In [0]:

    

import numpy as np
import pandas as pd
from scipy.stats import binom
import matplotlib.pyplot as plt
%matplotlib inline


    

In [2]:

    

rawdata = pd.read_csv('https://raw.githubusercontent.com/tomfaulkenberry/physNumComparisonTask/master/results/processed.csv')
data = rawdata.query('response_time < 2000')
data.head()


    

    
Out[2]:







  

    

      

      
congruity
      
correct
      
distance
      
leftStimulus
      
response
      
response_time
      
rightStimulus
      
subject_nr
    
  
  

    

      
0
      
incongruent
      
1
      
3
      
5
      
l
      
763
      
2
      
101
    
    

      
1
      
congruent
      
1
      
1
      
2
      
l
      
650
      
3
      
101
    
    

      
2
      
incongruent
      
1
      
4
      
7
      
l
      
654
      
3
      
101
    
    

      
3
      
congruent
      
1
      
4
      
3
      
l
      
488
      
7
      
101
    
    

      
4
      
congruent
      
1
      
3
      
4
      
l
      
438
      
7
      
101
    
  

In [10]:

    

fig, ax = plt.subplots()

ax.hist(data.response_time[data.congruity=='congruent'], bins=30, color='blue', edgecolor='black', label='Congruent trials')
ax.hist(data.response_time[data.congruity=='incongruent'], bins=30, color='white', edgecolor='black', alpha=0.8, label='Incongruent trials')

ax.set_xlabel('Response time (ms)', fontsize=14)
ax.legend()

plt.show()


    

In [0]:

    




    

In [0]:

    

binom.pmf(3, 10, 0.2)


    

    
Out[0]:





0.20132659200000022

In [0]:

    

fig, ax = plt.subplots()

Y = np.arange(0,10)
P = binom.pmf(Y, 10, 0.2)

ax.bar(Y,P)

ax.set_xlabel('$y$', fontsize=14)
ax.set_ylabel('$p(y\mid \\theta=0.2)$', fontsize=14)

plt.show()


    

In [0]:

    

fig, ax = plt.subplots()

Y = np.arange(0,10)
P = binom.pmf(Y, 10, 0.7)

ax.bar(Y,P)

ax.set_xlabel('$y$', fontsize=14)
ax.set_ylabel('$p(y\mid \\theta=0.7)$', fontsize=14)

plt.show()


    

In [0]:

    

def L(x):
  return(binom.pmf(7, 10, x))

fig, ax = plt.subplots(figsize=(8,5))
Theta = np.linspace(0,1,100)

ax.plot(Theta, L(Theta))

ax.set_xlabel('$\\theta$', fontsize=14)
ax.set_ylabel('$L(\\theta \mid y=7)$', fontsize=14)

plt.show()