Skip to content

2
2022-074
Commit cc7399bd authored by Anuththara18's avatar Anuththara18
Browse files
Options

Data Visualization Files Added

parent f353a9b0
Expand all Show whitespace changes
Inline Side-by-side
Showing with 54071 additions and 0 deletions
DataVisualization/BarPlots-Mean.html 0 → 100644
View file @ cc7399bd
This diff is collapsed.
DataVisualization/BarPlots-Mean.ipynb 0 → 100644
View file @ cc7399bd
This source diff could not be displayed because it is too large. You can view the blob instead.
DataVisualization/BoxPlots - Grouped.html 0 → 100644
View file @ cc7399bd
This diff is collapsed.
DataVisualization/BoxPlots - Grouped.ipynb 0 → 100644
View file @ cc7399bd
This source diff could not be displayed because it is too large. You can view the blob instead.
DataVisualization/DataVisualization.ipynb 0 → 100644
View file @ cc7399bd
This diff is collapsed.
DataVisualization/Mean Calculations & Box Plots.ipynb 0 → 100644
View file @ cc7399bd
This diff is collapsed.
DataVisualization/ScatterPlots-Group.ipynb 0 → 100644
View file @ cc7399bd
This source diff could not be displayed because it is too large. You can view the blob instead.
DataVisualization/ScatterPlots-Individual.ipynb 0 → 100644
View file @ cc7399bd
This source diff could not be displayed because it is too large. You can view the blob instead.
DataVisualization/ScatterPlots.html 0 → 100644
View file @ cc7399bd
This diff is collapsed.
DataVisualization/ScatterPlots.py 0 → 100644
View file @ cc7399bd
# -*- coding: utf-8 -*-
"""
E:\Education\SLIIT\Year 04\IT4010 - Research Project\19. DATA
@author: Anuththara
"""
import pandas as pd
import matplotlib.pyplot as plt
'''
correct_responses
commission_errors
omission_errors
mean_reaction_time
'''
###############################################################################
df1 = pd.read_csv("AA4.csv")
x = df1[['id']]
y = df1[['commission_errors']]
plt.scatter(x, y, c ="blue")
x2 = df1[['id']]
y2 = df1[['omission_errors']]
plt.scatter(x2, y2, c ="red")
plt.xlabel("ID")
plt.ylabel("Blue = CE, Red = OE")
plt.title("AA5 --> TCR = 19")
df1.plot(kind='scatter',x='id',y='correct_responses', c ="orange", title="AA4") # scatter plot
df1.plot(kind='scatter',x='id',y='mean_reaction_time', c ="green", title="AA4") # scatter plot
###############################################################################
df2 = pd.read_csv("AA5.csv")
df2.plot(kind='scatter',x='id',y='commission_errors', c ="blue") # scatter plot
x2 = df2[['id']]
y2 = df2[['omission_errors']]
plt.scatter(x2, y2, c ="red")
plt.xlabel("ID")
plt.ylabel("Blue = CE, Red = OE")
plt.title("AA5 --> TCR = 19")
df2.plot(kind='scatter',x='id',y='correct_responses', c ="orange", title="AA5") # scatter plot
df2.plot(kind='scatter',x='id',y='mean_reaction_time', c ="green", title="AA5") # scatter plot
DataVisualization/StimulusAnalysis.ipynb 0 → 100644
View file @ cc7399bd
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "26924545",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"id": "a46983af",
"metadata": {},
"source": [
"## Focused Attention"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a22f3ece",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"stimulus\n",
"cow 2\n",
"giraffe 1\n",
"lion 3\n",
"zebra 3\n",
"dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = pd.read_csv(r\"FA4.csv\")\n",
"df = data.loc[data['level'] == 3]\n",
"\n",
"occur = df.groupby(['stimulus']).size()\n",
"display(occur)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "62da490b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"stimulus\n",
"cow 1\n",
"dog 3\n",
"giraffe 2\n",
"zebra 1\n",
"dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = pd.read_csv(r\"FA5.csv\")\n",
"df = data.loc[data['level'] == 3]\n",
"occur = df.groupby(['stimulus']).size()\n",
"display(occur)"
]
},
{
"cell_type": "markdown",
"id": "7333ca15",
"metadata": {},
"source": [
"## Sustained Attention "
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "3979e4b1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"stimulus\n",
"pink 5\n",
"red 2\n",
"yellow 1\n",
"dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = pd.read_csv(r\"SuA4.csv\")\n",
"occur = df.groupby(['stimulus']).size()\n",
"display(occur)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "29596833",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"stimulus\n",
"blue 2\n",
"pink 5\n",
"red 2\n",
"dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = pd.read_csv(r\"SuA5.csv\")\n",
"occur = df.groupby(['stimulus']).size()\n",
"display(occur)"
]
},
{
"cell_type": "markdown",
"id": "260b5e44",
"metadata": {},
"source": [
"## Selective Attention "
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "ccebf218",
"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>percentage_no_of_correct_responses</th>\n",
" </tr>\n",
" <tr>\n",
" <th>stimulus</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>bee</th>\n",
" <td>88.541667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bird</th>\n",
" <td>97.564935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>butterfly</th>\n",
" <td>93.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>flower</th>\n",
" <td>94.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ladybird</th>\n",
" <td>92.682073</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" percentage_no_of_correct_responses\n",
"stimulus \n",
"bee 88.541667\n",
"bird 97.564935\n",
"butterfly 93.750000\n",
"flower 94.444444\n",
"ladybird 92.682073"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = pd.read_csv(r\"SA4.csv\")\n",
"\n",
"# standardizing the data\n",
"from sklearn.preprocessing import StandardScaler\n",
"scaler = StandardScaler()\n",
"data_scaled = scaler.fit_transform(df)\n",
"\n",
"# statistics of scaled data\n",
"pd.DataFrame(data_scaled).describe()\n",
"avg = df.groupby('stimulus')[['percentage_no_of_correct_responses']].mean()\n",
"display(avg)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "b8221fa7",
"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>percentage_no_of_correct_responses</th>\n",
" </tr>\n",
" <tr>\n",
" <th>stimulus</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>bear</th>\n",
" <td>91.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bee</th>\n",
" <td>92.063492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bird</th>\n",
" <td>98.484848</td>\n",
" </tr>\n",
" <tr>\n",
" <th>butterfly</th>\n",
" <td>94.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>flower</th>\n",
" <td>96.904762</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ladybird</th>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pig</th>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" percentage_no_of_correct_responses\n",
"stimulus \n",
"bear 91.666667\n",
"bee 92.063492\n",
"bird 98.484848\n",
"butterfly 94.444444\n",
"flower 96.904762\n",
"ladybird 100.000000\n",
"pig 100.000000"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = pd.read_csv(r\"SA5.csv\")\n",
"avg = df.groupby('stimulus')[['percentage_no_of_correct_responses']].mean()\n",
"display(avg)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8aff0262",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment