{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e60c7cd0",
   "metadata": {},
   "source": [
    "# Writing sample nix file for one session of spike sorted data for Monkey N"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0643592d",
   "metadata": {},
   "source": [
    "## Importing all the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3093b19",
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib\n",
    "\n",
    "import numpy as np\n",
    "import quantities as pq\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import neo\n",
    "import nixio as nix\n",
    "import elephant\n",
    "from utils.plotting import Plotter\n",
    "from utils.notebook import print_stats, print_metadata_table\n",
    "import datetime\n",
    "import odml"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc84b92b",
   "metadata": {},
   "source": [
    "## Reading and exploring the blackrock data from .nev file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4fbd8fec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#url_path = 'https://gin.g-node.org/INT/multielectrode_grasp/raw/master/datasets/i140703-001-03.nev'\n",
    "#local_path = './i140703-001-03.nev'\n",
    "#urllib.request.urlretrieve(url_path, local_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e54b698d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BlackrockIO: i140703-001-03.nev\n",
      "nb_block: 1\n",
      "nb_segment:  [1]\n",
      "signal_channels: []\n",
      "unit_channels: [ch1#0, ch1#1, ch1#2, ch2#0 ... ch95#2 ch95#3 ch96#0 ch96#1]\n",
      "event_channels: [digital_input_port, serial_input_port, comments]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#create a reader\n",
    "reader = neo.io.BlackrockIO(filename=\"i140703-001-03.nev\")\n",
    "print(reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cbe77c63",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n"
     ]
    }
   ],
   "source": [
    "blks = reader.read(lazy=False)[0]\n",
    "#Initializing an array to store all the spikestrains in each segment\n",
    "train_array = []\n",
    "for seg in blks.segments:\n",
    "    #Extracting only the magnitde of the spike train\n",
    "    train = [st.rescale('s').magnitude for st in seg.spiketrains]\n",
    "    #Stacking all the spiketrains,such that,each index would give st corresponding to each unit channel\n",
    "    train_array = np.hstack((train_array,train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a71fad6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nb_block 1\n",
      "nb_segment [1]\n",
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      " ('ch1#1', 'Unit 1001', 'uV', 0.25, 0., 10, 30000.)\n",
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      " ('ch2#0', 'Unit 2000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch2#1', 'Unit 2001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch2#2', 'Unit 2002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch3#0', 'Unit 3000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch3#1', 'Unit 3001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch3#2', 'Unit 3002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch4#0', 'Unit 4000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch4#1', 'Unit 4001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch4#2', 'Unit 4002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch5#0', 'Unit 5000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch5#1', 'Unit 5001', 'uV', 0.25, 0., 10, 30000.)\n",
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      " ('ch28#0', 'Unit 28000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch28#1', 'Unit 28001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch28#2', 'Unit 28002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch29#0', 'Unit 29000', 'uV', 0.25, 0., 10, 30000.)\n",
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      " ('ch33#2', 'Unit 33002', 'uV', 0.25, 0., 10, 30000.)\n",
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      " ('ch63#2', 'Unit 63002', 'uV', 0.25, 0., 10, 30000.)\n",
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      " ('ch72#1', 'Unit 72001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch73#0', 'Unit 73000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch73#1', 'Unit 73001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch73#2', 'Unit 73002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch74#0', 'Unit 74000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch74#1', 'Unit 74001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch74#2', 'Unit 74002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch75#0', 'Unit 75000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch75#1', 'Unit 75001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch75#2', 'Unit 75002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch76#0', 'Unit 76000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch76#1', 'Unit 76001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch77#0', 'Unit 77000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch77#1', 'Unit 77001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch77#2', 'Unit 77002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch78#0', 'Unit 78000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch78#1', 'Unit 78001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch78#2', 'Unit 78002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch79#0', 'Unit 79000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch79#1', 'Unit 79001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch79#2', 'Unit 79002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch79#3', 'Unit 79003', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch79#4', 'Unit 79004', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch80#0', 'Unit 80000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch80#1', 'Unit 80001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch80#2', 'Unit 80002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch81#0', 'Unit 81000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch81#1', 'Unit 81001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch81#2', 'Unit 81002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch82#0', 'Unit 82000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch82#1', 'Unit 82001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch83#0', 'Unit 83000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch83#1', 'Unit 83001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch83#2', 'Unit 83002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch84#0', 'Unit 84000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch84#1', 'Unit 84001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch85#0', 'Unit 85000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch85#1', 'Unit 85001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch85#2', 'Unit 85002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch86#0', 'Unit 86000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch86#1', 'Unit 86001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch86#2', 'Unit 86002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch86#3', 'Unit 86003', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch87#0', 'Unit 87000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch87#1', 'Unit 87001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch88#0', 'Unit 88000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch88#1', 'Unit 88001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch89#0', 'Unit 89000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch89#1', 'Unit 89001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch90#0', 'Unit 90000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch90#1', 'Unit 90001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch91#0', 'Unit 91000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch91#1', 'Unit 91001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch91#2', 'Unit 91002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch91#3', 'Unit 91003', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch92#0', 'Unit 92000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch92#1', 'Unit 92001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch92#2', 'Unit 92002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch93#0', 'Unit 93000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch93#1', 'Unit 93001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch93#2', 'Unit 93002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch94#0', 'Unit 94000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch94#1', 'Unit 94001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch94#2', 'Unit 94002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch95#0', 'Unit 95000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch95#1', 'Unit 95001', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch95#2', 'Unit 95002', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch95#3', 'Unit 95003', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch96#0', 'Unit 96000', 'uV', 0.25, 0., 10, 30000.)\n",
      " ('ch96#1', 'Unit 96001', 'uV', 0.25, 0., 10, 30000.)]\n",
      "event_channels [('digital_input_port', '', b'event') ('serial_input_port', '', b'event')\n",
      " ('comments', '', b'event')]\n"
     ]
    }
   ],
   "source": [
    "#Printing out entire reader dictionary to understand the structure of the file\n",
    "for key_var , value_var in reader.header.items():\n",
    "    print(key_var,value_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4c88a1d8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('name', '<U64'), ('id', '<U64'), ('wf_units', '<U64'), ('wf_gain', '<f8'), ('wf_offset', '<f8'), ('wf_left_sweep', '<i8'), ('wf_sampling_rate', '<f8')]\n"
     ]
    }
   ],
   "source": [
    "#Extracting the values for key of unit channels and printing the data types\n",
    "unit_channel_des = reader.header['unit_channels']\n",
    "print(unit_channel_des.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9c169484",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining a function to extact fields corresponding to the field name from a ndarray\n",
    "def fields_view(arr, fields):\n",
    "    dtype2 = np.dtype({name:arr.dtype.fields[name] for name in fields})\n",
    "    return np.ndarray(arr.shape, dtype2, arr, 0, arr.strides)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e0e52464",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extracting all the fields of unit channel key using fields_view() function\n",
    "channel_name = fields_view(unit_channel_des, [\"name\"])\n",
    "channel_id = fields_view(unit_channel_des, [\"id\"])\n",
    "channel_wf_units = fields_view(unit_channel_des, [\"wf_units\"])\n",
    "channel_wf_gain = fields_view(unit_channel_des, [\"wf_gain\"])\n",
    "channel_wf_offset = fields_view(unit_channel_des, [\"wf_offset\"])\n",
    "channel_wf_left_sweep = fields_view(unit_channel_des, [\"wf_left_sweep\"])\n",
    "channel_wf_sampling_rate = fields_view(unit_channel_des, [\"wf_sampling_rate\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e7c6a76",
   "metadata": {},
   "source": [
    "## Creating a nix file and adding some Data available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a3c93b2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Opening a nix file\n",
    "nixfile = nix.File.open(\"test_nix.h5\", nix.FileMode.Overwrite)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d586a6a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating blocks corresponding to each key of the reader dictionary of .nev file\n",
    "block = nixfile.create_block(\"nb_block\", \"nix.block\")\n",
    "block_seg = nixfile.create_block(\"nb_segment\",\"nix.segment\")\n",
    "block_sig = nixfile.create_block(\"signal_channels\", \"nix.signal_channels\")\n",
    "block_unit = nixfile.create_block(\"unit_channels\", \"nix.unit_channels\")\n",
    "block_event = nixfile.create_block(\"event_channels\", \"nix.event_channels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9997df53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of units : 271\n"
     ]
    }
   ],
   "source": [
    "#Counting and printing the total number of unit channels\n",
    "total_units = reader.unit_channels_count()\n",
    "print(\"Total number of units :\",total_units)\n",
    "\n",
    "#Iterating over each channel,to populate the unit_channels block with available information\n",
    "for unit_index in range(total_units):\n",
    "    \n",
    "    channel_name_str = str((channel_id[unit_index])[0])\n",
    "    data = block_unit.create_data_array(channel_name_str,\"session.unit_channel\",data=[])\n",
    "    \n",
    "    data.wf_units = str((channel_wf_units[unit_index])[0])\n",
    "    data.wf_gain = str((channel_wf_gain[unit_index])[0])\n",
    "    data.wf_offset = str((channel_wf_offset[unit_index])[0])\n",
    "    data.wf_left_sweep = str((channel_wf_left_sweep[unit_index])[0])\n",
    "    data.wf_sampling_rate = str((channel_wf_sampling_rate[unit_index])[0])    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "090a0dea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Blocks                                             (05)\n",
      "\ttype: nix.block                            (01)\n",
      "\ttype: nix.unit_channels                    (01)\n",
      "\ttype: nix.signal_channels                  (01)\n",
      "\ttype: nix.segment                          (01)\n",
      "\ttype: nix.event_channels                   (01)\n"
     ]
    }
   ],
   "source": [
    "#Printing all the blocks types and how many are there of each type\n",
    "print_stats(nixfile.blocks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e166cef6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DataArrays                                         (271)\n",
      "\ttype: session.unit_channel                 (271)\n"
     ]
    }
   ],
   "source": [
    "#Printing the type and number of data array if available in each block\n",
    "print_stats(block.data_arrays)\n",
    "print_stats(block_seg.data_arrays)\n",
    "print_stats(block_sig.data_arrays)\n",
    "print_stats(block_unit.data_arrays)\n",
    "print_stats(block_event.data_arrays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd41c8ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Iterating over each channel,to populate the segment block with spike train data\n",
    "for unit_index in range(total_units):\n",
    "    channel_name_str = str((channel_id[unit_index])[0])\n",
    "    data = block_seg.create_data_array(channel_name_str,\"segment.spike_train\",data=train_array[unit_index])\n",
    "    data.label = \"Spike_Train\"\n",
    "    data.unit = 's'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0957d9a",
   "metadata": {},
   "source": [
    "## Adding some metadata to the nix file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef411489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'avail_file_set': ['nev'],\n",
       " 'avail_nsx': [],\n",
       " 'avail_nev': True,\n",
       " 'rec_pauses': False,\n",
       " 'file_datetime': None,\n",
       " 'rec_datetime': datetime.datetime(2014, 7, 3, 10, 40, 59, 288),\n",
       " 'index': None,\n",
       " 'name': 'Blackrock Data Block',\n",
       " 'description': 'Block of data from Blackrock file set.',\n",
       " 'file_origin': 'i140703-001-03.nev'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extracting some metadata in the form of attributes using elephant\n",
    "elephant.neo_tools.extract_neo_attributes(blks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ae77ae2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding the metadata associated with the nb_block\n",
    "attribute_data = nixfile.create_section(\"nb_block\", \"nix.block\")\n",
    "attribute_data[\"Available_File_Set\"] = \".nev\"\n",
    "attribute_data[\"Recording_Date_Time\"] = str(datetime.datetime(2014, 7, 3, 10, 40, 59, 288))\n",
    "attribute_data[\"Name\"] = \"Blackrock Data Block\"\n",
    "attribute_data[\"Description\"] = \"Block of data from Blackrock file set.\"\n",
    "attribute_data[\"File_Origin\"] = \"i140703-001-03.nev\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "65b1a1c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "warning[None:17:<{http://www.w3.org/1999/xsl/transform}stylesheet>]: Invalid element <{http://www.w3.org/1999/xsl/transform}stylesheet> inside <odML> tag\n",
      " (line 17)\n",
      "warning[None:17:<{http://www.w3.org/1999/xsl/transform}stylesheet>]: Invalid element <{http://www.w3.org/1999/xsl/transform}stylesheet> in odML document section <odML>  (line 17)\n",
      "UserWarning: The loaded Document contains unresolved issues. Run the Documents 'validate' method to access them.\n",
      "Validation found 0 errors and 3 warnings in 0 Sections and 3 Properties.\n"
     ]
    }
   ],
   "source": [
    "#Loading the associated odml file for the session\n",
    "odml_load = odml.load(\"i140703-001.odml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "14476550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Document 0.1 {author = Lyuba Zehl, 8 sections}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odml_load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0575dfa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Lyuba Zehl [0.1] 2017-07-14, sections: 8, repository: None]\n",
      "   Setup [setup]\n",
      "      |- Location: ['Inst. de Neurosciences de la Timone (I ... Aix Marseille Univ., Marseille, France']\n",
      "      |- DAQSystem: ['Cerebus']\n",
      "     Apparatus [setup]\n",
      "       [...]\n",
      "   Subject [subject]\n",
      "      |- Species: ['Macaca mulatta']\n",
      "      |- TrivialName: ['Rhesus monkey']\n",
      "      |- GivenName: ['monkey_N']\n",
      "      |- Identifier: ['i']\n",
      "      |- Gender: ['male']\n",
      "      |- Birthday: [datetime.date(2008, 5, 17)]\n",
      "      |- ActiveHand: ['left']\n",
      "      |- Disabilities: ['-']\n",
      "      |- Character: ['Calm, but overall not really motivated. Less attentive.']\n",
      "     Training [subject/preparation]\n",
      "       [...]\n",
      "     ArrayImplant [subject/preparation]\n",
      "       [...]\n",
      "   Project [project]\n",
      "      |- Name: ['reach-to-grasp']\n",
      "      |- Type: ['electrophysiology']\n",
      "      |- Subtype: ['motor behavior']\n",
      "      |- Collaborators: ['Inst. of Neuroscience and Medicine (INM- ... Marseille Univ.', ' Marseille', ' France']\n",
      "     TaskDesigns [project]\n",
      "       [...]\n",
      "     ConditionCodes [codes]\n",
      "       [...]\n",
      "     DigitalEventLabelCodes [codes]\n",
      "       [...]\n",
      "     TrialSequenceEvents [codes]\n",
      "       [...]\n",
      "     PerformanceCodes [codes]\n",
      "       [...]\n",
      "     TrialSettings [setup/control]\n",
      "       [...]\n",
      "     TargetObjectSettings [setup/control]\n",
      "       [...]\n",
      "   Cerebus [setup/daq]\n",
      "      |- Owner: ['RIKEN-BSI, Japan']\n",
      "      |- Manufacturer: ['Blackrock Microsystems']\n",
      "     NeuralSignalProcessor [setup/daq/hardware]\n",
      "       [...]\n",
      "     NeuralSignalAmplifier [setup/daq/hardware]\n",
      "       [...]\n",
      "     ControlComputer [setup/daq/software]\n",
      "       [...]\n",
      "   UtahArray [setup/daq/hardware]\n",
      "      |- Owner: ['RIKEN-BSI, Japan']\n",
      "      |- SerialNo: ['8596-001139']\n",
      "      |- Manufacturer: ['Blackrock Microsystems']\n",
      "      |- WiresMaterial: ['Pt/Au']\n",
      "      |- WiresDiameter: [25.0]um\n",
      "      |- WiresLength: [40.0]mm\n",
      "     Connector [setup/daq/hardware]\n",
      "       [...]\n",
      "     Array [setup/daq/hardware]\n",
      "       [...]\n",
      "   Headstage [setup/daq/hardware]\n",
      "      |- Model: ['PatientCable']\n",
      "      |- Type: ['analog']\n",
      "      |- Gain: [1]\n",
      "      |- InFrom: ['UtahArray']\n",
      "      |- OutTo: ['NeuralSignalAmplifier']\n",
      "      |- StimSwitch: [True]\n",
      "   Recording [recording]\n",
      "      |- RecordingDay: ['i140703']\n",
      "      |- Recording: ['i140703-001']\n",
      "      |- Date: [datetime.date(2014, 7, 3)]\n",
      "      |- Weekday: ['Thursday']\n",
      "      |- Time: [datetime.time(10, 40, 59)]\n",
      "      |- Duration: [1003]s\n",
      "      |- IsSpikeSorted: [True]\n",
      "      |- TaskType: ['TwoCues']\n",
      "      |- Noisy: [True]\n",
      "      |- Comment: ['-']\n",
      "     TaskSettings [recording/task]\n",
      "       [...]\n",
      "   PreProcessing [preprocessing]\n",
      "      |- LFPBands: ['HFC', 'LFC', 'IFC']\n",
      "     HFC [subject/preparation]\n",
      "       [...]\n",
      "     LFC [subject/preparation]\n",
      "       [...]\n",
      "     IFC [subject/preparation]\n",
      "       [...]\n",
      "     OfflineSpikeSorting [subject/preparation]\n",
      "       [...]\n"
     ]
    }
   ],
   "source": [
    "#Printing all the the sections and properties from the odml file using pprint\n",
    "odml_load.pprint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ea0fee31",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding some metadata manually and associating it with the nb_segment block\n",
    "Recording_metadata = nixfile.create_section(\"nb_segment\",\"nix.segment\")\n",
    "Recording_metadata[\"RecordingDay\"] = \"i140703\"\n",
    "Recording_metadata[\"Recording\"] = \"i140703-001\"\n",
    "Recording_metadata[\"Date\"] = str(datetime.date(2014, 7, 3))\n",
    "Recording_metadata[\"Weekday\"] = \"Thursday\"\n",
    "Recording_metadata[\"Duration\"] = \"1003_seconds\"\n",
    "Recording_metadata[\"IsSpikeSorted\"] = \"True\"\n",
    "Recording_metadata[\"TaskType\"] = \"TwoCues\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "36f84601",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding source for the data in the segment block\n",
    "source = block_seg.create_source(\"monkey_N\", \"nix.experimental_subject\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7331b978",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding some subject metadata\n",
    "subject = Recording_metadata.create_section(\"monkey_N\", \"nix.experimental_subject\")\n",
    "subject[\"Species\"] = \"Macaca mulatta\"\n",
    "subject[\"TrivialName\"] = \"Rhesus monkey\"\n",
    "subject[\"Identifier\"] = 'i'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6d32cc3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "nixfile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e5deba5",
   "metadata": {},
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