synchronization_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spikeinterface.extractors as se\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import spikeinterface as si\n",
    "from readTrodesExtractedDataFile3 import readTrodesExtractedDataFile\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "rec_folder = r\"D:\\cl\\ephys\\latency_20240904_144725.rec\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dio_folders(rec_folder):\n",
    "    # get the folder under rec_folder that ends with \".DIO\"\n",
    "    rec_folder = Path(rec_folder)\n",
    "    dio_folders = [f for f in rec_folder.iterdir() if f.is_dir() and f.name.endswith(\".DIO\")]\n",
    "    return dio_folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[WindowsPath('D:/cl/ephys/latency_20240904_144725.rec/latency_20240904_144725.DIO'),\n",
       " WindowsPath('D:/cl/ephys/latency_20240904_144725.rec/latency_20240904_144725.part2.DIO')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dio_folders = get_dio_folders(rec_folder)\n",
    "# reorder the dio_folders according to the name\n",
    "dio_folders = sorted(dio_folders, key=lambda x: x.name)\n",
    "dio_folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_DIN(DIO_folder, channel_id):\n",
    "    # get the file name\n",
    "    DIO_folder = Path(DIO_folder)\n",
    "\n",
    "    # file name is end with Din1.dat\n",
    "    din_files = [f for f in DIO_folder.iterdir() if f.is_file() and f.name.endswith(f\"Din{channel_id}.dat\")]\n",
    "    if len(din_files) == 0:\n",
    "        # return error if no file found\n",
    "        return None\n",
    "    \n",
    "    din_file = din_files[0]\n",
    "    # read the file\n",
    "    time = readTrodesExtractedDataFile(din_file)['data']['time']\n",
    "    state = readTrodesExtractedDataFile(din_file)['data']['state']\n",
    "    return time, state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def concatenate_din_data(dio_folders, channel_id: int):\n",
    "    # initialize the din_data\n",
    "    time, state = extract_DIN(dio_folders[0], channel_id)\n",
    "\n",
    "    if len(dio_folders) == 1:\n",
    "        return time, state\n",
    "    \n",
    "    \n",
    "    for i in range(1, len(dio_folders)):\n",
    "        time_, state_ = extract_DIN(dio_folders[i], channel_id)\n",
    "        # if the end of the last state is the same as the start of the current state, remove the first element of the current state and time\n",
    "        if state[-1] == state_[0]:\n",
    "            state_ = state_[1:]\n",
    "            time_ = time_[1:]\n",
    "\n",
    "        time = np.concatenate((time, time_))\n",
    "        state = np.concatenate((state, state_))\n",
    "    return time, state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\\\\10.129.151.108\\xieluanlabs\\xl_cl\\code\\readTrodesExtractedDataFile3.py:64: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  return np.dtype(typearr)\n"
     ]
    }
   ],
   "source": [
    "strobe_out_time, strobe_out_state = concatenate_din_data(dio_folders, 1)\n",
    "indicator_time, indicator_state = concatenate_din_data(dio_folders, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def complete_time_series(indicator_time, indicator_state, time_series):\n",
    "    # Ensure indicator_time and indicator_state are numpy arrays\n",
    "    indicator_time = np.array(indicator_time)\n",
    "    indicator_state = np.array(indicator_state)\n",
    "\n",
    "    # Initialize the output array to store the state for each time in time_series\n",
    "    completed_state = np.zeros_like(time_series, dtype=int)\n",
    "\n",
    "    # Iterate through the time series and assign the state based on the indicator change points\n",
    "    for i, t in enumerate(time_series):\n",
    "        # Find the last indicator_time that is less than or equal to the current time\n",
    "        idx = np.searchsorted(indicator_time, t, side='right') - 1\n",
    "        \n",
    "        if idx >= 0:\n",
    "            # Assign the corresponding state\n",
    "            completed_state[i] = indicator_state[idx]\n",
    "\n",
    "    return completed_state\n",
    "\n",
    "time_series = np.linspace(0, strobe_out_time[-1], strobe_out_time[-1])\n",
    "\n",
    "completed_state = complete_time_series(indicator_time, indicator_state, time_series)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23acfca7750>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = time_series/30000\n",
    "t_range = (146.55,146.60)\n",
    "# t_range = (146, 160)\n",
    "# find the t index in the range\n",
    "t_idx = np.where((t>=t_range[0]) & (t<=t_range[1]))[0]\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(t[t_idx], completed_state[t_idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract rising and falling edges time stamps\n",
    "indicator_t = indicator_time/30000\n",
    "rising_edges = indicator_t[np.where(indicator_state==1)]\n",
    "falling_edges = indicator_t[np.where(indicator_state==0)][1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete all the fallings happens in 0.05s after a rising, and delete its corresponding next rising\n",
    "falling_to_delete = np.where(falling_edges - rising_edges < 0.02)[0]\n",
    "falling_edges = np.delete(falling_edges, falling_to_delete)\n",
    "rising_edges = np.delete(rising_edges, falling_to_delete+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23acfdebc10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the rising to falling time and falling to rising time\n",
    "rising_to_falling_time = falling_edges - rising_edges\n",
    "falling_to_rising_time = rising_edges[1:] - falling_edges[:-1]\n",
    "\n",
    "# plot the hist separately\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.hist(rising_to_falling_time, bins=10, alpha=0.5, label='rising to falling')\n",
    "plt.hist(falling_to_rising_time, bins=10, alpha=0.5, label='falling to rising')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,  33., 198.]),\n",
       " array([0.079     , 0.11326333, 0.14752667, 0.18179   , 0.21605333,\n",
       "        0.25031667, 0.28458   , 0.31884333, 0.35310667, 0.38737   ,\n",
       "        0.42163333]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "falling_to_falling_time = falling_edges[1:] - falling_edges[:-1]\n",
    "plt.hist(falling_to_falling_time, bins=10, alpha=0.5, label='falling to falling')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}