{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your allensdk version is: 2.15.2\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import math\n",
    "import numpy as np\n",
    "import scipy.signal as signal\n",
    "import scipy.fft as fft\n",
    "import pickle\n",
    "\n",
    "from allensdk.brain_observatory.ecephys.ecephys_project_cache import EcephysProjectCache\n",
    "from allensdk.brain_observatory.ecephys.ecephys_session import (\n",
    "    EcephysSession, \n",
    "    removed_unused_stimulus_presentation_columns\n",
    ")\n",
    "from allensdk.brain_observatory.ecephys.visualization import plot_mean_waveforms, plot_spike_counts, raster_plot\n",
    "from allensdk.brain_observatory.visualization import plot_running_speed\n",
    "\n",
    "import allensdk\n",
    "from allensdk.brain_observatory.behavior.behavior_project_cache import VisualBehaviorNeuropixelsProjectCache\n",
    "\n",
    "# Confirming your allensdk version\n",
    "print(f\"Your allensdk version is: {allensdk.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we import the LFP functions\n",
    "import importlib\n",
    "import LFP_functions\n",
    "importlib.reload(LFP_functions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LFP data obtention from Allen Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update this to a valid directory in your filesystem. This is where the data will be stored.\n",
    "output_dir = r'E:/BT_Code'\n",
    "DOWNLOAD_COMPLETE_DATASET = False\n",
    "manifest_path = os.path.join(output_dir, \"manifest.json\")\n",
    "cache = VisualBehaviorNeuropixelsProjectCache.from_s3_cache(cache_dir=output_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the manifest downloaded. We worked with version 0.5.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(cache.current_manifest())\n",
    "cache.load_latest_manifest()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now select the probes in VISp from session data in the dataset that suit our specifications and store them in lfp_VISp. This array contains the probe and session IDs, which we will use to download the LFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ecephys_sessions = cache.get_ecephys_session_table()\n",
    "probes = cache.get_probe_table()\n",
    "valid_lfp = probes[probes['has_lfp_data']] #we select sessions with LFP data\n",
    "\n",
    "#we find the indices for our sessions of interest (wt mice, 3uL reward, images G and containing a probe in VISp)\n",
    "wt_indices = ecephys_sessions[ecephys_sessions['genotype'] == 'wt/wt'][ecephys_sessions['session_type'] == 'EPHYS_1_images_G_3uL_reward'].index.tolist()\n",
    "lfp_VISp = valid_lfp[(valid_lfp['structure_acronyms'].str.contains(\", 'VISp',\")) & (valid_lfp['ecephys_session_id'].isin(wt_indices))]\n",
    "lfp_VISp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our work, we used 16 sessions. The probe and session ids are all stored in probe_ids and session_ids respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "probe_ids = [1055324729, 1062886498, 1055328906, 1065764271, 1067688317, 1081293774, 1081297300, 1104418694, 1108676394, 1109998730, 1117240990, 1118711338, 1120380894, 1128939997, 1130463465, 1140256852]\n",
    "\n",
    "session_ids = []\n",
    "for probe_id in probe_ids:\n",
    "    session_id = probes.loc[probe_id]['ecephys_session_id']\n",
    "    if session_id not in session_ids:\n",
    "        session_ids.append(session_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now obtain the LFPs and save them as pickle files. ATTENTION: each LFP file weights several GBs, so this is will take a long time. Make sure you work with a GPU or an overall powerful computer to avoid memory errors during the code execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sessions = {} #dictionary of sessions\n",
    "for ses in range(len(probe_ids)):\n",
    "    sessions[ses+1] = cache.get_ecephys_session(\n",
    "                ecephys_session_id=session_ids[ses])\n",
    "    #here we are getting the LFP data for the probe\n",
    "    lfp = sessions[ses+1].get_lfp(probe_ids[ses])\n",
    "    #we save the LFP data as a pickle file\n",
    "\n",
    "    pickle_file_path = \"lfp_data/lfp_data_\"+str(ses+1)+\".pickle\"\n",
    "    with open(pickle_file_path, \"wb\") as file:\n",
    "        pickle.dump(lfp, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LFP alingment to stimuli and V1 selection\n",
    "\n",
    "We are first going to select the stimulus presentation data for the active task and the passive replay. The LFP data is then aligned to the presentation of novel images with the align_image_lfps function. Finally, we select the channels in V1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check if the folders to store the aligned LFPs and channels exist, if not, create it\n",
    "if not os.path.exists(\"aligned_LFP_area\"):\n",
    "    os.makedirs(\"aligned_LFP_area\")\n",
    "\n",
    "if not os.path.exists(\"channels\"):\n",
    "    os.makedirs(\"channels\")\n",
    "\n",
    "\n",
    "#save in stim_active the stim_presentations with the stimulus_block 0\n",
    "for ses in range(len(session_ids)):\n",
    "    probe_id = probe_ids[ses] #select the probe id for the session\n",
    "    stim_presentations = sessions[ses+1].stimulus_presentations\n",
    "    stim_active = stim_presentations[stim_presentations['stimulus_block'] == 0]\n",
    "    stim_passive = stim_presentations[stim_presentations['stimulus_block'] == 5]\n",
    "\n",
    "    #we obtain alligned LFP data given the active session\n",
    "    aligned_lfps_act = LFP_functions.align_image_lfps(stim_active,lfp)\n",
    "\n",
    "    #we obtain alligned LFP data given the passive session\n",
    "    aligned_lfps_pas = LFP_functions.align_image_lfps(stim_passive,lfp)\n",
    "\n",
    "    #we select the channels within V1\n",
    "    chans = sessions[ses+1].get_channels()\n",
    "    aligned_lfps_act_en_V1,chans_V1 = LFP_functions.select_area(aligned_lfps_act, chans, probe_id, 'VISp')\n",
    "    aligned_lfps_pas_en_V1,_ = LFP_functions.select_area(aligned_lfps_pas, chans, probe_id, 'VISp')\n",
    "\n",
    "    #save aligned_lfps_V1 and chans_V1 in a pickle file for each session\n",
    "    with open(\"aligned_LFP_area/aligned_lfps_act_V1\"+str(ses)+\".pickle\", \"wb\") as file:\n",
    "        pickle.dump(aligned_lfps_act_en_V1, file)\n",
    "    with open(\"aligned_LFP_area/aligned_lfps_pas_V1_\"+str(ses)+\".pickle\", \"wb\") as file:\n",
    "        pickle.dump(aligned_lfps_pas_en_V1, file)\n",
    "    with open(\"channels/chans_V1_\"+str(ses)+\".pickle\", \"wb\") as file:\n",
    "        pickle.dump(chans_V1, file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have the aligned lfp data for each session and the channels, we can compute the power spectra and Current Source Density (see notebooks get_power_spectra and get_CSD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra code: visualize number of presentations and probe locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the file path of the pickle file\n",
    "ses=1\n",
    "pickle_file_path = \"aligned_LFP_area/aligned_lfps_act_V1_\"+str(ses)+\".pickle\"\n",
    "\n",
    "# Load the LFP data from the pickle file\n",
    "with open(pickle_file_path, \"rb\") as file:\n",
    "    lfp= pickle.load(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We obtain the active and passive stimuli for separate. Although they both have the same 'stimulus_name' atribute, they have different 'stimulus_block' values, where the active task corresponds to block 0 and the passive to block 5. We use this to obtain the two dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stimulus_block</th>\n",
       "      <th>stimulus_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stimulus_presentations_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Natural_Images_Lum_Matched_set_ophys_G_2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4804</th>\n",
       "      <td>1</td>\n",
       "      <td>spontaneous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4805</th>\n",
       "      <td>2</td>\n",
       "      <td>gabor_20_deg_250ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8450</th>\n",
       "      <td>3</td>\n",
       "      <td>spontaneous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8451</th>\n",
       "      <td>4</td>\n",
       "      <td>flash_250ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8601</th>\n",
       "      <td>5</td>\n",
       "      <td>Natural_Images_Lum_Matched_set_ophys_G_2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           stimulus_block  \\\n",
       "stimulus_presentations_id                   \n",
       "0                                       0   \n",
       "4804                                    1   \n",
       "4805                                    2   \n",
       "8450                                    3   \n",
       "8451                                    4   \n",
       "8601                                    5   \n",
       "\n",
       "                                                         stimulus_name  \n",
       "stimulus_presentations_id                                               \n",
       "0                          Natural_Images_Lum_Matched_set_ophys_G_2019  \n",
       "4804                                                       spontaneous  \n",
       "4805                                                gabor_20_deg_250ms  \n",
       "8450                                                       spontaneous  \n",
       "8451                                                       flash_250ms  \n",
       "8601                       Natural_Images_Lum_Matched_set_ophys_G_2019  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stim_presentations = session.stimulus_presentations\n",
    "stim_presentations[['stimulus_block','stimulus_name']][stim_presentations['stimulus_name'] != stim_presentations['stimulus_name'].shift(1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of active trials: 4804\n",
      "number of passive trials: 4804\n"
     ]
    }
   ],
   "source": [
    "#save in stim_active the stim_presentations with the stimulus_block 0\n",
    "stim_active = stim_presentations[stim_presentations['stimulus_block'] == 0]\n",
    "stim_passive = stim_presentations[stim_presentations['stimulus_block'] == 5]\n",
    "\n",
    "print('number of active trials:', len(stim_active))\n",
    "print('number of passive trials:', len(stim_passive))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the stimuli have the same length. In fact, as stated in Allen Visual Behavior Technical Whitepaper, it is a fram-for-frame replay of active behavior, the only difference is that the lick spout was retracted and therefore the mouse was unable to earn rewards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of pickle files: 9\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21755fff5e0>"
      ]
     },
     "execution_count": 17,
     "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": [
    "#import all pickle files in the folder channels\n",
    "\n",
    "pickle_files = sorted([f for f in os.listdir('channels') if f.endswith('.pickle')])\n",
    "print('number of pickle files:', len(pickle_files))\n",
    "\n",
    "#open the pickle files and save them in a dicitonary\n",
    "channels = {}\n",
    "\n",
    "for i,file in enumerate(pickle_files):\n",
    "    with open('channels/'+file, \"rb\") as file:\n",
    "        channel = pickle.load(file)\n",
    "        channels[i+1] = channel\n",
    "\n",
    "#plot the ccf positions of every channel in 3D\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "for channel in channels:\n",
    "    ax.scatter(channels[channel]['anterior_posterior_ccf_coordinate'], channels[channel]['dorsal_ventral_ccf_coordinate'], channels[channel]['left_right_ccf_coordinate'])\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('Z')\n",
    "\n",
    "#add a legend\n",
    "ax.legend(channels.keys())\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top: act stimuli\n",
      "bottom: pas stimuli\n",
      "------------------------\n",
      "im012_r (96, 22, 23250)\n",
      "im012_r (96, 22, 23250)\n",
      "------------------------\n",
      "im036_r (96, 23, 24375)\n",
      "im036_r (96, 23, 24375)\n",
      "------------------------\n",
      "im044_r (96, 23, 16500)\n",
      "im044_r (96, 23, 16500)\n",
      "------------------------\n",
      "im047_r (96, 23, 24375)\n",
      "im047_r (96, 23, 24375)\n",
      "------------------------\n",
      "im078_r (96, 21, 25125)\n",
      "im078_r (96, 21, 25125)\n",
      "------------------------\n",
      "im083_r (96, 22, 22875)\n",
      "im083_r (96, 22, 22875)\n",
      "------------------------\n",
      "im111_r (96, 21, 14250)\n",
      "im111_r (96, 21, 14250)\n",
      "------------------------\n",
      "im115_r (96, 21, 18000)\n",
      "im115_r (96, 21, 18000)\n",
      "------------------------\n"
     ]
    }
   ],
   "source": [
    "#we check that the dimensions of the act and pas dictionaries are the same\n",
    "print('top: act stimuli\\nbottom: pas stimuli')\n",
    "print('------------------------')\n",
    "for key in sorted(aligned_lfps_act_en.keys()):\n",
    "    val = aligned_lfps_act_en[key]\n",
    "    print(key,val.shape)\n",
    "    print(key,aligned_lfps_pas_en[key].shape)\n",
    "    print('------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the two dictionaries the exact same number of elements (that is, presentation ids and maximum timesteps for every image)"
   ]
  }
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