default_identity):\n",
    "    \"\"\"Fill all missing values in metadata with defaults.\"\"\"\n",
    "    setup = metadata.get('setup', default_setup)\n",
    "    metadata['setup'] = update_with_defaults(setup, default_setup)\n",
    "\n",
    "    identity = metadata.get('identity', default_identity)\n",
    "    metadata['identity'] = update_with_defaults(identity, default_identity)    \n",
    "    \n",
    "    sample = metadata['sample']\n",
    "    sample['num_dyes'] = len(sample['dye_names'].split(','))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = sm_load_photon_data(input_filename, metadata, def_measurement_specs)\n",
    "fill_with_defaults(data, default_setup, default_identity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "phc.plotter.alternation_hist(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Create Photon-HDF5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "phc.hdf5.save_photon_hdf5(data, h5_fname=str(out_filename), overwrite=True, close=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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