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provo_analysis.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Jan 20 16:36:27 2023
@author: fm02
"""
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
data = pd.read_csv(r"C:\Users\fm02\Downloads\Provo_Corpus-Eyetracking_Data.csv")
data.columns
cols = ['Participant_ID', 'Word_Unique_ID', "Word", "Word_Length","OrthographicMatch",
"Word_Number", "Word_POS", "Word_Content_Or_Function", "IA_FIRST_FIX_PROGRESSIVE",
"POS_CLAWS", "IA_FIRST_FIXATION_DURATION","IA_FIRST_RUN_DWELL_TIME"]
data = data[cols]
path = "C:/Users/fm02/OwnCloud/Sentences/"
os.chdir(path)
conc = pd.read_csv("concreteness.csv", header=0, decimal = ',',
usecols = ["Word", "Conc.M"])
FREQ = pd.read_excel(r"C:\Users\fm02\Downloads\SUBTLEX-US.xlsx", header = 0, engine="openpyxl", \
usecols = ["Word", "FREQcount", "Zipf-value"])
data['POS_CLAWS'].value_counts()
nouns = ['NN1', 'NN2', 'NN0', 'NP0']
data = data[data['POS_CLAWS'].isin(nouns)]
# equivalent to data = data[data['Word_POS'] == "Noun"]
data['POS_CLAWS'].value_counts()
# NN1 38052
# NN2 14028
# NP0 3360
# NN0 588
data = data.drop(columns=['POS_CLAWS'])
data = pd.merge(data, FREQ, how='inner', on=['Word'])
data = pd.merge(data, conc, how='inner', on=['Word'])
###############################################################################
############################ PLOTS ############################################
###############################################################################
stimuli = pd.read_excel('C:/Users/fm02/OwnCloud/Sentences/stimuli_ALL.xlsx', engine='openpyxl')
stimuli.columns
# Out[57]:
# Index(['ID', 'Word', 'ConcM', 'V_MeanSum', 'A_MeanSum', 'mink3_SM', 'Sentence',
# 'Predictability', 'BLP_rt', 'BLP_accuracy', 'OLD20', 'LEN', 'Orth',
# 'UN2_F', 'UN3_F', 'FreqCount', 'LogFreq(Zipf)', 'similarity', 'AoA',
# 'cloze', 'plausibility', 'Position', 'Sim', 'PRECEDING_Frequency',
# 'PRECEDING_LogFreq(Zipf)', 'LENprec'],
# dtype='object')
stimuli = stimuli[['Word', 'ConcM', 'LogFreq(Zipf)', 'LEN', 'cloze', 'Sim' ]]
sns.distplot(stimuli['cloze'], bins=10); sns.distplot(data['OrthographicMatch'], bins=10)
plt.legend(['EOS', 'Provo'])
plt.show()
sns.distplot(stimuli['ConcM'], bins=[1,2,3,4,5]); sns.distplot(data['Conc.M'], bins=[1,2,3,4,5])
plt.legend(['EOS', 'Provo'])
plt.show()
sns.distplot(stimuli['LogFreq(Zipf)'], bins=10); sns.distplot(data['Zipf-value'], bins=10)
plt.legend(['EOS', 'Provo'])
plt.show()
sns.distplot(stimuli['LEN'], hist=False); sns.distplot(data['Word_Length'], hist=False)
plt.legend(['EOS', 'Provo'])
plt.show()
sns.pairplot(data[['Word_Length', 'Zipf-value', 'Conc.M', 'OrthographicMatch']])
plt.title('PROVO')
plt.show()
sns.pairplot(stimuli[['LEN', 'LogFreq(Zipf)', 'ConcM', 'cloze']])
plt.title('EOS')
plt.show()
uniq = data.drop_duplicates(subset=['Word'], keep='first')
scaler = StandardScaler()
provo_normalised = data.copy()
for col in ['Word_Length', 'OrthographicMatch', 'FREQcount', 'Zipf-value', 'Conc.M']:
scaler.fit(np.array(uniq[col]).reshape(-1,1))
provo_normalised[col] = scaler.transform(np.array(provo_normalised[col]).reshape(-1,1))
provo_normalised.to_csv(r'C:\Users\fm02\ownCloud\Manuscripts\EOS\provo_normalised.csv', index=False)
##############################################################################
data = pd.read_csv(r"C:\Users\fm02\Downloads\Provo_Corpus-Eyetracking_Data.csv")
data.columns
path = "C:/Users/fm02/OwnCloud/Sentences/"
os.chdir(path)
conc = pd.read_csv("concreteness.csv", header=0, decimal = ',',
usecols = ["Word", "Conc.M"])
FREQ = pd.read_excel(r"C:\Users\fm02\Downloads\SUBTLEX-US.xlsx", header = 0, engine="openpyxl", \
usecols = ["Word", "FREQcount", "Zipf-value"])
data['POS_CLAWS'].value_counts()
nouns = ['NN1', 'NN2', 'NN0', 'NP0']
data = data[data['Word_Content_Or_Function'] == "Content"]
# equivalent to data = data[data['Word_POS'] == "Noun"]
data['POS_CLAWS'].value_counts()
# NN1 38052
# NN2 14028
# NP0 3360
# NN0 588
data = data.drop(columns=['POS_CLAWS'])
data = pd.merge(data, FREQ, how='inner', on=['Word'])
data = pd.merge(data, conc, how='inner', on=['Word'])
data = data[data["IA_FIRST_FIX_PROGRESSIVE"]==1.0]
###############################################################################
[sns.distplot(data['Conc.M'][data['Word_POS']==key], label=key) for key in ['Adjective', 'Adverb', 'Noun', 'Verb']]
plt.legend()
plt.show()
[sns.distplot(data['OrthographicMatch'][data['Word_POS']==key], label=key) for key in ['Adjective', 'Adverb', 'Noun', 'Verb']]
plt.legend()
plt.show()
[sns.distplot(data['Word_Length'][data['Word_POS']==key], label=key) for key in ['Adjective', 'Adverb', 'Noun', 'Verb']]
plt.legend()
plt.show()
[sns.distplot(data['Zipf-value'][data['Word_POS']==key], label=key) for key in ['Adjective', 'Adverb', 'Noun', 'Verb']]
plt.legend()
plt.show()
sns.pairplot(stimuli[['LEN', 'LogFreq(Zipf)', 'ConcM', 'cloze']])
plt.title('EOS')
plt.show()
uniq = data.drop_duplicates(subset=['Word'], keep='first')
scaler = StandardScaler()
provo_normalised = data.copy()
for col in ['Word_Length', 'OrthographicMatch', 'FREQcount', 'Zipf-value', 'Conc.M']:
scaler.fit(np.array(uniq[col]).reshape(-1,1))
provo_normalised[col] = scaler.transform(np.array(provo_normalised[col]).reshape(-1,1))
provo_normalised.to_csv(r'C:\Users\fm02\ownCloud\Manuscripts\EOS\provo_normalised.csv', index=False)