tutorial_data_analysis_nwb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from api_neurotask import *\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pynapple as nap\n",
    "import matplotlib.gridspec as gridspec"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Load and Filter Data\n",
    "\n",
    "Before diving into the analysis, it's essential to load the dataset and preprocess it. In this step, we'll filter out non-reward trials to focus our analysis on the relevant data.\n",
    "\n",
    "Remove trials with outcomes: Aborted (A), Incomplete (I), Failed (F)\n",
    "\n",
    "Also, return the bin size of the dataset in ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = nap.load_file(\"001056/sub-Animal-1-&-2/sub-Animal-1-&-2.nwb\")\n",
    "df , bin = get_dataframe(data,filter_result=[b'R'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sub-Animal-1-&-2\n",
       "┍━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━┑\n",
       "│ Keys              │ Type     │\n",
       "┝━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━┥\n",
       "│ spikes_counts     │ TsdFrame │\n",
       "│ trial_id          │ Tsd      │\n",
       "│ session           │ Tsd      │\n",
       "│ datasetID         │ Tsd      │\n",
       "│ animal            │ Tsd      │\n",
       "│ result            │ Tsd      │\n",
       "│ EventTarget_Onset │ Tsd      │\n",
       "│ EventGo_cue       │ Tsd      │\n",
       "│ EventBump         │ Tsd      │\n",
       "│ target_dir        │ Tsd      │\n",
       "│ hand_vel_y        │ Tsd      │\n",
       "│ hand_vel_x        │ Tsd      │\n",
       "│ hand_pos_y        │ Tsd      │\n",
       "│ hand_pos_x        │ Tsd      │\n",
       "│ bump_dir          │ Tsd      │\n",
       "┕━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━┙"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Neuron0</th>\n",
       "      <th>Neuron1</th>\n",
       "      <th>Neuron2</th>\n",
       "      <th>Neuron3</th>\n",
       "      <th>Neuron4</th>\n",
       "      <th>Neuron5</th>\n",
       "      <th>Neuron6</th>\n",
       "      <th>Neuron7</th>\n",
       "      <th>Neuron8</th>\n",
       "      <th>Neuron9</th>\n",
       "      <th>...</th>\n",
       "      <th>result</th>\n",
       "      <th>EventTarget_Onset</th>\n",
       "      <th>EventGo_cue</th>\n",
       "      <th>EventBump</th>\n",
       "      <th>target_dir</th>\n",
       "      <th>hand_vel_y</th>\n",
       "      <th>hand_vel_x</th>\n",
       "      <th>hand_pos_y</th>\n",
       "      <th>hand_pos_x</th>\n",
       "      <th>bump_dir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.270410</td>\n",
       "      <td>-9.879614</td>\n",
       "      <td>-40.837310</td>\n",
       "      <td>-2.729025</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.419577</td>\n",
       "      <td>-9.446477</td>\n",
       "      <td>-40.698774</td>\n",
       "      <td>-2.825641</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.573560</td>\n",
       "      <td>-8.983446</td>\n",
       "      <td>-40.568888</td>\n",
       "      <td>-2.917859</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.821613</td>\n",
       "      <td>-8.411639</td>\n",
       "      <td>-40.446963</td>\n",
       "      <td>-3.004936</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.053716</td>\n",
       "      <td>-7.756530</td>\n",
       "      <td>-40.332516</td>\n",
       "      <td>-3.085807</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 181 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Neuron0  Neuron1  Neuron2  Neuron3  Neuron4  Neuron5  Neuron6  Neuron7  \\\n",
       "0      1.0      0.0      0.0      0.0      0.0      0.0      0.0      1.0   \n",
       "1      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "2      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "3      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "4      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "   Neuron8  Neuron9  ...  result  EventTarget_Onset  EventGo_cue  EventBump  \\\n",
       "0      1.0      0.0  ...    b'R'              False        False      False   \n",
       "1      1.0      0.0  ...    b'R'              False        False      False   \n",
       "2      1.0      0.0  ...    b'R'              False        False      False   \n",
       "3      0.0      0.0  ...    b'R'              False        False      False   \n",
       "4      0.0      0.0  ...    b'R'              False        False      False   \n",
       "\n",
       "   target_dir  hand_vel_y  hand_vel_x  hand_pos_y  hand_pos_x  bump_dir  \n",
       "0         0.0   14.270410   -9.879614  -40.837310   -2.729025  1.570796  \n",
       "1         0.0   13.419577   -9.446477  -40.698774   -2.825641  1.570796  \n",
       "2         0.0   12.573560   -8.983446  -40.568888   -2.917859  1.570796  \n",
       "3         0.0   11.821613   -8.411639  -40.446963   -3.004936  1.570796  \n",
       "4         0.0   11.053716   -7.756530  -40.332516   -3.085807  1.570796  \n",
       "\n",
       "[5 rows x 181 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Rebin Data for Analysis"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `rebin` function is designed to rebin a dataset by aggregating data points into larger bins based on a specified bin size. Here's a brief overview of how to use it:\n",
    "\n",
    "The `rebin` function takes the following parameters:\n",
    "- `dataset1`: The DataFrame containing the data to be rebinned.\n",
    "- `prev_bin_size`: The original bin size of the data.\n",
    "- `new_bin_size`: The desired bin size to aggregate data points into. This is the new bin size you want the data to be rebinned to.\n",
    "- `reset` (optional): A boolean indicating whether to reset the index of the resulting DataFrame.\n",
    "\n",
    "When called, the function aggregates data points within each bin based on the specified aggregation functions. The aggregation functions are determined based on the column names of the input DataFrame. For spiking data, the function aggregates by summing the values. For columns related to behavior, the function applies a custom decimation function to downsample the data. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rebin the dataset with a bin size of 20 ms \n",
    "df10 = rebin(df,prev_bin_size = bin ,new_bin_size = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>session</th>\n",
       "      <th>trial_id</th>\n",
       "      <th>Neuron0</th>\n",
       "      <th>Neuron1</th>\n",
       "      <th>Neuron2</th>\n",
       "      <th>Neuron3</th>\n",
       "      <th>Neuron4</th>\n",
       "      <th>Neuron5</th>\n",
       "      <th>Neuron6</th>\n",
       "      <th>Neuron7</th>\n",
       "      <th>...</th>\n",
       "      <th>result</th>\n",
       "      <th>EventTarget_Onset</th>\n",
       "      <th>EventGo_cue</th>\n",
       "      <th>EventBump</th>\n",
       "      <th>target_dir</th>\n",
       "      <th>hand_vel_y</th>\n",
       "      <th>hand_vel_x</th>\n",
       "      <th>hand_pos_y</th>\n",
       "      <th>hand_pos_x</th>\n",
       "      <th>bump_dir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.844993</td>\n",
       "      <td>-9.663045</td>\n",
       "      <td>-40.768042</td>\n",
       "      <td>-2.777333</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.197587</td>\n",
       "      <td>-8.697542</td>\n",
       "      <td>-40.507926</td>\n",
       "      <td>-2.961398</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.629333</td>\n",
       "      <td>-7.434564</td>\n",
       "      <td>-40.279349</td>\n",
       "      <td>-3.122960</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.039913</td>\n",
       "      <td>-6.239253</td>\n",
       "      <td>-40.083160</td>\n",
       "      <td>-3.259433</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.627443</td>\n",
       "      <td>-5.119604</td>\n",
       "      <td>-39.916148</td>\n",
       "      <td>-3.373000</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361893</th>\n",
       "      <td>2</td>\n",
       "      <td>1386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>59.294004</td>\n",
       "      <td>11.951860</td>\n",
       "      <td>-27.398168</td>\n",
       "      <td>0.320557</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361894</th>\n",
       "      <td>2</td>\n",
       "      <td>1386</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>57.145496</td>\n",
       "      <td>9.816891</td>\n",
       "      <td>-26.229608</td>\n",
       "      <td>0.539739</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361895</th>\n",
       "      <td>2</td>\n",
       "      <td>1386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>51.964747</td>\n",
       "      <td>6.463730</td>\n",
       "      <td>-25.133289</td>\n",
       "      <td>0.704709</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361896</th>\n",
       "      <td>2</td>\n",
       "      <td>1386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>44.039648</td>\n",
       "      <td>2.081319</td>\n",
       "      <td>-24.168594</td>\n",
       "      <td>0.791723</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361897</th>\n",
       "      <td>2</td>\n",
       "      <td>1386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>33.624477</td>\n",
       "      <td>-3.044886</td>\n",
       "      <td>-23.389764</td>\n",
       "      <td>0.782321</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>361898 rows × 181 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        session  trial_id  Neuron0  Neuron1  Neuron2  Neuron3  Neuron4  \\\n",
       "0             1         1      1.0      0.0      0.0      0.0      0.0   \n",
       "1             1         1      0.0      0.0      0.0      0.0      0.0   \n",
       "2             1         1      0.0      0.0      0.0      0.0      0.0   \n",
       "3             1         1      0.0      0.0      0.0      0.0      1.0   \n",
       "4             1         1      0.0      0.0      0.0      0.0      0.0   \n",
       "...         ...       ...      ...      ...      ...      ...      ...   \n",
       "361893        2      1386      0.0      1.0      0.0      1.0      0.0   \n",
       "361894        2      1386      1.0      0.0      0.0      1.0      0.0   \n",
       "361895        2      1386      0.0      0.0      0.0      1.0      0.0   \n",
       "361896        2      1386      0.0      0.0      0.0      0.0      0.0   \n",
       "361897        2      1386      0.0      0.0      0.0      3.0      0.0   \n",
       "\n",
       "        Neuron5  Neuron6  Neuron7  ...  result  EventTarget_Onset  \\\n",
       "0           0.0      0.0      1.0  ...    b'R'              False   \n",
       "1           0.0      0.0      0.0  ...    b'R'              False   \n",
       "2           1.0      0.0      0.0  ...    b'R'              False   \n",
       "3           0.0      0.0      0.0  ...    b'R'              False   \n",
       "4           0.0      0.0      0.0  ...    b'R'              False   \n",
       "...         ...      ...      ...  ...     ...                ...   \n",
       "361893      1.0      0.0      0.0  ...    b'R'              False   \n",
       "361894      2.0      2.0      0.0  ...    b'R'              False   \n",
       "361895      0.0      1.0      0.0  ...    b'R'              False   \n",
       "361896      2.0      0.0      0.0  ...    b'R'              False   \n",
       "361897      0.0      0.0      0.0  ...    b'R'              False   \n",
       "\n",
       "        EventGo_cue  EventBump  target_dir  hand_vel_y  hand_vel_x  \\\n",
       "0             False      False    0.000000   13.844993   -9.663045   \n",
       "1             False      False    0.000000   12.197587   -8.697542   \n",
       "2             False      False    0.000000   10.629333   -7.434564   \n",
       "3             False      False    0.000000    9.039913   -6.239253   \n",
       "4             False      False    0.000000    7.627443   -5.119604   \n",
       "...             ...        ...         ...         ...         ...   \n",
       "361893        False      False    1.570796   59.294004   11.951860   \n",
       "361894        False      False    1.570796   57.145496    9.816891   \n",
       "361895        False      False    1.570796   51.964747    6.463730   \n",
       "361896        False      False    1.570796   44.039648    2.081319   \n",
       "361897        False      False    1.570796   33.624477   -3.044886   \n",
       "\n",
       "        hand_pos_y  hand_pos_x  bump_dir  \n",
       "0       -40.768042   -2.777333  1.570796  \n",
       "1       -40.507926   -2.961398  1.570796  \n",
       "2       -40.279349   -3.122960  1.570796  \n",
       "3       -40.083160   -3.259433  1.570796  \n",
       "4       -39.916148   -3.373000  1.570796  \n",
       "...            ...         ...       ...  \n",
       "361893  -27.398168    0.320557  0.000000  \n",
       "361894  -26.229608    0.539739  0.000000  \n",
       "361895  -25.133289    0.704709  0.000000  \n",
       "361896  -24.168594    0.791723  0.000000  \n",
       "361897  -23.389764    0.782321  0.000000  \n",
       "\n",
       "[361898 rows x 181 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Align to specific event"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before using the `align_event` function, it's essential to understand its purpose and how it operates. This function is designed to align events within a DataFrame based on a specified start event marker. Here's a brief overview of how to use it:\n",
    "\n",
    "The `align_event` function takes the following parameters:\n",
    "- `df`: The DataFrame containing the data.\n",
    "- `bin_size`: the bin size of the data in ms.\n",
    "- `start_event`: The event marker indicating the start of a trial or session.\n",
    "- `offset_min` (optional): The minimum offset (in ms) to consider before the start_event.\n",
    "- `offset_max` (optional): The maximum offset (in ms) to consider after the start_event.\n",
    "\n",
    "\n",
    "Please note that for Dataset 1, it's not possible to align events since it doesn't contain event information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['EventTarget_Onset', 'EventGo_cue', 'EventBump']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[col for col in df.columns if col.startswith('Event')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = align_event(df10, 'EventTarget_Onset', bin_size=20,offset_min=-20,offset_max=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>session</th>\n",
       "      <th>trial_id</th>\n",
       "      <th>Neuron0</th>\n",
       "      <th>Neuron1</th>\n",
       "      <th>Neuron2</th>\n",
       "      <th>Neuron3</th>\n",
       "      <th>Neuron4</th>\n",
       "      <th>Neuron5</th>\n",
       "      <th>Neuron6</th>\n",
       "      <th>Neuron7</th>\n",
       "      <th>...</th>\n",
       "      <th>result</th>\n",
       "      <th>EventTarget_Onset</th>\n",
       "      <th>EventGo_cue</th>\n",
       "      <th>EventBump</th>\n",
       "      <th>target_dir</th>\n",
       "      <th>hand_vel_y</th>\n",
       "      <th>hand_vel_x</th>\n",
       "      <th>hand_pos_y</th>\n",
       "      <th>hand_pos_x</th>\n",
       "      <th>bump_dir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.936864</td>\n",
       "      <td>-1.749977</td>\n",
       "      <td>-32.451963</td>\n",
       "      <td>0.111210</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.249790</td>\n",
       "      <td>-0.079801</td>\n",
       "      <td>-32.457340</td>\n",
       "      <td>0.093195</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.423443</td>\n",
       "      <td>0.961891</td>\n",
       "      <td>-32.408668</td>\n",
       "      <td>0.103679</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.001144</td>\n",
       "      <td>1.019113</td>\n",
       "      <td>-32.330823</td>\n",
       "      <td>0.125254</td>\n",
       "      <td>1.570796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.141593</td>\n",
       "      <td>0.185450</td>\n",
       "      <td>-5.327119</td>\n",
       "      <td>-32.338394</td>\n",
       "      <td>0.920466</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324168</th>\n",
       "      <td>2</td>\n",
       "      <td>1031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.926991</td>\n",
       "      <td>-1.184138</td>\n",
       "      <td>-0.090619</td>\n",
       "      <td>-35.233672</td>\n",
       "      <td>0.353955</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324439</th>\n",
       "      <td>2</td>\n",
       "      <td>1032</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.926991</td>\n",
       "      <td>-0.231191</td>\n",
       "      <td>15.396076</td>\n",
       "      <td>-34.698562</td>\n",
       "      <td>-1.142190</td>\n",
       "      <td>2.356194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324440</th>\n",
       "      <td>2</td>\n",
       "      <td>1032</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.926991</td>\n",
       "      <td>-0.297402</td>\n",
       "      <td>13.231062</td>\n",
       "      <td>-34.703743</td>\n",
       "      <td>-0.854803</td>\n",
       "      <td>2.356194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324441</th>\n",
       "      <td>2</td>\n",
       "      <td>1032</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.926991</td>\n",
       "      <td>-0.763108</td>\n",
       "      <td>10.433138</td>\n",
       "      <td>-34.713396</td>\n",
       "      <td>-0.617138</td>\n",
       "      <td>2.356194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324442</th>\n",
       "      <td>2</td>\n",
       "      <td>1032</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.926991</td>\n",
       "      <td>-1.665809</td>\n",
       "      <td>7.245807</td>\n",
       "      <td>-34.737073</td>\n",
       "      <td>-0.440304</td>\n",
       "      <td>2.356194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11063 rows × 181 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        session  trial_id  Neuron0  Neuron1  Neuron2  Neuron3  Neuron4  \\\n",
       "116           1         1      1.0      0.0      0.0      0.0      0.0   \n",
       "117           1         1      1.0      1.0      0.0      0.0      0.0   \n",
       "118           1         1      0.0      0.0      0.0      0.0      0.0   \n",
       "119           1         1      0.0      0.0      1.0      0.0      0.0   \n",
       "378           1         2      0.0      0.0      0.0      0.0      0.0   \n",
       "...         ...       ...      ...      ...      ...      ...      ...   \n",
       "324168        2      1031      0.0      0.0      0.0      0.0      0.0   \n",
       "324439        2      1032      0.0      0.0      0.0      0.0      0.0   \n",
       "324440        2      1032      1.0      0.0      1.0      0.0      0.0   \n",
       "324441        2      1032      0.0      0.0      0.0      0.0      0.0   \n",
       "324442        2      1032      3.0      0.0      1.0      0.0      0.0   \n",
       "\n",
       "        Neuron5  Neuron6  Neuron7  ...  result  EventTarget_Onset  \\\n",
       "116         1.0      1.0      2.0  ...    b'R'                NaN   \n",
       "117         0.0      0.0      0.0  ...    b'R'               True   \n",
       "118         1.0      0.0      1.0  ...    b'R'                NaN   \n",
       "119         0.0      0.0      0.0  ...    b'R'                NaN   \n",
       "378         0.0      0.0      1.0  ...    b'R'                NaN   \n",
       "...         ...      ...      ...  ...     ...                ...   \n",
       "324168      0.0      0.0      0.0  ...    b'R'                NaN   \n",
       "324439      0.0      0.0      0.0  ...    b'R'                NaN   \n",
       "324440      0.0      0.0      0.0  ...    b'R'               True   \n",
       "324441      0.0      0.0      0.0  ...    b'R'                NaN   \n",
       "324442      0.0      0.0      0.0  ...    b'R'                NaN   \n",
       "\n",
       "        EventGo_cue  EventBump  target_dir  hand_vel_y  hand_vel_x  \\\n",
       "116           False      False    0.000000   -1.936864   -1.749977   \n",
       "117            True      False    0.000000    1.249790   -0.079801   \n",
       "118           False      False    0.000000    3.423443    0.961891   \n",
       "119           False      False    0.000000    4.001144    1.019113   \n",
       "378           False      False    3.141593    0.185450   -5.327119   \n",
       "...             ...        ...         ...         ...         ...   \n",
       "324168        False      False    3.926991   -1.184138   -0.090619   \n",
       "324439        False      False    3.926991   -0.231191   15.396076   \n",
       "324440         True      False    3.926991   -0.297402   13.231062   \n",
       "324441        False      False    3.926991   -0.763108   10.433138   \n",
       "324442        False      False    3.926991   -1.665809    7.245807   \n",
       "\n",
       "        hand_pos_y  hand_pos_x  bump_dir  \n",
       "116     -32.451963    0.111210  1.570796  \n",
       "117     -32.457340    0.093195  1.570796  \n",
       "118     -32.408668    0.103679  1.570796  \n",
       "119     -32.330823    0.125254  1.570796  \n",
       "378     -32.338394    0.920466  0.000000  \n",
       "...            ...         ...       ...  \n",
       "324168  -35.233672    0.353955  0.000000  \n",
       "324439  -34.698562   -1.142190  2.356194  \n",
       "324440  -34.703743   -0.854803  2.356194  \n",
       "324441  -34.713396   -0.617138  2.356194  \n",
       "324442  -34.737073   -0.440304  2.356194  \n",
       "\n",
       "[11063 rows x 181 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Data split and model's inputs processing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " This function is designed to process a dataset, splitting it into training, validation, and testing sets, and optionally performing z-score normalization on the input data. Here's a brief overview of how to use it:\n",
    "\n",
    "The `process_data` function takes the following parameters:\n",
    "\n",
    "- `df` (pd.DataFrame): The DataFrame containing the data.\n",
    "- `bins_before` (int): Number of bins before the output used for decoding.\n",
    "- `training_range` (list): The range [start, end] for the training set.\n",
    "- `valid_range` (list): The range [start, end] for the validation set.\n",
    "- `testing_range` (list): The range [start, end] for the testing set.\n",
    "- `behavior_columns` (list): List of columns containing behavioral data.\n",
    "- `zscore` (bool): Whether to apply z-score normalization.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_list, y_train_list, X_val_list, y_val_list, X_test_list, y_test_list = process_data(df10, bins_before=5, \n",
    "                                                 training_range=[0, 0.7], valid_range=[0.7, 0.8], testing_range=[0.8, 1], \n",
    "                                                 behavior_columns=['hand_vel_x', 'hand_vel_y'],zscore = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 461 unique neurons in the dataset in 4 different sessions\n"
     ]
    }
   ],
   "source": [
    "print(\"There are\", sum(X.shape[2] for X in X_train_list), \"unique neurons in the dataset in\",len(X_train_list),\"different sessions\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Exploratory Data Analysis"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this analysis, we are examining the unique number of trials per session for each animal. We use the pandas `groupby` function to group the data by the 'animal' and 'session' columns, and then calculate the number of unique trial IDs within each group using the `nunique` function. Finally, we reset the index of the resulting DataFrame and rename the column to 'unique_trials_per_session' for clarity. This analysis allows us to understand the distribution of trial counts across different sessions and animals in the dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>animal</th>\n",
       "      <th>session</th>\n",
       "      <th>unique_trials_per_session</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   animal  session  unique_trials_per_session\n",
       "0       1        1                        540\n",
       "1       1        2                       1279\n",
       "2       2        1                        483\n",
       "3       2        2                        464"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['animal', 'session'])['trial_id'].nunique().reset_index(name='unique_trials_per_session')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1 Velocity and Activty Visualization"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to visualize one trial's neural activity alongside hand movement velocity. This code generates a raster plot and plots velocity data for a specific trial in the dataset. First, it filters the dataset to select the data for a particular trial based on the trial ID, session, and animal. It then identifies the columns corresponding to neurons and event timings. Using this information, it creates a raster plot to visualize the firing activity of neurons over time, with each row representing a different neuron. Additionally, it plots vertical lines to indicate the occurrence of specific events such as 'Go_cue', 'Target_Onset', and 'Bump_time'. The x-axis ticks are labeled with event indications to show the timing of these events. Furthermore, velocity data for the hand movement in the x and y directions is plotted on the secondary y-axis. This combined visualization provides insights into the neural activity and hand movement dynamics during the trial.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trial_id = 3\n",
    "trial_data = df[(df['trial_id'] == trial_id) & (df['session'] == 1) & (df['animal'] == 1)]\n",
    "\n",
    "# Select data for neurons\n",
    "neuron_columns = [col for col in trial_data.columns if col.startswith('Neuron')]\n",
    "neurons = neuron_columns[:]\n",
    "\n",
    "# Identify event columns\n",
    "#event_columns = ['Go_cue', 'Target_Onset', 'Bump_time']\n",
    "event_columns = [col for col in df.columns if col.startswith('Event')]\n",
    "\n",
    "# Create a figure and axis\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "# Plot events per time for the selected neurons\n",
    "for neuron in neurons:\n",
    "    events = trial_data.index[trial_data[neuron] == 1].tolist()\n",
    "    ax.eventplot(events, lineoffsets=neurons.index(neuron), linelengths=0.5, color='black')\n",
    "\n",
    "# Plot event indications as vertical lines for each event type\n",
    "colors = ['red', 'blue', 'green']\n",
    "event_labels = []\n",
    "event_positions = []\n",
    "\n",
    "for idx, event_col in enumerate(event_columns):\n",
    "    event_indices = trial_data.index[trial_data[event_col] == 1].tolist()\n",
    "    for event_index in event_indices:\n",
    "        ax.axvline(x=event_index, linestyle='-', label=event_col.replace(\"Event\", \"\"))\n",
    "        event_positions.append(event_index)\n",
    "        event_labels.append(event_col.replace(\"Event\", \"\"))\n",
    "\n",
    "# Combine event labels for positions with multiple events\n",
    "event_dict = {}\n",
    "for pos, label in zip(event_positions, event_labels):\n",
    "    if pos in event_dict:\n",
    "        event_dict[pos].add(label)\n",
    "    else:\n",
    "        event_dict[pos] = {label}\n",
    "\n",
    "# Sort events to ensure the ticks are in order\n",
    "sorted_event_positions = sorted(event_dict.keys())\n",
    "sorted_event_labels = [', '.join(event_dict[pos]) for pos in sorted_event_positions]\n",
    "\n",
    "# Set x-axis ticks with event indications\n",
    "ax.set_xticks(sorted_event_positions)\n",
    "ax.set_xticklabels(sorted_event_labels, rotation=45, fontsize=12)\n",
    "\n",
    "# Plot velocity data for y-axis\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(trial_data['hand_vel_y'], color='black', label='vy')\n",
    "ax2.plot(trial_data['hand_vel_x'], color='blue', label='vx')\n",
    "\n",
    "# Set y-axis label for velocity\n",
    "ax2.set_ylabel('Velocity', fontsize=14)\n",
    "\n",
    "# Increase font sizes for better readability\n",
    "ax.set_ylabel('Neurons', fontsize=14)\n",
    "ax.set_title(f'Raster Plot for Trial {trial_id}', fontsize=16)\n",
    "plt.legend()\n",
    "# Show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1 Get Individual Trial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also important to obtain and inspect individual trials. Here by defining the different conditions, we can analyse the desired trial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>session</th>\n",
       "      <th>trial_id</th>\n",
       "      <th>Neuron0</th>\n",
       "      <th>Neuron1</th>\n",
       "      <th>Neuron2</th>\n",
       "      <th>Neuron3</th>\n",
       "      <th>Neuron4</th>\n",
       "      <th>Neuron5</th>\n",
       "      <th>Neuron6</th>\n",
       "      <th>Neuron7</th>\n",
       "      <th>...</th>\n",
       "      <th>result</th>\n",
       "      <th>EventTarget_Onset</th>\n",
       "      <th>EventGo_cue</th>\n",
       "      <th>EventBump</th>\n",
       "      <th>target_dir</th>\n",
       "      <th>hand_vel_y</th>\n",
       "      <th>hand_vel_x</th>\n",
       "      <th>hand_pos_y</th>\n",
       "      <th>hand_pos_x</th>\n",
       "      <th>bump_dir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>-0.403976</td>\n",
       "      <td>-0.401545</td>\n",
       "      <td>-32.490878</td>\n",
       "      <td>-0.911772</td>\n",
       "      <td>4.712389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>-0.399243</td>\n",
       "      <td>-0.404625</td>\n",
       "      <td>-32.496644</td>\n",
       "      <td>-0.918049</td>\n",
       "      <td>4.712389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>-0.960029</td>\n",
       "      <td>-0.272455</td>\n",
       "      <td>-32.509032</td>\n",
       "      <td>-0.924836</td>\n",
       "      <td>4.712389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>-1.636682</td>\n",
       "      <td>-0.168425</td>\n",
       "      <td>-32.535650</td>\n",
       "      <td>-0.929261</td>\n",
       "      <td>4.712389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>-2.102847</td>\n",
       "      <td>-0.067119</td>\n",
       "      <td>-32.572998</td>\n",
       "      <td>-0.931526</td>\n",
       "      <td>4.712389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>28.724272</td>\n",
       "      <td>-7.871682</td>\n",
       "      <td>-24.989324</td>\n",
       "      <td>0.530218</td>\n",
       "      <td>4.712389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>23.008711</td>\n",
       "      <td>-10.761518</td>\n",
       "      <td>-24.470422</td>\n",
       "      <td>0.343102</td>\n",
       "      <td>4.712389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>17.195406</td>\n",
       "      <td>-12.802956</td>\n",
       "      <td>-24.069575</td>\n",
       "      <td>0.105206</td>\n",
       "      <td>4.712389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>12.520113</td>\n",
       "      <td>-13.389835</td>\n",
       "      <td>-23.774864</td>\n",
       "      <td>-0.158903</td>\n",
       "      <td>4.712389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>10.111746</td>\n",
       "      <td>-13.151919</td>\n",
       "      <td>-23.604688</td>\n",
       "      <td>-0.358865</td>\n",
       "      <td>4.712389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 181 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     session  trial_id  Neuron0  Neuron1  Neuron2  Neuron3  Neuron4  Neuron5  \\\n",
       "0          2        10      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1          2        10      0.0      2.0      0.0      0.0      0.0      1.0   \n",
       "2          2        10      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "3          2        10      0.0      2.0      0.0      0.0      0.0      0.0   \n",
       "4          2        10      0.0      1.0      0.0      1.0      0.0      1.0   \n",
       "..       ...       ...      ...      ...      ...      ...      ...      ...   \n",
       "105        2        10      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "106        2        10      0.0      0.0      0.0      1.0      0.0      0.0   \n",
       "107        2        10      0.0      0.0      1.0      0.0      0.0      2.0   \n",
       "108        2        10      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "109        2        10      1.0      1.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "     Neuron6  Neuron7  ...  result  EventTarget_Onset  EventGo_cue  EventBump  \\\n",
       "0        0.0      0.0  ...    b'R'              False        False      False   \n",
       "1        0.0      0.0  ...    b'R'              False        False      False   \n",
       "2        0.0      0.0  ...    b'R'              False        False      False   \n",
       "3        0.0      0.0  ...    b'R'              False        False      False   \n",
       "4        1.0      0.0  ...    b'R'              False        False      False   \n",
       "..       ...      ...  ...     ...                ...          ...        ...   \n",
       "105      2.0      0.0  ...    b'R'              False        False      False   \n",
       "106      0.0      0.0  ...    b'R'              False        False      False   \n",
       "107      1.0      0.0  ...    b'R'              False        False      False   \n",
       "108      1.0      0.0  ...    b'R'              False        False      False   \n",
       "109      1.0      0.0  ...    b'R'              False        False      False   \n",
       "\n",
       "     target_dir  hand_vel_y  hand_vel_x  hand_pos_y  hand_pos_x  bump_dir  \n",
       "0      1.570796   -0.403976   -0.401545  -32.490878   -0.911772  4.712389  \n",
       "1      1.570796   -0.399243   -0.404625  -32.496644   -0.918049  4.712389  \n",
       "2      1.570796   -0.960029   -0.272455  -32.509032   -0.924836  4.712389  \n",
       "3      1.570796   -1.636682   -0.168425  -32.535650   -0.929261  4.712389  \n",
       "4      1.570796   -2.102847   -0.067119  -32.572998   -0.931526  4.712389  \n",
       "..          ...         ...         ...         ...         ...       ...  \n",
       "105    1.570796   28.724272   -7.871682  -24.989324    0.530218  4.712389  \n",
       "106    1.570796   23.008711  -10.761518  -24.470422    0.343102  4.712389  \n",
       "107    1.570796   17.195406  -12.802956  -24.069575    0.105206  4.712389  \n",
       "108    1.570796   12.520113  -13.389835  -23.774864   -0.158903  4.712389  \n",
       "109    1.570796   10.111746  -13.151919  -23.604688   -0.358865  4.712389  \n",
       "\n",
       "[110 rows x 181 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specifications\n",
    "dataset_id = 2\n",
    "animal = 1\n",
    "session = 2\n",
    "trial_id = 10\n",
    "dataframe = df10\n",
    "\n",
    "# Computes the conditions\n",
    "conditions = (\n",
    "    (dataframe['datasetID'] == dataset_id) &\n",
    "    (dataframe['animal'] == animal) &\n",
    "    (dataframe['session'] == session) &\n",
    "    (dataframe['trial_id'] == trial_id)\n",
    ")\n",
    "\n",
    "# generates a dataset with the desired trial\n",
    "trial_df = dataframe[conditions]\n",
    "trial_df = trial_df.reset_index(drop=True)\n",
    "trial_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3 Tuning Curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we want to visualise each neuron's activity versus some behaviour. To do so, first, let's shape our data to work with the Pynapple package. This step is characterised by converting the 0s and 1s from each neuron to a timestamp (nap.Ts). In this data format, we only register when the spike happens. After doing it for every neuron, we can use the information for different tuning curves, using the Pynapple tuning curve functions. Here are some applications: Tuning curve to analyse how each neuron acts with different velocities (either in the x direction or the y direction). We can also get the tuning curve of the activity versus the angle of movement. Finally, we could check all of these data at the same by making use of 2D tuning curves."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Tuning Curve 1D - Hand Velocity\n",
    "The following tuning curves check, per plot, only one behaviour. before ploting we need to prepare the data and turn it into the desired Pynapple format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specifications\n",
    "neuron = 10\n",
    "dataframe = df\n",
    "y_var = 'hand_vel_y'\n",
    "x_var = 'hand_vel_x'\n",
    "\n",
    "# Data preparation\n",
    "ts_dict = {}\n",
    "tsd_dict = {}\n",
    "\n",
    "# Extract spike timestamps and create Ts objects for each Neuron\n",
    "for i, column in enumerate(dataframe.columns):\n",
    "    if column.startswith('Neuron'):\n",
    "        timeSeries = nap.Tsd(t=np.array(dataframe[column].index)*bin, d=np.array(dataframe[column], dtype=int), time_units='ms')\n",
    "        spike_times = timeSeries.index[timeSeries.values > 0]\n",
    "        tsd_dict[i] = timeSeries\n",
    "        ts_dict[i] = nap.Ts(t=spike_times)\n",
    "\n",
    "# Groups all neurons information\n",
    "tsd_group = nap.TsGroup(tsd_dict)\n",
    "ts_group = nap.TsGroup(ts_dict)\n",
    "\n",
    "# Hand velocity over the x direction tuning curves\n",
    "feature_x = nap.Tsd(t=np.array(dataframe[x_var].index)*bin, d=np.array(dataframe[x_var]), time_units='ms')\n",
    "tune_x = nap.process.tuning_curves.compute_1d_tuning_curves(ts_group, feature_x, 30)\n",
    "\n",
    "# Hand velocity over the y direction tuning curves\n",
    "feature_y = nap.Tsd(t=np.array(df[y_var].index)*bin, d=np.array(df[y_var]), time_units='ms')\n",
    "tune_y = nap.process.tuning_curves.compute_1d_tuning_curves(ts_group, feature_y, 30)\n",
    "\n",
    "# Angle tuning curve \n",
    "index = np.array(dataframe[x_var].index)\n",
    "# Calculates the angle behaviour\n",
    "angle = np.arctan2(np.array(dataframe[y_var]), np.array(dataframe[x_var]))\n",
    "angle = nap.Tsd(index*bin, np.array(angle), time_units='ms')\n",
    "tune_angle = nap.process.tuning_curves.compute_1d_tuning_curves(ts_group, angle, 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have prepared all our calculations, let's visualize them!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x297d2fd10>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hand Velocity over the X Direction Tuning Curve Visualization\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.title(f\"Tuning Curve of Neuron {neuron} over Hand Velocity X\")\n",
    "plt.xlabel('Velocity (cm/s)')\n",
    "plt.ylabel('Frequency / Hz')\n",
    "plt.plot(tune_x.index, tune_x[neuron])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x297f19090>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hand Velocity over the Y Direction Tuning Curve Visualization\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.title(f\"Tuning Curve of Neuron {neuron} over Hand Velocity Y\")\n",
    "plt.xlabel('Velocity (cm/s)')\n",
    "plt.ylabel('Frequency / Hz')\n",
    "plt.plot(tune_y.index, tune_y[neuron])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x297cccb50>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hand Angle of Movement Tuning Curve Visualization\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.title(f\"Tuning Curve of Neuron {neuron} over Angle of Movement\")\n",
    "plt.xlabel('Angle (rad)')\n",
    "plt.ylabel('Frequency / Hz')\n",
    "plt.plot(tune_angle.index, tune_angle[neuron])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Tuning Curve 2D - Hand Velocity\n",
    "Again we can start by prearing the data to get the tuning information, by turning the data into Pynapples prefered format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specifications\n",
    "dataframe = df\n",
    "resolution = 30\n",
    "save = False\n",
    "x_var = 'hand_vel_x'\n",
    "y_var = 'hand_vel_y'\n",
    "\n",
    "# Extract spike timestamps and create Ts objects as the previous code does (for each neuron)\n",
    "ts_dict = {}\n",
    "for i, column in enumerate(dataframe.columns):\n",
    "    if column.startswith('Neuron'):\n",
    "        timeSeries = nap.Tsd(t=np.array(dataframe[column].index)*bin, d=np.array(dataframe[column], dtype=int), time_units='ms')\n",
    "        spike_times = timeSeries.index[timeSeries.values > 0]\n",
    "        ts_dict[i] = nap.Ts(t=spike_times)\n",
    "\n",
    "# Groups all spikes per neuron\n",
    "ts_group = nap.TsGroup(ts_dict)\n",
    "\n",
    "# Get the features in place (hand velocity) for the tuning\n",
    "feature_x = np.array(dataframe[x_var])\n",
    "feature_y = np.array(dataframe[y_var])\n",
    "index = np.array(dataframe[x_var].index)\n",
    "\n",
    "# Build up the features for angle and 2D tuning\n",
    "angle = np.arctan2(feature_y, feature_x)\n",
    "features = np.stack((feature_x, feature_y), axis=-1)\n",
    "\n",
    "# Get the features in the Pynapple desired format\n",
    "feature_frame = nap.TsdFrame(t=index*bin, d=np.array(features))\n",
    "feature_x = nap.Tsd(index*bin, feature_x, time_units='ms')\n",
    "feature_y = nap.Tsd(index*bin, feature_y, time_units='ms')\n",
    "angle = nap.Tsd(index*bin, np.array(angle), time_units='ms')\n",
    "\n",
    "# Generates all tuning curves data (if you run the previous cells aready you don't need to run the next 3 lines)\n",
    "tune_y = nap.process.tuning_curves.compute_1d_tuning_curves(ts_group, feature_y, resolution)\n",
    "tune_x = nap.process.tuning_curves.compute_1d_tuning_curves(ts_group, feature_x, resolution)\n",
    "tune_angle = nap.process.tuning_curves.compute_1d_tuning_curves(ts_group, angle, resolution)\n",
    "tune_2d = nap.process.tuning_curves.compute_2d_tuning_curves(ts_group, feature_frame, resolution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specifications\n",
    "neuron = 105\n",
    "\n",
    "# Extracts frequency and grid values (needed for the 2d  tuning)\n",
    "grids = np.meshgrid(tune_2d[1][0], tune_2d[1][1])\n",
    "grid_x, grid_y = grids\n",
    "value_2d = tune_2d[0][neuron]\n",
    "value_y = tune_y[neuron]\n",
    "value_x = tune_x[neuron]\n",
    "\n",
    "# Get relevant information for extra plot information\n",
    "minimum_x = np.min(grid_x)\n",
    "minimum_y = np.min(grid_y)\n",
    "maximum_x = np.max(grid_x)\n",
    "maximum_y = np.max(grid_y)\n",
    "hist_bound = np.max([np.abs(minimum_x), np.abs(minimum_y), np.abs(maximum_x), np.abs(maximum_y)])\n",
    "max_tunex = np.max(tune_x[neuron])\n",
    "max_tuney = np.max(tune_y[neuron])\n",
    "max_tuneangle = np.max(tune_angle[neuron])\n",
    "min_tuneangle = np.min(tune_angle[neuron])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Let's make a plot where we can visualize everything at the same time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Angle rad')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare the plot by breaking it into multiple grids\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "gs = gridspec.GridSpec(5, 5, width_ratios=[1, 1, 1, 1, 1], height_ratios=[1,1,1,1,1])\n",
    "gs.update(wspace=0.2, hspace=0.2)\n",
    "\n",
    "# |----- 2D Tuning Subplot -----|\n",
    "hist_subplot = fig.add_subplot(gs[2:4,1:3])\n",
    "im = hist_subplot.imshow(value_2d, origin='lower', extent=(minimum_x, maximum_x, minimum_y, maximum_y))\n",
    "\n",
    "# Gets the velocity vector where the activity is the highest\n",
    "max_locationF = np.nanargmax(value_2d)\n",
    "max_location = np.unravel_index(max_locationF, value_2d.shape)\n",
    "\n",
    "plt.scatter(grid_x[max_location], grid_y[max_location], c='white', marker='o', alpha=0.7, linewidths=1,\n",
    "               label=f'X: {np.round(grid_x[max_location],0)} cm/s\\nY: {np.round(grid_y[max_location],0)}cm/s')\n",
    "\n",
    "\n",
    "# Centers the data in a equal sided square\n",
    "plt.xlim((-hist_bound, hist_bound))\n",
    "plt.ylim((-hist_bound, hist_bound))\n",
    "\n",
    "# Define ticks to present\n",
    "xticks = np.round((np.arange(-hist_bound, hist_bound+1, (2*hist_bound)/4)))\n",
    "yticks = xticks\n",
    "\n",
    "# Configure the ticks\n",
    "plt.minorticks_on()\n",
    "plt.tick_params(direction='in')\n",
    "plt.tick_params(direction='in',which='minor', length=2.5, bottom=True, top=True, left=True, right=True)\n",
    "plt.tick_params(direction='in',which='major', length=5, bottom=True, top=True, left=True, right=True)\n",
    "plt.xticks(xticks)\n",
    "plt.yticks(yticks)\n",
    "\n",
    "#Label Managemnt\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "\n",
    "# |----- 2D Tuning Curve (Colorbar) -----|\n",
    "cb_subplot = fig.add_subplot(gs[2:4, 0])\n",
    "\n",
    "# Define the position and shrink the width\n",
    "pos = cb_subplot.get_position() \n",
    "new_pos = [pos.x0 + 0.09, pos.y0, pos.width * 0.25, pos.height] \n",
    "cb_subplot.set_position(new_pos) \n",
    "\n",
    "# Create the colorbar with the desired specs\n",
    "cb_max = np.nanmax(value_2d)\n",
    "cb_range = np.linspace(0, cb_max, 5)\n",
    "cbar = plt.colorbar(im, ticks=cb_range, label='Frequency Hz', cax=cb_subplot)\n",
    "cbar.ax.tick_params(labelsize=8)\n",
    "\n",
    "# Positions the ticks and labels in the correct side\n",
    "cbar.ax.yaxis.set_label_position('left') \n",
    "cbar.ax.yaxis.set_ticks_position('left')\n",
    "\n",
    "# Round colorbar tick labels to 2 decimal places\n",
    "cbar.ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "\n",
    "\n",
    "# |----- 1D Tuning X Subplot -----|\n",
    "x_subplot = fig.add_subplot(gs[1,1:3])\n",
    "x_subplot = plt.plot(tune_x.index, tune_x[neuron])\n",
    "\n",
    "# Define ticks to present\n",
    "yticks = np.round(np.arange(0, max_tunex+1, max_tunex/3),1)\n",
    "xticks = []\n",
    "\n",
    "# Configure the ticks\n",
    "plt.minorticks_on()\n",
    "plt.tick_params(direction='in')\n",
    "plt.tick_params(direction='in',which='minor', length=2.5, bottom=True, top=False, left=True, right=False)\n",
    "plt.tick_params(direction='in',which='major', length=5, bottom=True, top=False, left=True, right=False)\n",
    "plt.xticks(xticks)\n",
    "plt.yticks(yticks)\n",
    "\n",
    "#Label Managemnt\n",
    "plt.ylabel('Frequency Hz')\n",
    "plt.xlabel('Hand velocity over X cm/s')\n",
    "plt.title('Full Tuning Curve Analysis')\n",
    "\n",
    "\n",
    "\n",
    "#|----- 1D Tuning Y Subplot -----|\n",
    "y_subplot = fig.add_subplot(gs[2:4,3])\n",
    "y_subplot = plt.plot(tune_y[neuron], tune_y.index)\n",
    "\n",
    "# Define ticks to present\n",
    "yticks = []\n",
    "xticks = np.round(np.arange(0, max_tuney+1, max_tuney/3),1)\n",
    "\n",
    "# Configure the ticks\n",
    "plt.minorticks_on()\n",
    "plt.tick_params(direction='in')\n",
    "plt.tick_params(direction='in',which='minor', length=2.5, bottom=True, top=False, left=True, right=False)\n",
    "plt.tick_params(direction='in',which='major', length=5, bottom=True, top=False, left=True, right=False)\n",
    "plt.xticks(xticks)\n",
    "plt.yticks(yticks)\n",
    "\n",
    "#Label Managemnt\n",
    "plt.xlabel('Frequency Hz')\n",
    "plt.ylabel('Hand velocity over Y cm/s', rotation=270, labelpad=10, va='center')  \n",
    "\n",
    "\n",
    "\n",
    "# |----- Angle Plot ----|\n",
    "angle_subplot = fig.add_subplot(gs[4,1:3])\n",
    "angle_subplot = plt.plot(tune_angle.index, tune_angle[neuron])\n",
    "\n",
    "# Define ticks to present\n",
    "yticks = np.round(np.arange(min_tuneangle-0.5, max_tuneangle+1, max_tuneangle/3),1)\n",
    "xticks = []\n",
    "\n",
    "# Configure the ticks\n",
    "plt.minorticks_on()\n",
    "plt.tick_params(direction='in')\n",
    "plt.tick_params(direction='in',which='minor', length=2.5, bottom=True, top=False, left=True, right=False)\n",
    "plt.tick_params(direction='in',which='major', length=5, bottom=True, top=False, left=True, right=False)\n",
    "#plt.xticks(xticks)\n",
    "plt.yticks(yticks)\n",
    "\n",
    "#Label Managemnt\n",
    "plt.ylabel('Frequency Hz')\n",
    "plt.xlabel('Angle rad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}