tutorial_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from api_neurotask import *\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded from NeuroTask/2_10_Chowdhury_CObump.parquet with bin size of 10.0 ms\n",
      "Events columns: ['EventTarget_Onset', 'EventGo_cue', 'EventBump']\n",
      "Covariates columns: ['hand_pos_x', 'hand_pos_y', 'hand_vel_x', 'hand_vel_y', 'bump_dir', 'target_dir']\n"
     ]
    }
   ],
   "source": [
    "parquet_file_path = 'NeuroTask/2_10_Chowdhury_CObump.parquet'\n",
    "df,bin = load_and_filter_parquet(parquet_file_path,['A', 'I','F'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "   Neuron1  Neuron2  Neuron3  Neuron4  Neuron5  Neuron6  Neuron7  Neuron8  \\\n",
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Rebin Data for Analysis"
   ]
  },
  {
   "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": 4,
   "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": 5,
   "metadata": {},
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       "      <td>R</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>CenterOut_Bump</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>361898 rows × 182 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        session  trial_id  Neuron1  Neuron2  Neuron3  Neuron4  Neuron5  \\\n",
       "0             1         1        1        0        0        0        0   \n",
       "1             1         1        0        0        0        0        0   \n",
       "2             1         1        0        0        0        0        0   \n",
       "3             1         1        0        0        0        0        1   \n",
       "4             1         1        0        0        0        0        0   \n",
       "...         ...       ...      ...      ...      ...      ...      ...   \n",
       "361893        2      1386        0        1        0        1        0   \n",
       "361894        2      1386        1        0        0        1        0   \n",
       "361895        2      1386        0        0        0        1        0   \n",
       "361896        2      1386        0        0        0        0        0   \n",
       "361897        2      1386        0        0        0        3        0   \n",
       "\n",
       "        Neuron6  Neuron7  Neuron8  ...  hand_vel_y  result  bump_dir  \\\n",
       "0             0        0        1  ...   13.844993       R  1.570796   \n",
       "1             0        0        0  ...   12.197587       R  1.570796   \n",
       "2             1        0        0  ...   10.629333       R  1.570796   \n",
       "3             0        0        0  ...    9.039913       R  1.570796   \n",
       "4             0        0        0  ...    7.627443       R  1.570796   \n",
       "...         ...      ...      ...  ...         ...     ...       ...   \n",
       "361893        1        0        0  ...   59.294004       R  0.000000   \n",
       "361894        2        2        0  ...   57.145496       R  0.000000   \n",
       "361895        0        1        0  ...   51.964747       R  0.000000   \n",
       "361896        2        0        0  ...   44.039648       R  0.000000   \n",
       "361897        0        0        0  ...   33.624477       R  0.000000   \n",
       "\n",
       "        target_dir  EventTarget_Onset  EventGo_cue  EventBump  datasetID  \\\n",
       "0         0.000000              False        False      False          2   \n",
       "1         0.000000              False        False      False          2   \n",
       "2         0.000000              False        False      False          2   \n",
       "3         0.000000              False        False      False          2   \n",
       "4         0.000000              False        False      False          2   \n",
       "...            ...                ...          ...        ...        ...   \n",
       "361893    1.570796              False        False      False          2   \n",
       "361894    1.570796              False        False      False          2   \n",
       "361895    1.570796              False        False      False          2   \n",
       "361896    1.570796              False        False      False          2   \n",
       "361897    1.570796              False        False      False          2   \n",
       "\n",
       "        animal            task  \n",
       "0            1  CenterOut_Bump  \n",
       "1            1  CenterOut_Bump  \n",
       "2            1  CenterOut_Bump  \n",
       "3            1  CenterOut_Bump  \n",
       "4            1  CenterOut_Bump  \n",
       "...        ...             ...  \n",
       "361893       1  CenterOut_Bump  \n",
       "361894       1  CenterOut_Bump  \n",
       "361895       1  CenterOut_Bump  \n",
       "361896       1  CenterOut_Bump  \n",
       "361897       1  CenterOut_Bump  \n",
       "\n",
       "[361898 rows x 182 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Align to specific event"
   ]
  },
  {
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['EventTarget_Onset', 'EventGo_cue', 'EventBump']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[col for col in df.columns if col.startswith('Event')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = align_event(df10, 'EventTarget_Onset', bin_size=20,offset_min=-20,offset_max=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Neuron1</th>\n",
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       "      <th>...</th>\n",
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       "      <th>result</th>\n",
       "      <th>bump_dir</th>\n",
       "      <th>target_dir</th>\n",
       "      <th>EventTarget_Onset</th>\n",
       "      <th>EventGo_cue</th>\n",
       "      <th>EventBump</th>\n",
       "      <th>datasetID</th>\n",
       "      <th>animal</th>\n",
       "      <th>task</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>CenterOut_Bump</td>\n",
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       "      <td>1</td>\n",
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       "      <td>False</td>\n",
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       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.423443</td>\n",
       "      <td>R</td>\n",
       "      <td>1.570796</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>CenterOut_Bump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.570796</td>\n",
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       "      <td>2</td>\n",
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       "      <td>CenterOut_Bump</td>\n",
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       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>1</td>\n",
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       "      <td>CenterOut_Bump</td>\n",
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       "      <td>2</td>\n",
       "      <td>CenterOut_Bump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324439</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.231191</td>\n",
       "      <td>R</td>\n",
       "      <td>2.356194</td>\n",
       "      <td>3.926991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>CenterOut_Bump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324440</th>\n",
       "      <td>2</td>\n",
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       "      <td>R</td>\n",
       "      <td>2.356194</td>\n",
       "      <td>3.926991</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>CenterOut_Bump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324441</th>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.763108</td>\n",
       "      <td>R</td>\n",
       "      <td>2.356194</td>\n",
       "      <td>3.926991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>CenterOut_Bump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324442</th>\n",
       "      <td>2</td>\n",
       "      <td>1032</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.665809</td>\n",
       "      <td>R</td>\n",
       "      <td>2.356194</td>\n",
       "      <td>3.926991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>CenterOut_Bump</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11063 rows × 182 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        session  trial_id  Neuron1  Neuron2  Neuron3  Neuron4  Neuron5  \\\n",
       "116           1         1        1        0        0        0        0   \n",
       "117           1         1        1        1        0        0        0   \n",
       "118           1         1        0        0        0        0        0   \n",
       "119           1         1        0        0        1        0        0   \n",
       "378           1         2        0        0        0        0        0   \n",
       "...         ...       ...      ...      ...      ...      ...      ...   \n",
       "324168        2      1031        0        0        0        0        0   \n",
       "324439        2      1032        0        0        0        0        0   \n",
       "324440        2      1032        1        0        1        0        0   \n",
       "324441        2      1032        0        0        0        0        0   \n",
       "324442        2      1032        3        0        1        0        0   \n",
       "\n",
       "        Neuron6  Neuron7  Neuron8  ...  hand_vel_y  result  bump_dir  \\\n",
       "116           1        1        2  ...   -1.936864       R  1.570796   \n",
       "117           0        0        0  ...    1.249790       R  1.570796   \n",
       "118           1        0        1  ...    3.423443       R  1.570796   \n",
       "119           0        0        0  ...    4.001144       R  1.570796   \n",
       "378           0        0        1  ...    0.185450       R  0.000000   \n",
       "...         ...      ...      ...  ...         ...     ...       ...   \n",
       "324168        0        0        0  ...   -1.184138       R  0.000000   \n",
       "324439        0        0        0  ...   -0.231191       R  2.356194   \n",
       "324440        0        0        0  ...   -0.297402       R  2.356194   \n",
       "324441        0        0        0  ...   -0.763108       R  2.356194   \n",
       "324442        0        0        0  ...   -1.665809       R  2.356194   \n",
       "\n",
       "        target_dir  EventTarget_Onset  EventGo_cue  EventBump  datasetID  \\\n",
       "116       0.000000                NaN        False      False          2   \n",
       "117       0.000000               True         True      False          2   \n",
       "118       0.000000                NaN        False      False          2   \n",
       "119       0.000000                NaN        False      False          2   \n",
       "378       3.141593                NaN        False      False          2   \n",
       "...            ...                ...          ...        ...        ...   \n",
       "324168    3.926991                NaN        False      False          2   \n",
       "324439    3.926991                NaN        False      False          2   \n",
       "324440    3.926991               True         True      False          2   \n",
       "324441    3.926991                NaN        False      False          2   \n",
       "324442    3.926991                NaN        False      False          2   \n",
       "\n",
       "        animal            task  \n",
       "116          1  CenterOut_Bump  \n",
       "117          1  CenterOut_Bump  \n",
       "118          1  CenterOut_Bump  \n",
       "119          1  CenterOut_Bump  \n",
       "378          1  CenterOut_Bump  \n",
       "...        ...             ...  \n",
       "324168       2  CenterOut_Bump  \n",
       "324439       2  CenterOut_Bump  \n",
       "324440       2  CenterOut_Bump  \n",
       "324441       2  CenterOut_Bump  \n",
       "324442       2  CenterOut_Bump  \n",
       "\n",
       "[11063 rows x 182 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Data split and model's inputs processing"
   ]
  },
  {
   "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": 12,
   "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": 13,
   "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\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "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": 14,
   "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": 14,
     "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": [
    "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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "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",
    "\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)\n",
    "        event_positions.append(event_index)\n",
    "        event_labels.append(event_col)\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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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