{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "64103f44",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "import shutil\n",
    "\n",
    "#from klusta.launch import klusta as klaunch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0ebece1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# session paths\n",
    "source  = '/home/sobolev/nevermind/Andrey/data'\n",
    "animal  = '006865'\n",
    "session = '006865_hippoSIT_2021-12-09_15-28-36'\n",
    "session_path = os.path.join(source, animal, session)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "97dd5d9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#session_path = 'Z:\\\\Andrey\\\\sandbox'\n",
    "#ss_path = os.path.join(session_path, 'ss2')\n",
    "#animal  = 'GB049'\n",
    "#session = '2021-07-13T15-12-29'\n",
    "#filebase = '%s_%s' % (animal, session)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07710b0c",
   "metadata": {},
   "source": [
    "## Copy Neurosuite settings files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a308cdf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "settings_source = os.path.join(source, animal)\n",
    "ns_settings_file = os.path.join(settings_source, 'default.xml')\n",
    "kwik_params_file = os.path.join(settings_source, 'default.prm')\n",
    "kwik_probes_file = os.path.join(settings_source, 'default.prb')\n",
    "\n",
    "shutil.copy(ns_settings_file, os.path.join(session_path, '%s.xml' % session))\n",
    "shutil.copy(kwik_params_file, os.path.join(session_path, '%s.prm' % session))\n",
    "shutil.copy(kwik_probes_file, os.path.join(session_path, '%s.prb' % session))\n",
    "\n",
    "with open(os.path.join(session_path, '%s.prm' % session), 'r+') as f:\n",
    "    content = f.read()\n",
    "    f.seek(0, 0)\n",
    "    lines = \"experiment_name = '%s'\\nprb_file = '%s.prb'\\n \\n\" % (session, session)\n",
    "    f.write(lines.rstrip('\\r\\n') + '\\n' + content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9e743ef",
   "metadata": {},
   "source": [
    "## Conversion of MCS .h5 to .dat file\n",
    "\n",
    "http://neurosuite.sourceforge.net/formats.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3397fb7b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channels: 32, samples: 48008115, type: int32\n",
      "100 percent complete\r"
     ]
    }
   ],
   "source": [
    "crossref = None\n",
    "#crossref = 11\n",
    "\n",
    "MCS_h5_name = [x for x in os.listdir(session_path) if x.startswith('Andrey') and x.endswith('.h5')][0]\n",
    "chunk_size = 25000 # 1 second at sampling rate 25kHz\n",
    "dest_file = os.path.join(session_path, '%s.dat' % session)\n",
    "if crossref is not None:\n",
    "    dest_file = os.path.join(session_path, '%s.cf.dat' % session)\n",
    "if not os.path.exists(session_path):\n",
    "    os.makedirs(session_path)\n",
    "if os.path.exists(dest_file):\n",
    "    os.remove(dest_file)\n",
    "\n",
    "with h5py.File(os.path.join(session_path, MCS_h5_name), 'r') as f:\n",
    "    #ds = f['Data']['Recording_0']['AnalogStream']['Stream_1']['ChannelData']\n",
    "    ds = f['Data']['Recording_0']['AnalogStream']['Stream_2']['ChannelData']\n",
    "    print(\"Channels: %s, samples: %s, type: %s\" % (ds.shape[0], ds.shape[1], ds.dtype))\n",
    "    \n",
    "    chunks_no = int(np.ceil(ds.shape[1]/chunk_size))\n",
    "    #chunks_no = 60\n",
    "    for i in range(chunks_no):\n",
    "        idx = i * chunk_size\n",
    "        testds = ds[:, idx:idx + chunk_size]\n",
    "        data = np.array(ds[:, idx:idx + chunk_size]).astype(np.int16)  # int16 is correct\n",
    "        if crossref is not None:\n",
    "            to_sub = np.array(data[crossref])\n",
    "            data -= to_sub\n",
    "        with open(dest_file, 'ab') as f:\n",
    "            data.T.flatten().tofile(f)\n",
    "            \n",
    "        print(\"%s percent complete\\r\" % str(round(100*(i + 1)/float(chunks_no))), end='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16b5ce0c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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