2_Finding_sleeping_range.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ee820b9c",
   "metadata": {},
   "source": [
    "# import + functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5058a84",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import csv\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from moviepy.editor import *\n",
    "\n",
    "\n",
    "def get_csv_speed_align_time(Tracking_sleep_data,align_path,save):\n",
    "    dlc_data =get_dlc_data(Tracking_sleep_data,'body_centre',True,0.9995)\n",
    "\n",
    "    all_distances = []\n",
    "    for data_ in dlc_data:\n",
    "        all_distances+=[get_distances(data_)]\n",
    "    distances = np.mean(all_distances,axis = 0)\n",
    "    align = pd.read_csv(align_path)\n",
    "    align = pd.read_csv(align_path)\n",
    "    Camera_EPhys_aligned= align['Camera_time_Ephys_Aligned'].values\n",
    "\n",
    "    # cut down to epoch i care about\n",
    "    if 'Post' in align_path:\n",
    "        distances = distances[align['index'][0]::]\n",
    "    else:\n",
    "        distances = distances[0:align['index'][-1]]\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2,figsize=(10, 4))\n",
    "\n",
    "    axs[0].plot(distances, color = 'blue')\n",
    "    axs[0].set_title('Distance')\n",
    "    if 'Post' in align_path:\n",
    "        axs[1].plot(dlc_data[0]['interped_x'][align['index'][0]::].values,color = 'red',markersize=1)\n",
    "        axs[1].plot(dlc_data[0]['interped_y'][align['index'][0]::].values,color = 'green',markersize=1)\n",
    "    else:\n",
    "        axs[1].plot(dlc_data[0]['interped_x'][0:align['index'][-1]].values,color = 'red',markersize=1)\n",
    "        axs[1].plot(dlc_data[0]['interped_y'][0:align['index'][-1]].values,color = 'green',markersize=1)\n",
    "    axs[1].set_title('x & y tracking values')\n",
    "        \n",
    "    df = pd.DataFrame({\"v\" : distances, \"Ephys\" : Camera_EPhys_aligned})\n",
    "    df.to_csv(save, index=False)    \n",
    "    return Camera_EPhys_aligned , distances\n",
    "\n",
    "def get_dlc_data(Tracking_sleep_data,name,interp,val):\n",
    "    # Load in all '.h5' files for a given folder:\n",
    "    TFiles_unsort = list_files(Tracking_sleep_data, 'h5')\n",
    "    for file in TFiles_unsort:\n",
    "        if 'ABOVE' in file:\n",
    "            h5_read=pd.read_hdf(Tracking_sleep_data+file)\n",
    "    # Access the head center      \n",
    "    scorer =  h5_read.columns.tolist()[0][0]\n",
    "\n",
    "    colum_headings = h5_read[scorer].columns\n",
    "    bodyparts = np.unique([item[0] for item in colum_headings])\n",
    "\n",
    "    output = []\n",
    "    for name in bodyparts:\n",
    "        print(name)\n",
    "        dat_ =  h5_read[scorer][name]\n",
    "        if interp:\n",
    "            dat_interped=clean_and_interpolate(dat_,val)\n",
    "        output +=[dat_interped]\n",
    "    return output\n",
    "\n",
    "def get_distances(Task_body_centre_2):\n",
    "    distance=[]\n",
    "    for i, x in enumerate(Task_body_centre_2['interped_x'].values):\n",
    "        if i== 0:\n",
    "            x_vector_old = Task_body_centre_2['interped_y'].values[i]\n",
    "            y_vector_old = Task_body_centre_2['interped_x'].values[i]\n",
    "        else:\n",
    "            x_vector_new = Task_body_centre_2['interped_y'].values[i]\n",
    "            y_vector_new = Task_body_centre_2['interped_x'].values[i]\n",
    "        \n",
    "            distance_x = (x_vector_new -x_vector_old)**2 \n",
    "            distance_y = (y_vector_new -y_vector_old)**2 \n",
    "        \n",
    "            distance.append(np.sqrt(distance_x + distance_y))\n",
    "            y_vector_old = y_vector_new\n",
    "            x_vector_old = x_vector_new\n",
    "    distance.append(np.sqrt(distance_x + distance_y))   # Repeat the last row to have the same size vector     \n",
    "    return distance\n",
    "\n",
    "def list_files(directory, extension):\n",
    "    return (f for f in os.listdir(directory) if f.endswith('.' + extension))\n",
    "\n",
    "def clean_and_interpolate(data,threshold):\n",
    "\n",
    "    bad_confidence_inds = np.where(data.likelihood.values<threshold)[0]\n",
    "    newx = data.x.values\n",
    "    newx[bad_confidence_inds] = 0\n",
    "    newy = data.y.values\n",
    "    newy[bad_confidence_inds] = 0\n",
    "\n",
    "    start_value_cleanup(newx)\n",
    "    interped_x = interp_0_coords(newx)\n",
    "\n",
    "    start_value_cleanup(newy)\n",
    "    interped_y = interp_0_coords(newy)\n",
    "    \n",
    "    data['interped_x'] = interped_x\n",
    "    data['interped_y'] = interped_y\n",
    "    \n",
    "    return data\n",
    "\n",
    "def start_value_cleanup(coords):\n",
    "    # This is for when the starting value of the coords == 0; interpolation will not work on these coords until the first 0 \n",
    "    #is changed. The 0 value is changed to the first non-zero value in the coords lists\n",
    "    for index, value in enumerate(coords):\n",
    "        working = 0\n",
    "        if value > 0:\n",
    "            start_value = value\n",
    "            start_index = index\n",
    "            working = 1\n",
    "            break\n",
    "    if working == 1:\n",
    "        for x in range(start_index):\n",
    "            coords[x] = start_value\n",
    "            \n",
    "def interp_0_coords(coords_list):\n",
    "    #coords_list is one if the outputs of the get_x_y_data = a list of co-ordinate points\n",
    "    for index, value in enumerate(coords_list):\n",
    "        if value == 0:\n",
    "            if coords_list[index-1] > 0:\n",
    "                value_before = coords_list[index-1]\n",
    "                interp_start_index = index-1\n",
    "                \n",
    "\n",
    "        if index < len(coords_list)-1:\n",
    "            if value ==0:\n",
    "                if coords_list[index+1] > 0:\n",
    "                    interp_end_index = index+1\n",
    "                    value_after = coords_list[index+1]\n",
    "                   \n",
    "\n",
    "                    #now code to interpolate over the values\n",
    "                    try:\n",
    "                        interp_diff_index = interp_end_index - interp_start_index\n",
    "                    except UnboundLocalError:\n",
    "#                         print('the first value in list is 0, use the function start_value_cleanup to fix')\n",
    "                        break\n",
    "                 \n",
    "\n",
    "                    new_values = np.linspace(value_before, value_after, interp_diff_index)\n",
    "                    #print(new_values)\n",
    "\n",
    "                    interp_index = interp_start_index+1\n",
    "                    for x in range(interp_diff_index):\n",
    "                        \n",
    "                        coords_list[interp_index] = new_values[x]\n",
    "                        interp_index +=1\n",
    "        if index == len(coords_list)-1:\n",
    "            if value ==0:\n",
    "                for x in range(30):\n",
    "                    coords_list[index-x] = coords_list[index-30]\n",
    "                    #print('')\n",
    "    print('function exiting')\n",
    "    return(coords_list)\n",
    "\n",
    "def find_nearest(array, value):\n",
    "    array = np.asarray(array)\n",
    "    idx = (np.abs(array - value)).argmin()\n",
    "    return array[idx]\n",
    "                  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "524bf2dc",
   "metadata": {},
   "source": [
    "# set paths "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "92a1728c",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = r'Z:\\projects\\sequence_squad\\revision_data\\organised_data\\animals\\\\'\n",
    "\n",
    "animal = \"seq006_implant1\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb547e03",
   "metadata": {},
   "source": [
    "# main loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fd79b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recording10_27-11-2024\n",
      "implant_centre\n",
      "function exiting\n",
      "function exiting\n",
      "mouse_centre\n",
      "function exiting\n",
      "function exiting\n",
      "no presleep?\n",
      "implant_centre\n",
      "function exiting\n",
      "function exiting\n",
      "mouse_centre\n",
      "function exiting\n",
      "function exiting\n",
      "no postsleep?\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'blarg' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 28\u001b[0m\n\u001b[0;32m     25\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m     26\u001b[0m         \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mno postsleep?\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 28\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mblarg\u001b[49m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'blarg' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_ = path+animal\n",
    "for recording in os.listdir(path_):\n",
    "    if not 'Store' in recording: # ignore ds store thing\n",
    "        print(recording)\n",
    "        current_path = os.path.join(path_,recording) + '\\\\'\n",
    "        output_path = os.path.join(path_,recording,'post_process_ppseq') + '\\\\' \n",
    "        \n",
    "        # presleep        \n",
    "        video_path = os.path.join(current_path, r\"video/videos//\")\n",
    "        Tracking_sleep_data = os.path.join(current_path, r\"video/tracking//\")\n",
    "        align_path = os.path.join(current_path, r\"behav_sync/1_pre_sleep//\") + \"Presleep_Ephys_Camera_sync.csv\"\n",
    "        \n",
    "        try:\n",
    "            Camera_EPhys_aligned, distances =get_csv_speed_align_time(Tracking_sleep_data,align_path,output_path + 'velocity_mice_1_presleep.csv')\n",
    "        except:\n",
    "            print('no presleep?')\n",
    "    \n",
    "        # post sleep\n",
    "        video_path = os.path.join(current_path, r\"video/videos//\")\n",
    "        Tracking_sleep_data = os.path.join(current_path, r\"video/tracking//\")\n",
    "        align_path = os.path.join(current_path, r\"behav_sync/3_post_sleep//\") + \"Postsleep_Ephys_Camera_sync.csv\"\n",
    "\n",
    "        try:\n",
    "            Camera_EPhys_aligned, distances =get_csv_speed_align_time(Tracking_sleep_data,align_path,output_path + 'velocity_mice_3_post_sleep.csv')       \n",
    "        except:\n",
    "            print('no postsleep?')\n",
    "            \n",
    "\n",
    "        \n",
    "        \n"
   ]
  }
 ],
 "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}