Thesis-Hector-VR/User Study/TestResult/statistic.py

import glob
import os
import pandas as pd
import matplotlib.pyplot as plt
import time
import numpy as np
import seaborn as sns

path = os.getcwd()

def draw(filename):
    conditions = file['condition']
    result = file[filename]
    plt.figure(figsize=(9, 6), dpi=100)
    plt.bar(conditions, result, width=0.35, color=colors,alpha=a)
    plt.title(filename)
    plt.ylabel('score')
    plt.grid(alpha=0, linestyle=':')
    plt.savefig(filename + ".jpg", dpi=300)
    #plt.show()

def drawTogether():
    plt.figure(figsize=(20,7))
    x = np.arange(len(scales))
    total_width, n = 0.8, 4
    width = total_width / n
    
    for i in range(0,4):
        result = []
        for scale in scales:
            result.append(file.iloc[i][scale])
        plt.bar(x+width*(i-1),result,width=width,color=colors[i],label=file.iloc[i]["condition"],alpha=a)

    plt.legend()
    
    plt.xticks(x+width/2,scales)
    #plt.show()
    
    plt.savefig("summary.jpg",dpi=300)



# Merge all the .csv file 
all_files = glob.glob(os.path.join(path, "*.csv"))
df_from_each_file = (pd.read_csv(f, sep=',') for f in all_files)
df_merged = pd.concat(df_from_each_file, ignore_index=True)

# Save the file to Merged.csv in the same folder
df_merged.to_csv( "Merged.csv")

# save the results in csv
file = df_merged.groupby(["condition"]).mean() 
file.to_csv( "Mean.csv")


file = pd.read_csv("Mean.csv")
scales = ["Collision","Drive Distance","Total driving time","Adverage speed","Rescued Target", "Remained Visible Target","Remained Unvisible Target"]
colors = sns.color_palette()
a = 0.6
for scale in scales:
    draw(scale)

drawTogether()