perspective_taking_oscillatory_networks

# -*- coding: utf-8 -*-
"""
@author: Robert Seymour

Script to analyse the behavioural output of the perspective taking task.

It requires a CSV file in which all eprime data has been merged and pasted
into the same array.

"""

## Import required variables
import csv
import numpy as np
from matplotlib import pyplot as plt;

## Location of CSV files
csvfile = ''

output = [];

## Deliniarate the csv file
with open(csvfile) as f:
    for line in f:
        cells = line.split( "," )
        output.append( ( cells[ : ]) )

participant_list = sorted(['2','4','7','9','10','11','13','14','15','17','19','18','20','21','22','25','8','24'])

## Function to Extract RT info from the CSV file
def extract_RT(participant_list,output,perspective_taking_level, angle):

    # INPUTS:
    # - participant_list = list of particpants
    # output = CSV file
    # perspective_taking_level = 1 or 2
    # angle = 60 or 160

    # OUTPUTS:
    # RT_list    

    RT_list = [[] for x in xrange(len(participant_list))]
        # For every participant...          
    for p in range(0,len((participant_list)),1):
        # Iterate through list looking for matching condition
        for n in range(1,(len(output)),1):
            if output[n][1] == participant_list[p] and output[n][32] == '1' \
                     and output[n][23] == perspective_taking_level and \
                               output[n][30] == angle:
                # Put RT into the RT array
                RT_list[p].append(float(output[n][36]))

        # Remove RT cases 2SD from the median
        twostd_val = np.median(RT_list[p])+2*(np.std(RT_list[p]))       
        RT_list[p] = [x for x in RT_list[p] if x<twostd_val]
    return RT_list

## Extract RT
RT_LR_160 = extract_RT(participant_list,output,'vpt2proc', '160')
RT_LR_60 = extract_RT(participant_list,output,'vpt2proc', '60')
RT_VO_160 = extract_RT(participant_list,output,'vpt1proc', '160')
RT_VO_60 = extract_RT(participant_list,output,'vpt2proc', '60')

## Function to make a boxplot for participantxRT data for each condition
def make_boxplot_1(RT_scores,n,participant_list,title):
    plt.figure(n)
    plt.boxplot(RT_scores,labels = participant_list,showfliers=False)
    plt.title(title)
    plt.xlabel('Partipant Code')
    plt.ylabel('Reaction Time (msec)')
    plt.show()

make_boxplot_1(RT_LR_160,1,participant_list,'LR_160')
make_boxplot_1(RT_LR_60,2,participant_list,'LR_60')
make_boxplot_1(RT_VO_160,3,participant_list,'VO_160')
make_boxplot_1(RT_VO_60,4,participant_list,'VO_60')

## Get medians from each participant
def get_group_data(participant_list,RT_scores):
    data_median = []
    for nnn in range(0,len((participant_list)),1):
        data_median.append(np.median(RT_scores[nnn]))
    return data_median

data_LR_160_median = get_group_data(participant_list,RT_LR_160)
data_LR_60_median = get_group_data(participant_list,RT_LR_60)
data_VO_160_median = get_group_data(participant_list,RT_VO_160)
data_VO_60_median = get_group_data(participant_list,RT_VO_60)

data = [data_LR_160_median,data_LR_60_median, data_VO_160_median, data_VO_60_median]

## Show Bar-Graph
labels = ('LR_160','LR_60','VO_160','VO_60')
group_medians = [np.median(data[0]),np.median(data[1]),np.median(data[2]),np.median(data[3])]
y_pos = np.arange(len(group_medians))
plt.figure(5)
plt.bar(y_pos, group_medians, align='center', alpha=0.4)
plt.xticks(y_pos, labels)
plt.ylabel('RT (ms)')
plt.title('Group median RTs')

plt.show()

## Beeswarm Plot

plt.rcParams["font.family"] = "arial"

from beeswarm import *               

beeswarm([[],data_LR_160_median,data_LR_60_median,data_VO_160_median,data_VO_60_median], method="swarm", labels=["","data_LR_160", "data_LR_60","data_VO_160","data_VO_60"], col=["red","red","red","red","red"])
plt.ylabel('RT (msec)',fontsize = 15)
plt.tick_params(axis='both', which='major', labelsize=15)
bp = plt.boxplot(data,labels = ['LR-160','LR-60','VO-160','VO-60'],showfliers=False)

## change outline color, fill color and linewidth of the boxes
for box in bp['boxes']:
    # change outline color
    box.set( color='#000000', linewidth=1)
    # change fill color
    #box.set( facecolor = '#1b9e77' )

## change color and linewidth of the whiskers
for whisker in bp['whiskers']:
    whisker.set(color='#000000', linewidth=1)

## change color and linewidth of the caps
for cap in bp['caps']:
    cap.set(color='#2d3582', linewidth=1)

## change color and linewidth of the medians
for median in bp['medians']:
    median.set(color='#0000FF', linewidth=3)

## change the style of fliers and their fill
for flier in bp['fliers']:
    flier.set(marker='o', color='#000000', alpha=0.5)  

plt.savefig('all_conditions_boxplot_with_data.png',dpi=600)