• Download and install Anaconda for Package and Environment
• Conda package manager comes preinstalled. Here's a thorough walkthrough of package management with Conda
• In command prompt, use 'conda install [packagename]' to install relevant packages in your Python environment

1. Numpy
2. Scipy
3. Matplotlib
4. Pandas

• from future import print_function
• import json
• import csv
• import os
• import numpy as np
• import matplotlib.pyplot as plt
• import scipy
• import statsmodels
• from scipy import stats
• from operator import itemgetter, attrgetter, methodcaller
• from collections import OrderedDict
• from scipy.stats import ttest_ind, ttest_ind_from_stats
• from scipy.special import stdtr
• from statsmodels import +
• from decimal import Decimal

The data used for this project is saved in a file over 19.6GB in size before cleaning, ~9GB after cleaning. Not possible to include here.

Initial script used for exploration of data. Calculated actual and optimal distances for each user, saves them as well as efficiency (optimal/actual) in separate user files. No time distinction yet.

Calculates the optimal and actual distances for both x and y components of in lab and out of lab click sequences Writes these values from numpy arrays to 8 different CSV output files.

Reads data from reversed JSON file, outputs to user categorised CSV file.

Calculates time the cursor hovered over click point and writes it to individual output files for each user which it creates

Much more refined version of above deals with constraints and error values

Outputs all mouse events which took place on the day of each lab (the full 24 hour period)

Creates scatter plots of all Click Sequences by individual user. 128 plots created in all.

Reads in efficiency and time CSV files and plots efficiency using histograms and boxplots. Time used as a weight in histograms.

Creates scatter plot of mouse movement within both lab environments over time.

Reverses original log file

Kind of a rough work file

Histograms and box plots of Click Sequence time duration data

Individual user histograms of efficiency. Reads in JSON rather than CSV. Subsequently replaced.

Histograms and box plots of mouse hover time

Analysis of x axis mouse movement in lab environment

Analysis of y axis mouse movement in lab environment

Initial script used to clean the data, removes unnecessary fields and outputs data to individual user JSON files

Box plots of data in lab environment

Histogram of mouse activity on day of lab exams

individual user scatter plots of in lab data

Generates and outputs data from entire week of lab exam to CSV files

Creates Histograms of mouse activity over week of lab exams

Plots all mouse events on a scatter plot, recreates path of mouse movements. Only used to generate a sample, way too many to generate all of them.

Analysis of x axis mouse movement out of lab environment

Analysis of y axis mouse movement out of lab environment

Calculates overshoot for relevant lab times

Calculates overshoot for all click sequences

Graphs overshoot data

Creates data for mouse activity over entire semester

Graphs mouse movement activity over semester

Generates data for speed with time weighting

Graphs the above

Various graphs of time data

Graphs various efficiency graphs using different weightings

Generates all CSV files for metrics and outputs them to files for in or out of lab

Script used to do all hypothesis t-testing using Welch's t-test