{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Workbook for Eye Tracking Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Basics\n",
"import numpy as np\n",
"import os\n",
"import math\n",
"\n",
"# Data processing\n",
"import pandas as pd\n",
"import awkward as ak\n",
"\n",
"# ML\n",
"import sklearn\n",
"\n",
"# Misc\n",
"from pathlib import Path\n",
"import pyarrow as pa\n",
"import urllib.request"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import users and their score"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Imports the user score information\n",
"user_data = pd.read_csv(r\"wetransfer_wtg-ar-daten_2023-05-31_1333/scores_WtG_PrePost.csv\", delimiter=\",\", usecols=[\"User\", \"Pre score\", \"Post score\", \"Difference\", \"Group cat\"])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Defines my directory with the user data\n",
"user_dir = 'wetransfer_wtg-ar-daten_2023-05-31_1333/WtG_AR_data_cleaned/with ET'"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Filters and drops non-relevant users\n",
"to_drop = []\n",
"for i, cat in enumerate(user_data[\"Group cat\"]):\n",
" if math.isnan(cat):\n",
" to_drop.append(i)\n",
"user_data = user_data.drop(to_drop)\n",
"user_data = user_data.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Filters and drops users with no directory\n",
"not_existing_names = []\n",
"for i, user in enumerate(user_data[\"User\"]):\n",
" if not os.path.isdir(user_dir + '/' + user):\n",
" not_existing_names.append(i)\n",
"user_data = user_data.drop(not_existing_names)\n",
"user_data = user_data.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<pre>[{level_0: 0, index: 0, User: '12C', 'Pre score': 1, 'Post score': 1, ...},\n",
" {level_0: 1, index: 1, User: '13C', 'Pre score': 1, 'Post score': 2, ...},\n",
" {level_0: 2, index: 2, User: '14A', 'Pre score': 1, 'Post score': 2, ...},\n",
" {level_0: 3, index: 3, User: '15A', 'Pre score': 1, 'Post score': 1, ...},\n",
" {level_0: 4, index: 4, User: '16A', 'Pre score': 1, 'Post score': 1, ...},\n",
" {level_0: 5, index: 5, User: '17A', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 6, index: 6, User: '19A', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 7, index: 7, User: '1A', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 8, index: 8, User: '20A', 'Pre score': 0, 'Post score': 0, ...},\n",
" {level_0: 9, index: 9, User: '21A', 'Pre score': 1, 'Post score': 1, ...},\n",
" ...,\n",
" {level_0: 31, index: 33, User: '62A', 'Pre score': 1, 'Post score': 2, ...},\n",
" {level_0: 32, index: 34, User: '64A', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 33, index: 35, User: '66A', 'Pre score': 1, 'Post score': 2, ...},\n",
" {level_0: 34, index: 36, User: '67A', 'Pre score': 1, 'Post score': 1, ...},\n",
" {level_0: 35, index: 37, User: '69B', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 36, index: 38, User: '70A', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 37, index: 39, User: '72B', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 38, index: 40, User: '74A', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 39, index: 41, User: '75A', 'Pre score': 2, 'Post score': 2, ...}]\n",
"-----------------------------------------------------------------------------\n",
"type: 36 * {\n",
" level_0: int64,\n",
" index: int64,\n",
" User: string,\n",
" "Pre score": float64,\n",
" "Post score": float64,\n",
" Difference: int64,\n",
" "Group cat": float64\n",
"}</pre>"
],
"text/plain": [
"<Array [{level_0: 0, index: 0, ...}, ...] type='36 * {level_0: int64, index...'>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert to awkward array\n",
"array_user = ak.zip(dict(user_data))\n",
"array_user"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import Eye Tracking data"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Creates dictionary with all the files for one user\n",
"file_names = {}\n",
"for user in user_data[\"User\"]:\n",
" #print(user)\n",
" available_files = os.listdir(user_dir + '/' + user)\n",
" #print(available_files)\n",
" file_names[user] = available_files\n",
"#file_names"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"36\n",
"36\n"
]
}
],
"source": [
"# Read each CSV file for one user, stored for each attempt\n",
"df_attempt1 = []\n",
"df_attempt2 = []\n",
"for user in user_data['User']:\n",
" files = file_names[user]\n",
" if len(files) == 2:\n",
" df_attempt1.append(pd.read_csv(user_dir + '/' + user + '/' + files[0], delimiter=\"\t\", usecols=[\"eyeDataTimestamp\", \"gazePointAOI_target_x\", \"gazePointAOI_target_y\"]))\n",
" df_attempt2.append(pd.read_csv(user_dir + '/' + user + '/' + files[1], delimiter=\"\t\", usecols=[\"eyeDataTimestamp\", \"gazePointAOI_target_x\", \"gazePointAOI_target_y\"]))\n",
" elif len(files) == 1:\n",
" df_attempt1.append(pd.read_csv(user_dir + '/' + user + '/' + files[0], delimiter=\"\t\", usecols=[\"eyeDataTimestamp\", \"gazePointAOI_target_x\", \"gazePointAOI_target_y\"]))\n",
" df_attempt2.append(pd.read_csv(user_dir + '/' + user + '/' + files[0], delimiter=\"\t\", usecols=[\"eyeDataTimestamp\", \"gazePointAOI_target_x\", \"gazePointAOI_target_y\"]))\n",
"print(len(df_attempt1))\n",
"print(len(df_attempt2))\n",
"#df_attempt1"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Read each CSV file in user_dir\n",
"#files = []\n",
"#dfs = []\n",
"#for file in Path(user_dir).glob(\"**/*.csv\"):\n",
"# files.append(file)\n",
"# dfs.append(pd.read_csv(file, delimiter=\"\t\", usecols=[\"eyeDataTimestamp\", \"gazePointAOI_target_x\", \"gazePointAOI_target_y\"]))\n",
"#dfs"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"[<Array [{eyeDataTimestamp: ..., ...}, ...] type='2429 * {eyeDataTimestamp: ...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='445 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='216 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='153 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='364 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='810 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='200 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='316 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='463 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='157 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='745 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='246 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='125 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='423 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='1418 * {eyeDataTimestamp: ...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='1553 * {eyeDataTimestamp: ...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='130 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='1074 * {eyeDataTimestamp: ...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='198 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='168 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='327 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='580 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='219 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='236 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='186 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='121 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='315 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='222 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='133 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='1251 * {eyeDataTimestamp: ...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='469 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='255 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='74 * {eyeDataTimestamp: in...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='785 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='316 * {eyeDataTimestamp: i...'>,\n",
" <Array [{eyeDataTimestamp: ..., ...}, ...] type='129 * {eyeDataTimestamp: i...'>]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert df_attempts to ak.Array\n",
"array_attempt1 = []\n",
"array_attempt2 = []\n",
"for df in df_attempt1:\n",
" array_attempt1.append(ak.Array(dict(df)))\n",
"for df in df_attempt2:\n",
" array_attempt2.append(ak.Array(dict(df)))\n",
"array_attempt1"
]
},
{
"cell_type": "markdown",
"metadata": {
"jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
"### Test with single .csv"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"df1 = pd.read_csv(user_dir + r\"/1A/2020_11_03-01_58_35-graph01-ET_planning-1A-Graph_Hololens.csv\", delimiter=\"\t\", usecols=[\"eyeDataTimestamp\", \"gazePointAOI_target_x\", \"gazePointAOI_target_y\"])\n",
"df2 = pd.read_csv(user_dir + r\"/1A/2020_11_03-01_59_11-graph01-ET_planning-1A-Graph_Hololens.csv\", delimiter=\"\t\", usecols=[\"eyeDataTimestamp\", \"gazePointAOI_target_x\", \"gazePointAOI_target_y\"])"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"tags": []
},
"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>eyeDataTimestamp</th>\n",
" <th>gazePointAOI_target_x</th>\n",
" <th>gazePointAOI_target_y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1604397462436</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1604397462469</td>\n",
" <td>0.13731</td>\n",
" <td>-0.23269</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1604397462502</td>\n",
" <td>0.18260</td>\n",
" <td>-0.20021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1604397462536</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1604397462569</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1468</th>\n",
" <td>1604397511370</td>\n",
" <td>0.04413</td>\n",
" <td>-0.20095</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1469</th>\n",
" <td>1604397511403</td>\n",
" <td>0.04517</td>\n",
" <td>-0.20161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1470</th>\n",
" <td>1604397511436</td>\n",
" <td>0.04554</td>\n",
" <td>-0.20434</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1471</th>\n",
" <td>1604397511470</td>\n",
" <td>0.04488</td>\n",
" <td>-0.20405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1472</th>\n",
" <td>1604397511503</td>\n",
" <td>0.04554</td>\n",
" <td>-0.20371</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1473 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" eyeDataTimestamp gazePointAOI_target_x gazePointAOI_target_y\n",
"0 1604397462436 NaN NaN\n",
"1 1604397462469 0.13731 -0.23269\n",
"2 1604397462502 0.18260 -0.20021\n",
"3 1604397462536 NaN NaN\n",
"4 1604397462569 NaN NaN\n",
"... ... ... ...\n",
"1468 1604397511370 0.04413 -0.20095\n",
"1469 1604397511403 0.04517 -0.20161\n",
"1470 1604397511436 0.04554 -0.20434\n",
"1471 1604397511470 0.04488 -0.20405\n",
"1472 1604397511503 0.04554 -0.20371\n",
"\n",
"[1473 rows x 3 columns]"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1"
]
},
{
"cell_type": "code",
"execution_count": 250,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<pre>[{eyeDataTimestamp: 1604397462436, gazePointAOI_target_x: nan, ...},\n",
" {eyeDataTimestamp: 1604397462469, gazePointAOI_target_x: 0.137, ...},\n",
" {eyeDataTimestamp: 1604397462502, gazePointAOI_target_x: 0.183, ...},\n",
" {eyeDataTimestamp: 1604397462536, gazePointAOI_target_x: nan, ...},\n",
" {eyeDataTimestamp: 1604397462569, gazePointAOI_target_x: nan, ...},\n",
" {eyeDataTimestamp: 1604397462602, gazePointAOI_target_x: nan, ...},\n",
" {eyeDataTimestamp: 1604397462636, gazePointAOI_target_x: nan, ...},\n",
" {eyeDataTimestamp: 1604397462669, gazePointAOI_target_x: nan, ...},\n",
" {eyeDataTimestamp: 1604397462702, gazePointAOI_target_x: nan, ...},\n",
" {eyeDataTimestamp: 1604397462736, gazePointAOI_target_x: nan, ...},\n",
" ...,\n",
" {eyeDataTimestamp: 1604397511236, gazePointAOI_target_x: 0.0384, ...},\n",
" {eyeDataTimestamp: 1604397511270, gazePointAOI_target_x: 0.0396, ...},\n",
" {eyeDataTimestamp: 1604397511303, gazePointAOI_target_x: 0.0416, ...},\n",
" {eyeDataTimestamp: 1604397511336, gazePointAOI_target_x: 0.043, ...},\n",
" {eyeDataTimestamp: 1604397511370, gazePointAOI_target_x: 0.0441, ...},\n",
" {eyeDataTimestamp: 1604397511403, gazePointAOI_target_x: 0.0452, ...},\n",
" {eyeDataTimestamp: 1604397511436, gazePointAOI_target_x: 0.0455, ...},\n",
" {eyeDataTimestamp: 1604397511470, gazePointAOI_target_x: 0.0449, ...},\n",
" {eyeDataTimestamp: 1604397511503, gazePointAOI_target_x: 0.0455, ...}]\n",
"-----------------------------------------------------------------------\n",
"type: 1473 * {\n",
" eyeDataTimestamp: int64,\n",
" gazePointAOI_target_x: float64,\n",
" gazePointAOI_target_y: float64\n",
"}</pre>"
],
"text/plain": [
"<Array [{eyeDataTimestamp: ..., ...}, ...] type='1473 * {eyeDataTimestamp: ...'>"
]
},
"execution_count": 250,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert to awkward array\n",
"array_column1 = ak.Array(dict(df1))\n",
"array_column2 = ak.Array(dict(df2))\n",
"array_column1"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#array_column2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data processing"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"### Add Eye Tracking Data to user data"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def add_column_old_broken(ak_array1, ak_array2, col_name):\n",
" entries = []\n",
" for entry, dataframe in zip(ak_array1, ak_array2):\n",
" entry_with_column = {**entry, col_name: dataframe}\n",
" print(entry_with_column)\n",
" entries.append(entry_with_column)\n",
" print(entries)\n",
" return ak.Array(entries)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def add_column_old(ak_array1, arrays, col_name):\n",
" return ak.zip({**{k: ak_array1[k] for k in ak_array1.fields}, col_name: arrays})"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Adds a list of arrays in a new column to an array\n",
"def add_column(ak_array, arrays, col_name):\n",
" combined_entries = [\n",
" {**{k: ak_array[k][i] for k in ak_array.fields}, col_name: array} for i, (entry, array) in enumerate(zip(ak_array, arrays))\n",
" ]\n",
" return ak.Array(combined_entries)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#[{k: array_user[i][k] for k in array_user.fields} for i in range(36)]"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<pre>[{level_0: 0, index: 0, User: '12C', 'Pre score': 1, 'Post score': 1, ...},\n",
" {level_0: 1, index: 1, User: '13C', 'Pre score': 1, 'Post score': 2, ...},\n",
" {level_0: 2, index: 2, User: '14A', 'Pre score': 1, 'Post score': 2, ...},\n",
" {level_0: 3, index: 3, User: '15A', 'Pre score': 1, 'Post score': 1, ...},\n",
" {level_0: 4, index: 4, User: '16A', 'Pre score': 1, 'Post score': 1, ...},\n",
" {level_0: 5, index: 5, User: '17A', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 6, index: 6, User: '19A', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 7, index: 7, User: '1A', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 8, index: 8, User: '20A', 'Pre score': 0, 'Post score': 0, ...},\n",
" {level_0: 9, index: 9, User: '21A', 'Pre score': 1, 'Post score': 1, ...},\n",
" ...,\n",
" {level_0: 31, index: 33, User: '62A', 'Pre score': 1, 'Post score': 2, ...},\n",
" {level_0: 32, index: 34, User: '64A', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 33, index: 35, User: '66A', 'Pre score': 1, 'Post score': 2, ...},\n",
" {level_0: 34, index: 36, User: '67A', 'Pre score': 1, 'Post score': 1, ...},\n",
" {level_0: 35, index: 37, User: '69B', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 36, index: 38, User: '70A', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 37, index: 39, User: '72B', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 38, index: 40, User: '74A', 'Pre score': 2, 'Post score': 2, ...},\n",
" {level_0: 39, index: 41, User: '75A', 'Pre score': 2, 'Post score': 2, ...}]\n",
"-----------------------------------------------------------------------------\n",
"type: 36 * {\n",
" level_0: int64,\n",
" index: int64,\n",
" User: string,\n",
" "Pre score": float64,\n",
" "Post score": float64,\n",
" Difference: int64,\n",
" "Group cat": float64,\n",
" Attempt1: var * {\n",
" eyeDataTimestamp: int64,\n",
" gazePointAOI_target_x: float64,\n",
" gazePointAOI_target_y: float64\n",
" },\n",
" Attempt2: var * {\n",
" eyeDataTimestamp: int64,\n",
" gazePointAOI_target_x: float64,\n",
" gazePointAOI_target_y: float64\n",
" }\n",
"}</pre>"
],
"text/plain": [
"<Array [{level_0: 0, index: 0, ...}, ...] type='36 * {level_0: int64, index...'>"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Creates array with first and second attempts added\n",
"array_data = add_column(array_user, array_attempt1, 'Attempt1')\n",
"array_data = add_column(array_data, array_attempt2, 'Attempt2')\n",
"array_data"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
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" {eyeDataTimestamp: 1604397536537, gazePointAOI_target_x: 0.0204, ...},\n",
" {eyeDataTimestamp: 1604397536570, gazePointAOI_target_x: 0.0288, ...},\n",
" {eyeDataTimestamp: 1604397536603, gazePointAOI_target_x: 0.032, ...},\n",
" {eyeDataTimestamp: 1604397536637, gazePointAOI_target_x: 0.0373, ...},\n",
" {eyeDataTimestamp: 1604397536670, gazePointAOI_target_x: 0.0376, ...},\n",
" {eyeDataTimestamp: 1604397536703, gazePointAOI_target_x: 0.0382, ...},\n",
" {eyeDataTimestamp: 1604397536737, gazePointAOI_target_x: 0.0351, ...},\n",
" {eyeDataTimestamp: 1604397536770, gazePointAOI_target_x: 0.0374, ...},\n",
" {eyeDataTimestamp: 1604397536803, gazePointAOI_target_x: 0.0418, ...},\n",
" ...,\n",
" {eyeDataTimestamp: 1604397546737, gazePointAOI_target_x: 0.0109, ...},\n",
" {eyeDataTimestamp: 1604397546770, gazePointAOI_target_x: 0.0113, ...},\n",
" {eyeDataTimestamp: 1604397546803, gazePointAOI_target_x: 0.0118, ...},\n",
" {eyeDataTimestamp: 1604397546837, gazePointAOI_target_x: 0.0111, ...},\n",
" {eyeDataTimestamp: 1604397546870, gazePointAOI_target_x: 0.0106, ...},\n",
" {eyeDataTimestamp: 1604397546903, gazePointAOI_target_x: 0.012, ...},\n",
" {eyeDataTimestamp: 1604397546937, gazePointAOI_target_x: 0.0133, ...},\n",
" {eyeDataTimestamp: 1604397546970, gazePointAOI_target_x: 0.0172, ...},\n",
" {eyeDataTimestamp: 1604397547003, gazePointAOI_target_x: 0.019, ...}]\n",
"-----------------------------------------------------------------------\n",
"type: 316 * {\n",
" eyeDataTimestamp: int64,\n",
" gazePointAOI_target_x: float64,\n",
" gazePointAOI_target_y: float64\n",
"}</pre>"
],
"text/plain": [
"<Array [{eyeDataTimestamp: ..., ...}, ...] type='316 * {eyeDataTimestamp: i...'>"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array_data['Attempt1'][7]"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ak.all(array_data['Attempt1']['eyeDataTimestamp'][0] == array_data['Attempt1']['eyeDataTimestamp'][1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"display_name": "Python 3 (ipykernel)",
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