diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..bc157d4 Binary files /dev/null and b/.DS_Store differ diff --git a/assignments/A01_Python_Functions-jhr3kd-Copy1.ipynb b/assignments/A01_Python_Functions-jhr3kd-Copy1.ipynb new file mode 100644 index 0000000..1259b95 --- /dev/null +++ b/assignments/A01_Python_Functions-jhr3kd-Copy1.ipynb @@ -0,0 +1,202 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment 1: Python Functions\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020\n", + "By: Per B. Sederberg, PhD\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Objectives\n", + "\n", + "Upon completion of this assignment, the student will be able to:\n", + "\n", + "1. Create and run a Python script\n", + "\n", + "2. Demonstrate the use of basic math and comparison operators\n", + "\n", + "3. Use conditional statements in Python\n", + "\n", + "4. Use the \"for\" statement to create loops\n", + "\n", + "5. Create custom functions, using parameters to make them generalizable\n", + "\n", + "6. Document code with in-code comments and docstrings\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment\n", + "\n", + "- Read Think Python chapters 3, 5, and 6 (Functions, Conditionals and Recursion, and Fruitful Functions). Be sure to open a new Jupyter Notebook and type in \n", + " the examples as you read through the text. You can also try the Exercises\n", + " at the end of each chapter.\n", + "\n", + "- After you have read the chapters, write code in *this notebook* (***after making a copy and renaming it to have your userid in the title --- e.g., A01_Python_Functions_mst3k***) that performs the \n", + " following tasks:\n", + "\n", + " 1. Get two numbers from the user \n", + " 2. Compare the numbers\n", + " 3. If the first number is smaller than the second, calculate the mean\n", + " 4. If the first number is larger than the second, calculate the difference\n", + " 5. If the two numbers are the same, calculate the factorial using a custom function\n", + " 6. Display the results (indicating what operation you did)\n", + "\n", + "- Test your code and debug as necessary.\n", + "\n", + "- ***When you are done, save this notebook as HTML (`File -> Download as -> HTML`) and upload it to the matching assignment on UVACollab.***\n", + "\n", + "## HINTS\n", + "\n", + "- Be sure to comment your code\n", + "- Make use of the `input` function to get numbers (see examples from class)\n", + "- Use the template below to calculate the factorial using a non-recursive\n", + " function\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Name: Jerome Romualdez\n", + "# User ID: JHR3KD" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# ***finish this function replacing the `pass` with your code***\n", + "def factorial(n):\n", + " \"\"\"\n", + " Takes in a positive integer n and returns n!\n", + " \"\"\"\n", + " #***Did not include the input check and type check because I already have checks for those before I make the function call\n", + " # not needed already made check prior to call\n", + " # make sure the input is an integer greater than or equal to 1\n", + " if type(n) is not int:\n", + " pass # not needed already made check prior to call\n", + " \n", + " \n", + " # Initialize a variable to 1\n", + " y = 1\n", + " # Successively multiply the variable by the integers 2 up to n\n", + " #Multiplying by x + 1 because counting startes at 0\n", + " for x in range(n):\n", + " y *= (x+1)\n", + " \n", + " # return the result\n", + " return y\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter a number: 4\n", + "Enter another number: 4\n", + "The factorial of 4 is 24\n" + ] + } + ], + "source": [ + "# ***your code here for reading in the numbers and operating correctly***\n", + "try:\n", + " x = int(input(\"Enter a number: \"))\n", + " y = int(input(\"Enter another number: \"))\n", + " if(x < 1 or y < 1):\n", + " raise Exception() \n", + " # first input is smaller than second so get mean\n", + " if(x < y):\n", + " mean = ((x+y)/2)\n", + " print(\"The mean of the two numbers is \" + str(mean))\n", + " #second input is smaller than first so get difference\n", + " elif (y < x):\n", + " diff = (x - y)\n", + " print(\"The difference of the two numbers is \" + str(diff))\n", + " #inputs are the same so call factorial()\n", + " else:\n", + " print(\"The factorial of \" + str(x) + \" is \" + str(factorial(x)))\n", + "except:\n", + " # if the input is not an int or is an int less than 1 display an error message\n", + " print(\"Please enter a positive integer!\")\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge problem (not required, but fun!)\n", + "\n", + "- Re-write the factorial function using a recursive algorithm.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "def factorial(n):\n", + " \"\"\"\n", + " Takes in a positive integer n and returns n!\n", + " Reccursive method\n", + " \"\"\"\n", + " #base case\n", + " if (n <= 1):\n", + " return 1\n", + " #recursive case\n", + " else: return factorial(n-1)* n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/assignments/A01_Python_Functions-jhr3kd.html b/assignments/A01_Python_Functions-jhr3kd.html new file mode 100644 index 0000000..e953ac0 --- /dev/null +++ b/assignments/A01_Python_Functions-jhr3kd.html @@ -0,0 +1,13289 @@ + + + + +A01_Python_Functions-jhr3kd + + + + + + + + + + + + + + + + + + + + + + +
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+

Assignment 1: Python Functions

Computational Methods in Psychology (and Neuroscience)

Psychology 4500/7559 --- Fall 2020

By: Per B. Sederberg, PhD

+ +
+
+
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+
+
+

Objectives

Upon completion of this assignment, the student will be able to:

+
    +

  1. Create and run a Python script

    +
    2. +

  2. Demonstrate the use of basic math and comparison operators

    +
    3. +

  3. Use conditional statements in Python

    +
    4. +

  4. Use the "for" statement to create loops

    +
    5. +

  5. Create custom functions, using parameters to make them generalizable

    +
    6. +

  6. Document code with in-code comments and docstrings

    +
    7. +
+ +
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+
+
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+

Assignment

    +

  • Read Think Python chapters 3, 5, and 6 (Functions, Conditionals and Recursion, and Fruitful Functions). Be sure to open a new Jupyter Notebook and type in +the examples as you read through the text. You can also try the Exercises +at the end of each chapter.

    +
    • +

  • After you have read the chapters, write code in this notebook (after making a copy and renaming it to have your userid in the title --- e.g., A01_Python_Functions_mst3k) that performs the +following tasks:

    +
      +
    1. Get two numbers from the user
      2. +
    2. Compare the numbers
      3. +
    3. If the first number is smaller than the second, calculate the mean
      4. +
    4. If the first number is larger than the second, calculate the difference
      5. +
    5. If the two numbers are the same, calculate the factorial using a custom function
      6. +
    6. Display the results (indicating what operation you did)
      7. +
    +
    • +

  • Test your code and debug as necessary.

    +
    • +

  • When you are done, save this notebook as HTML (File -> Download as -> HTML) and upload it to the matching assignment on UVACollab.

    +
    • +
+

HINTS

    +
  • Be sure to comment your code
    • +
  • Make use of the input function to get numbers (see examples from class)
    • +
  • Use the template below to calculate the factorial using a non-recursive +function
    • +
+ +
+
+
+
+
+
+

Name: Jerome Romualdez

User ID: JHR3KD

+
+
+
+
+
+
In [6]:
+
+
+
# ***finish this function replacing the `pass` with your code***
+def factorial(n):
+    """
+    Takes in a positive integer n and returns n!
+    """
+    #***Did not include the input check and type check because I already have checks for those  before I make the function call
+    # not needed already made check prior to call
+    # make sure the input is an integer greater than or equal to 1
+    if type(n) is not int:
+        pass # not needed already made check prior to call
+        
+    
+    # Initialize a variable to 1
+    y = 1
+    # Successively multiply the variable by the integers 2 up to n
+    #Multiplying by x + 1 because counting startes at 0
+    for x in range(n):
+        y *= (x+1)
+    
+    # return the result
+    return y
+    
+
+ +
+
+
+ +
+
+
+
In [8]:
+
+
+
# ***your code here for reading in the numbers and operating correctly***
+try:
+    x = int(input("Enter a number: "))
+    y = int(input("Enter another number: "))
+    if(x < 1 or y < 1):
+        raise Exception() 
+    # first input is smaller than second so get mean
+    if(x < y):
+        mean = ((x+y)/2)
+        print("The mean of the two numbers is " + str(mean))
+    #second input is smaller than first so get difference
+    elif (y < x):
+        diff = (x - y)
+        print("The difference of the two numbers is " + str(diff))
+    #inputs are the same so call factorial()
+    else:
+        print("The factorial of " + str(x) + " is " + str(factorial(x)))
+except:
+    # if the input is not an int or is an int less than 1 display an error message
+    print("Please enter a positive integer!")
+
+
+    
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Enter a number: 7
+Enter another number: 7
+The factorial of 7 is 5040
+
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+ +
+
+ +
+
+
+
+

Challenge problem (not required, but fun!)

    +
  • Re-write the factorial function using a recursive algorithm.
    • +
+ +
+
+
+
+
+
In [59]:
+
+
+
def factorial(n):
+    """
+    Takes in a positive integer n and returns n!
+    Reccursive method
+    """
+    #base case
+    if (n <= 1):
+        return 1
+    #recursive case
+    else: return factorial(n-1)* n
+
+ +
+
+
+ +
+
+
+
In [ ]:
+
+
+
 
+
+ +
+
+
+ +
+
+
+ + + + + + diff --git a/assignments/A01_Python_Functions-jhr3kd.ipynb b/assignments/A01_Python_Functions-jhr3kd.ipynb new file mode 100644 index 0000000..1259b95 --- /dev/null +++ b/assignments/A01_Python_Functions-jhr3kd.ipynb @@ -0,0 +1,202 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment 1: Python Functions\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020\n", + "By: Per B. Sederberg, PhD\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Objectives\n", + "\n", + "Upon completion of this assignment, the student will be able to:\n", + "\n", + "1. Create and run a Python script\n", + "\n", + "2. Demonstrate the use of basic math and comparison operators\n", + "\n", + "3. Use conditional statements in Python\n", + "\n", + "4. Use the \"for\" statement to create loops\n", + "\n", + "5. Create custom functions, using parameters to make them generalizable\n", + "\n", + "6. Document code with in-code comments and docstrings\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment\n", + "\n", + "- Read Think Python chapters 3, 5, and 6 (Functions, Conditionals and Recursion, and Fruitful Functions). Be sure to open a new Jupyter Notebook and type in \n", + " the examples as you read through the text. You can also try the Exercises\n", + " at the end of each chapter.\n", + "\n", + "- After you have read the chapters, write code in *this notebook* (***after making a copy and renaming it to have your userid in the title --- e.g., A01_Python_Functions_mst3k***) that performs the \n", + " following tasks:\n", + "\n", + " 1. Get two numbers from the user \n", + " 2. Compare the numbers\n", + " 3. If the first number is smaller than the second, calculate the mean\n", + " 4. If the first number is larger than the second, calculate the difference\n", + " 5. If the two numbers are the same, calculate the factorial using a custom function\n", + " 6. Display the results (indicating what operation you did)\n", + "\n", + "- Test your code and debug as necessary.\n", + "\n", + "- ***When you are done, save this notebook as HTML (`File -> Download as -> HTML`) and upload it to the matching assignment on UVACollab.***\n", + "\n", + "## HINTS\n", + "\n", + "- Be sure to comment your code\n", + "- Make use of the `input` function to get numbers (see examples from class)\n", + "- Use the template below to calculate the factorial using a non-recursive\n", + " function\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Name: Jerome Romualdez\n", + "# User ID: JHR3KD" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# ***finish this function replacing the `pass` with your code***\n", + "def factorial(n):\n", + " \"\"\"\n", + " Takes in a positive integer n and returns n!\n", + " \"\"\"\n", + " #***Did not include the input check and type check because I already have checks for those before I make the function call\n", + " # not needed already made check prior to call\n", + " # make sure the input is an integer greater than or equal to 1\n", + " if type(n) is not int:\n", + " pass # not needed already made check prior to call\n", + " \n", + " \n", + " # Initialize a variable to 1\n", + " y = 1\n", + " # Successively multiply the variable by the integers 2 up to n\n", + " #Multiplying by x + 1 because counting startes at 0\n", + " for x in range(n):\n", + " y *= (x+1)\n", + " \n", + " # return the result\n", + " return y\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter a number: 4\n", + "Enter another number: 4\n", + "The factorial of 4 is 24\n" + ] + } + ], + "source": [ + "# ***your code here for reading in the numbers and operating correctly***\n", + "try:\n", + " x = int(input(\"Enter a number: \"))\n", + " y = int(input(\"Enter another number: \"))\n", + " if(x < 1 or y < 1):\n", + " raise Exception() \n", + " # first input is smaller than second so get mean\n", + " if(x < y):\n", + " mean = ((x+y)/2)\n", + " print(\"The mean of the two numbers is \" + str(mean))\n", + " #second input is smaller than first so get difference\n", + " elif (y < x):\n", + " diff = (x - y)\n", + " print(\"The difference of the two numbers is \" + str(diff))\n", + " #inputs are the same so call factorial()\n", + " else:\n", + " print(\"The factorial of \" + str(x) + \" is \" + str(factorial(x)))\n", + "except:\n", + " # if the input is not an int or is an int less than 1 display an error message\n", + " print(\"Please enter a positive integer!\")\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge problem (not required, but fun!)\n", + "\n", + "- Re-write the factorial function using a recursive algorithm.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "def factorial(n):\n", + " \"\"\"\n", + " Takes in a positive integer n and returns n!\n", + " Reccursive method\n", + " \"\"\"\n", + " #base case\n", + " if (n <= 1):\n", + " return 1\n", + " #recursive case\n", + " else: return factorial(n-1)* n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/assignments/A01_Python_Functions.ipynb b/assignments/A01_Python_Functions.ipynb index 27718f2..d0d56f4 100644 --- a/assignments/A01_Python_Functions.ipynb +++ b/assignments/A01_Python_Functions.ipynb @@ -140,7 +140,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.7" } }, "nbformat": 4, diff --git a/assignments/A02_GitHub.ipynb b/assignments/A02_GitHub.ipynb index 65628ac..7759353 100644 --- a/assignments/A02_GitHub.ipynb +++ b/assignments/A02_GitHub.ipynb @@ -130,7 +130,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.7" } }, "nbformat": 4, diff --git a/assignments/A03_Manipulating_Lists-jhr3kd.ipynb b/assignments/A03_Manipulating_Lists-jhr3kd.ipynb new file mode 100644 index 0000000..7fbeb47 --- /dev/null +++ b/assignments/A03_Manipulating_Lists-jhr3kd.ipynb @@ -0,0 +1,170 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment 3: Manipulating Lists\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Objectives\n", + "\n", + "Upon completion of this exercise, the student will be able to:\n", + "\n", + "1. Create and manipulate lists\n", + "\n", + "2. Iterate over lists\n", + "\n", + "3. Demonstrate the use of lists in functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment\n", + "\n", + "* Write code in this notebook (after making a copy and renaming it to have your userid in the title --- e.g., A03_Manipulating_Lists_mst3k) that performs the following tasks:\n", + "\n", + " 1. Take in a list of numbers from the user.\n", + " 2. Store those numbers as a list.\n", + " 3. Write a function that takes in a list and sorts it.\n", + " 4. Print out the sorted list\n", + " \n", + "* Test your script and debug as necessary. You can compare your output with the `sort` method built into the list object.\n", + "\n", + "* Hint: You can look at the Wikipedia entry for [Bubble Sort](https://en.wikipedia.org/wiki/Bubble_sort) for one\n", + " possible sorting algorithm.\n", + " \n", + "* ***When you are done, save this notebook as HTML (`File -> Download as -> HTML`) and upload it to the matching assignment on UVACollab.*** " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Here are some comments to get you started!\n", + "# You can fill in your code here.\n", + "\n", + "def my_sort(my_list):\n", + " temp = 0\n", + " for j in range(len(my_list)):\n", + " for i in range(len(my_list)-1):\n", + " if(my_list[i] >= my_list[i+1]):\n", + " temp = my_list[i]\n", + " my_list[i] = my_list[i+1]\n", + " my_list[i+1] = temp\n", + "\n", + "def my_sort_rev(my_list):\n", + " temp = 0\n", + " for j in range(len(my_list)):\n", + " for i in range(len(my_list)-1):\n", + " if(my_list[i] <= my_list[i+1]):\n", + " temp = my_list[i]\n", + " my_list[i] = my_list[i+1]\n", + " my_list[i+1] = temp\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input a list size:9\n", + "Enter list item #1:1\n", + "Enter list item #2:1\n", + "Enter list item #3:9\n", + "Enter list item #4:9\n", + "Enter list item #5:1\n", + "Enter list item #6:2\n", + "Enter list item #7:3\n", + "Enter list item #8:3\n", + "Enter list item #9:9\n", + "[1, 1, 9, 9, 1, 2, 3, 3, 9]\n", + "Enter 'up' for ascending or 'down' for descending: up\n", + "[1, 1, 1, 2, 3, 3, 9, 9, 9]\n" + ] + } + ], + "source": [ + "# Write code to test your script here\n", + "\n", + "# Collect numbers with an `input` method\n", + "# You can have them enter the items as a comma-separated list\n", + "# Or call `input` in a loop\n", + "# Convert the input into a list\n", + "try:\n", + " size = int(input(\"Input a list size:\"))\n", + " my_list = []\n", + " for i in range(size):\n", + " x = int(input(\"Enter list item #\" + str(i + 1) + \":\"))\n", + " my_list.append(x)\n", + " print(my_list)\n", + " \n", + " direction = input(\"Enter 'up' for ascending or 'down' for descending: \")\n", + " if (direction == \"up\"):\n", + " my_sort(my_list)\n", + " print(my_list)\n", + " elif (direction ==\"down\"):\n", + " my_sort_rev(my_list)\n", + " print(my_list)\n", + " else:\n", + " print(\"Did not enter a valid direction please try again\")\n", + "except:\n", + " print(\"Pls enter a valid integer for list size and list inputs!\")\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge (for advanced students)\n", + "\n", + "* Try allowing the user to sort in ascending or descending order!\n", + "* Write the sort function recursively." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/assignments/A03_Manipulating_Lists.ipynb b/assignments/A03_Manipulating_Lists.ipynb index 86d4d27..f3a84f9 100644 --- a/assignments/A03_Manipulating_Lists.ipynb +++ b/assignments/A03_Manipulating_Lists.ipynb @@ -107,7 +107,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.7" } }, "nbformat": 4, diff --git a/assignments/A04_ListGen-jhr3kd.ipynb b/assignments/A04_ListGen-jhr3kd.ipynb new file mode 100644 index 0000000..b12ae83 --- /dev/null +++ b/assignments/A04_ListGen-jhr3kd.ipynb @@ -0,0 +1,5281 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment 4: List Generation for Experiments\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Objectives\n", + "\n", + "Upon completion of this assignment, the student will have:\n", + "\n", + "1. Read in a stimulus pool from a file.\n", + "\n", + "2. Randomly generated lists to use in a experiment.\n", + "\n", + "3. Written the lists out to files for future use.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment\n", + "\n", + "* Write code in a Jupyter notebook (after making a copy and renaming it to have your userid in the title --- e.g., A04_ListGen_mst3k).\n", + "\n", + "## Design\n", + "\n", + "Your assignment is to write a script that reads in a pool of stimuli\n", + "and creates lists of dictionaries that you will later present to\n", + "participants as part of an experiment. \n", + "\n", + "The script should be configurable such that you can specify different\n", + "numbers of lists and trials, along with other details specific to the\n", + "experiment you decide to do.\n", + "\n", + "Each dictionary represents a trial and should contain all the\n", + "information necessary to identify the stimulus to be presented,\n", + "details about that stimulus, and the condition in which to present it.\n", + "This information will be experiment-specific, as outlined below.\n", + "\n", + "You have three options for your experiment. Please select **one** of\n", + "the following experiments, keeping in mind that your next assignment\n", + "will be to code the experiment presentation and response collection\n", + "for the lists you generate from this assignment.\n", + "\n", + " \n", + "* ***When you are done, save this notebook as HTML (`File -> Download as -> HTML`) and upload it to the matching assignment on UVACollab.*** " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Option 1: Valence Study\n", + "\n", + "The main question of this study is whether recognition memory for\n", + "words depends on the emotional or affective valence of those words.\n", + "Participants will study lists of positive (+), negative (-), and\n", + "neutral (~) words and then, after a short delay, they will be given a\n", + "recognition test over all the studied target words plus a matched set\n", + "of non-studied lures. The stimuli are contained in three separate CSV\n", + "files:\n", + "\n", + "- [Positive Pool](./pos_pool.csv)\n", + "- [Negative Pool](./neg_pool.csv)\n", + "- [Neutral Pool](./neu_pool.csv)\n", + "\n", + "You will need to read these files in as lists of dictionaries (hint,\n", + "use the ``DictReader`` from the ``csv`` module that was covered in\n", + "class.) Use these pools to create lists of trials for two\n", + "experimental conditions: pure or mixed. In the *pure* condition,\n", + "all of the trials should be words from the same valence (be sure to\n", + "have the same number of positive, negative, and neutral pure lists.)\n", + "In the *mixed* condition, each list should contain an equal number of\n", + "positive, negative, and neutral words in *random* order (hint, use the\n", + "``shuffle`` function provided by the ``random`` module.) \n", + "\n", + "You will need to generate a matching test list for each study list\n", + "that includes all the studied items, plus a set of lures that match\n", + "the valence of the studied words.\n", + "\n", + "Be sure to add in information to each trial dictionary that identifies\n", + "the word, its valence, the condition of the list, and whether it is a\n", + "target or a lure. Feel free to add in more information if you would\n", + "like.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "import random\n", + "\n", + "pos_pool = csv.DictReader(open('pos_pool.csv', 'r'))\n", + "neg_pool = csv.DictReader(open('neg_pool.csv', 'r'))\n", + "neu_pool = csv.DictReader(open('neu_pool.csv', 'r'))\n", + "pos_list=[]\n", + "\n", + "'''\n", + "Creating a starting list of dicts for each valence. \n", + "Each dict will contain the stimulus and its valence\n", + "'''\n", + "for l in pos_pool:\n", + " x = {}\n", + " x['stimulus'] = l['description']\n", + " x['valence'] = 'POS'\n", + " pos_list.append(x)\n", + "neg_list =[]\n", + "for l in neg_pool:\n", + " x = {}\n", + " x['stimulus'] = l['description']\n", + " x['valence'] = 'NEG'\n", + " neg_list.append(x)\n", + "neu_list=[]\n", + "for l in neu_pool:\n", + " x = {}\n", + " x['stimulus'] = l['description']\n", + " x['valence'] = 'NEU'\n", + " neu_list.append(x)\n", + "\n", + " \n", + "#shuffle three lists before creating study and test lists\n", + "\n", + "random.shuffle(pos_list)\n", + "random.shuffle(neg_list)\n", + "random.shuffle(neu_list)\n", + "\n", + "'''\n", + "Function for creating all pure dicts(Positive, Negative and Neutral). \n", + "Takes in a nunber of study items and a pool list. Pool lists created above ^. \n", + "Starts by generating the study and test lists for the pure dict\n", + "then inserts these lists into the dict\n", + "'''\n", + "def gen_pure(num_study, pool_list):\n", + " pure_study = []\n", + " pure_test = []\n", + " pure = {} \n", + " #for loop takes in number of desire items and makes the study list off of the pool list\n", + " #also takes in values for the first half of the test list (Targets)\n", + " #\n", + " for i in range(num_study):\n", + " l_dict = pool_list[int(i + ((num_study/3)* 2))]\n", + " #index used here is i + ((num_study/3)* 2) because I expect to call the gen_mixed fucntion first \n", + " #That function will thus use the first ((num_study/3)* 2) elements in our pool list before \n", + " x = {}\n", + " x['stimulus'] = l_dict['stimulus']\n", + " x['valence'] = l_dict['valence']\n", + " x['cond'] = 'PURE'\n", + " x['novelty'] = 'TARGET'\n", + " pure_test.append(x)\n", + " pure_study.append(x)\n", + " # keep traversing the pool with a higher index to ensure no duplicates\n", + " # find num_study more items to add to the test list as LURES\n", + " for j in range(num_study):\n", + " l_dict = pool_list[int(j + ((num_study/3)* 2) + num_study)]\n", + " x = {}\n", + " x['stimulus'] = l_dict['stimulus']\n", + " x['valence'] = l_dict['valence']\n", + " x['cond'] = 'PURE'\n", + " x['novelty'] = 'LURE'\n", + " pure_test.append(x)\n", + " #shuffle the test list before placing both the study and test list in the final dict\n", + " random.shuffle(pure_test)\n", + " pure['study_list'] = pure_study\n", + " pure['test_list'] = pure_test\n", + " return pure\n", + "'''\n", + "Function to create mixed dict. Similar to the structure and logic to gen_pure function. \n", + "'''\n", + "def gen_mixed(num_study):\n", + " mix_study = []\n", + " mix_test = []\n", + " mix = {}\n", + " for i in range(num_study):\n", + " pos_dict = pos_list[i]\n", + " x = {}\n", + " x['stimulus'] = pos_dict['stimulus']\n", + " x['valence'] = pos_dict['valence']\n", + " x['cond'] = 'MIXED'\n", + " x['novelty'] = 'TARGET'\n", + " mix_test.append(x)\n", + " mix_study.append(x)\n", + " neg_dict = neg_list[i]\n", + " y = {}\n", + " y['stimulus'] = neg_dict['stimulus']\n", + " y['valence'] = neg_dict['valence']\n", + " y['cond'] = 'MIXED'\n", + " y['novelty'] = 'TARGET'\n", + " mix_test.append(y)\n", + " mix_study.append(y)\n", + " neu_dict = neu_list[i]\n", + " z = {}\n", + " z['stimulus'] = neu_dict['stimulus']\n", + " z['valence'] = neu_dict['valence']\n", + " z['cond'] = 'MIXED'\n", + " z['novelty'] = 'TARGET'\n", + " mix_test.append(z)\n", + " mix_study.append(z)\n", + " for j in range(num_study):\n", + " pos_dict = pos_list[j+num_study]\n", + " x = {}\n", + " x['stimulus'] = pos_dict['stimulus']\n", + " x['valence'] = pos_dict['valence']\n", + " x['cond'] = 'MIXED'\n", + " x['novelty'] = 'LURE'\n", + " mix_test.append(x)\n", + " neg_dict = neg_list[j+num_study]\n", + " y = {}\n", + " y['stimulus'] = neg_dict['stimulus']\n", + " y['valence'] = neg_dict['valence']\n", + " y['cond'] = 'MIXED'\n", + " y['novelty'] = 'LURE'\n", + " mix_test.append(y)\n", + " neu_dict = neu_list[j+num_study]\n", + " z = {}\n", + " z['stimulus'] = neu_dict['stimulus']\n", + " z['valence'] = neu_dict['valence']\n", + " z['cond'] = 'MIXED'\n", + " z['novelty'] = 'LURE'\n", + " mix_test.append(z)\n", + " neu_dict = neu_list[j+num_study]\n", + " random.shuffle(mix_test)\n", + " mix['study_list'] = mix_study\n", + " mix['test_list'] = mix_test\n", + " mix_test.append(z)\n", + " return mix\n", + "\n", + "'''\n", + "Final function to call our gen_pure and gen_mixed functions to create our blocks. \n", + "One block = 1 Pure Pos, 1 Pure Neg, 1 Pure Neu, and one Mixed Dict\n", + "Has a num_block parameter to determine the number of blocks \n", + "Has a num_study parameter for the number of items for EACH valence in the mixed study\n", + "Pure study lists will be made with num_study*3 items\n", + "\n", + "This function creates our list of dicts of lists of dicts\n", + "'''\n", + "def gen_blocks(num_blocks, num_study):\n", + " blocks = []\n", + " for i in range(num_blocks):\n", + " mixed = gen_mixed(num_study)\n", + " pos_pure = gen_pure(num_study*3,pos_list)\n", + " neg_pure = gen_pure(num_study*3,neg_list)\n", + " neu_pure = gen_pure(num_study*3,neu_list)\n", + " #shuffle all our pool lists after creating one block\n", + " random.shuffle(pos_list)\n", + " random.shuffle(neg_list)\n", + " random.shuffle(neu_list)\n", + " #radomize order of dicts in a block\n", + " blocks_temp = []\n", + " blocks_temp.append(pos_pure)\n", + " blocks_temp.append(neg_pure)\n", + " blocks_temp.append(neu_pure)\n", + " blocks_temp.append(mixed)\n", + " random.shuffle(blocks_temp)\n", + " for block in blocks_temp:\n", + " blocks.append(block)\n", + " return blocks\n", + "\n", + "\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "How many blocks would you like for the study? 3\n", + "How many items would you like for EACH valance in the mixed study list? 3\n", + "PLEASE NOTE: You will have 9 items for EACH study list and 18 items for EACH test list\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'study_list': [{'stimulus': 'merry',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'maniac',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'consoled',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'health',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'blackmail',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'hay', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'warmth',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'spanking',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'rain',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}],\n", + " 'test_list': [{'stimulus': 'health',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'home', 'valence': 'POS', 'cond': 'MIXED', 'novelty': 'LURE'},\n", + " {'stimulus': 'infection',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'spanking',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'warmth',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'insecure',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'maniac',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'kids', 'valence': 'POS', 'cond': 'MIXED', 'novelty': 'LURE'},\n", + " {'stimulus': 'consoled',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'hay', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'detached',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'melody',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'blackmail',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'merry',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'rain', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'tower',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'}]},\n", + " {'study_list': [{'stimulus': 'bright',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'sugar',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'tune', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'sky', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'youth',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'jolly',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'lucky',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'capable',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'useful',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}],\n", + " 'test_list': [{'stimulus': 'patriot',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'knowledge',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'memories',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'youth',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'bright',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'jolly',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'muffin', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'tune', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'fame', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'lottery',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'capable',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'pleasure',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'improve',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'grin', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'sugar',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'sky', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'lucky',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'useful',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}]},\n", + " {'study_list': [{'stimulus': 'wagon',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'bowl', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'hammer',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'owl', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'whistle',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'cabinet',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'paper',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'invest',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'coast',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}],\n", + " 'test_list': [{'stimulus': 'bowl',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'board', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'circle', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'cabinet',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'odd', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'city', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'knot', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'hammer',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'whistle',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'paper',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'wagon',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'lamp', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'concentrate',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'invest',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'coast',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'owl', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'book', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'cannon',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'}]},\n", + " {'study_list': [{'stimulus': 'gossip',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'paralysis',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'beggar',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'clumsy',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'victim',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'waste',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'madman',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'scorpion',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'upset',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}],\n", + " 'test_list': [{'stimulus': 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'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'snob', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'headache',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'waste',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'addicted',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'dummy', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'pity', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'dagger', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'upset',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}]},\n", + " {'study_list': [{'stimulus': 'indifferent',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'part', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'horse',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'coin', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'violin',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'teacher',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'ketchup',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'thermometer',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'spray',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}],\n", + " 'test_list': [{'stimulus': 'cannon',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'square', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'book', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'ink', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'spray',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'door', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'detail', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'ketchup',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'thermometer',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'teacher',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'indifferent',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'part', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'truck', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'violin',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'horse',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'cork', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'market', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'coin',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}]},\n", + " {'study_list': [{'stimulus': 'adventure',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'dustpan',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'basket',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'scholar',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'feeble',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'ship', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'free',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'ache',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'kerosene',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}],\n", + " 'test_list': [{'stimulus': 'crime',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'restaurant',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'nectar',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'scholar',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'ship', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'dustpan',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'kerosene',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'mind', 'valence': 'POS', 'cond': 'MIXED', 'novelty': 'LURE'},\n", + " {'stimulus': 'dump', 'valence': 'NEG', 'cond': 'MIXED', 'novelty': 'LURE'},\n", + " {'stimulus': 'slum', 'valence': 'NEG', 'cond': 'MIXED', 'novelty': 'LURE'},\n", + " {'stimulus': 'basket',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'feeble',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'free',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'adventure',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'ache',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'umbrella',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'}]},\n", + " {'study_list': [{'stimulus': 'meek',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'burn', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'pervert',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'starving',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'blackmail',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'needle',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'torture',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'timid',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'impotent',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}],\n", + " 'test_list': [{'stimulus': 'torture',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'manure', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'cold', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'cell', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'blackmail',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'needle',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'sissy', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'pervert',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'helpless',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'meek', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'disturb',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'starving',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'shamed', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'spider', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'burn', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'impotent',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'listless',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'timid',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}]},\n", + " {'study_list': [{'stimulus': 'movie',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'grateful',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'graduate',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'snow', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'star', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'comfort',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'astonished',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'brother',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'intercourse',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}],\n", + " 'test_list': [{'stimulus': 'brother',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'birthday',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'daylight',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'safe', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'star', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'grateful',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'soothe', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'art', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'graduate',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'gift', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'bathtub',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'movie',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'snow', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'leisurely',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'astonished',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'melody', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'comfort',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'intercourse',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}]},\n", + " {'study_list': [{'stimulus': 'vacation',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'poison',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'icebox',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'kiss',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'python',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'violin',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'money',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'abortion',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'teacher',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}],\n", + " 'test_list': [{'stimulus': 'gossip',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'idea', 'valence': 'POS', 'cond': 'MIXED', 'novelty': 'LURE'},\n", + " {'stimulus': 'kiss',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'teacher',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'icebox',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'melody',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'money',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'mucus', 'valence': 'NEG', 'cond': 'MIXED', 'novelty': 'LURE'},\n", + " {'stimulus': 'alimony',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'god', 'valence': 'POS', 'cond': 'MIXED', 'novelty': 'LURE'},\n", + " {'stimulus': 'abortion',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'python',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'poison',\n", + " 'valence': 'NEG',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'vacation',\n", + " 'valence': 'POS',\n", + " 'cond': 'MIXED',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'violin',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'arm', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'}]},\n", + " {'study_list': [{'stimulus': 'boxer',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'scissors',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'curtains',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'kettle',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'ink', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'coin', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'trunk',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'jug', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'journal',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}],\n", + " 'test_list': [{'stimulus': 'kettle',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'appliance',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'jug', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'trunk',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'statue', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'wagon', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'ink', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'journal',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'milk', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'truck', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'book', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'curtains',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'scissors',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'frog', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'boxer',\n", + " 'valence': 'NEU',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'coin', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'vest', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'rock', 'valence': 'NEU', 'cond': 'PURE', 'novelty': 'LURE'}]},\n", + " {'study_list': [{'stimulus': 'burial',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'burn', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'upset',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'crutch',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'mosquito',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'dreadful',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'fearful',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'pungent',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'devil',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}],\n", + " 'test_list': [{'stimulus': 'crutch',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'fearful',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'dreadful',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'burial',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'pungent',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'burn', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'mosquito',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'upset',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'habit', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'depression',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'maggot', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'wounds', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'measles',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'cockroach',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'foul', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'severe', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'putrid', 'valence': 'NEG', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'devil',\n", + " 'valence': 'NEG',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}]},\n", + " {'study_list': [{'stimulus': 'color',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'hug', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'grin', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'ocean',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'bathtub',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'tune', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'eat', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'agreement',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'muscular',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}],\n", + " 'test_list': [{'stimulus': 'color',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'hug', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'identity',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'astonished',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'interest',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'prestige',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'bathtub',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'elated', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'muscular',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'easy', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'LURE'},\n", + " {'stimulus': 'grin', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'eat', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'agreement',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'tune', 'valence': 'POS', 'cond': 'PURE', 'novelty': 'TARGET'},\n", + " {'stimulus': 'butterfly',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'graduate',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'carefree',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'ocean',\n", + " 'valence': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}]}]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# taking in our input for num_blocks and num_study\n", + "num_blocks = (int(input(\"How many blocks would you like for the study? \")))\n", + "num_study = (int(input(\"How many items would you like for EACH valance in the mixed study list? \")))\n", + "print(\"PLEASE NOTE: You will have \" + str(num_study * 3) + \" items for EACH study list and \" + str(num_study * 6) + \" items for EACH test list\")\n", + "\n", + "# max number of items for num_study input is 26\n", + "# because the neutral pool has the least number of items which is 209 items\n", + "# the mixed test list would have 26 x 2 neutral test items (26 lures and 26 targets) items = 52\n", + "# using the same logic, the neutral pure test list would have ((26 x 3) x 2) items = 156\n", + "# 156 + 52 = 208... any more and we would go out of bounds \n", + "assert (num_study < 27), \"TOO MANY ITEMS. Please pick a smaller number of study items\"\n", + "\n", + "gen_blocks(num_blocks,num_study)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'study': [{'valence': 'neg',\n", + " 'description': 'traitor',\n", + " 'word_no': '448',\n", + " 'valence_mean': '2.2200000000000002',\n", + " 'valence_sd': '1.6899999999999999',\n", + " 'arousal_mean': '5.7800000000000002',\n", + " 'arousal_sd': '2.4700000000000002',\n", + " 'dominance_mean': '4.6100000000000003',\n", + " 'dominance_sd': '2.71',\n", + " 'word_frequency': '2',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'punishment',\n", + " 'word_no': '335',\n", + " 'valence_mean': '2.2200000000000002',\n", + " 'valence_sd': '1.4099999999999999',\n", + " 'arousal_mean': '5.9299999999999997',\n", + " 'arousal_sd': '2.3999999999999999',\n", + " 'dominance_mean': '3.5',\n", + " 'dominance_sd': '2.4300000000000002',\n", + " 'word_frequency': '21',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'cell',\n", + " 'word_no': '587',\n", + " 'valence_mean': '3.8199999999999998',\n", + " 'valence_sd': '1.7',\n", + " 'arousal_mean': '4.0800000000000001',\n", + " 'arousal_sd': '2.1899999999999999',\n", + " 'dominance_mean': '4.1200000000000001',\n", + " 'dominance_sd': '2.1299999999999999',\n", + " 'word_frequency': '65',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'pest',\n", + " 'word_no': '313',\n", + " 'valence_mean': '3.1299999999999999',\n", + " 'valence_sd': '1.8200000000000001',\n", + " 'arousal_mean': '5.6200000000000001',\n", + " 'arousal_sd': '2.1499999999999999',\n", + " 'dominance_mean': '5.29',\n", + " 'dominance_sd': '2.1299999999999999',\n", + " 'word_frequency': '4',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'dirty',\n", + " 'word_no': '590',\n", + " 'valence_mean': '3.0800000000000001',\n", + " 'valence_sd': '2.0499999999999998',\n", + " 'arousal_mean': '4.8799999999999999',\n", + " 'arousal_sd': '2.29',\n", + " 'dominance_mean': '4.7000000000000002',\n", + " 'dominance_sd': '2.1200000000000001',\n", + " 'word_frequency': '36',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'immature',\n", + " 'word_no': '806',\n", + " 'valence_mean': '3.3900000000000001',\n", + " 'valence_sd': '1.7',\n", + " 'arousal_mean': '4.1500000000000004',\n", + " 'arousal_sd': '1.96',\n", + " 'dominance_mean': '4.8499999999999996',\n", + " 'dominance_sd': '2.2000000000000002',\n", + " 'word_frequency': '7',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'neglect',\n", + " 'word_no': '898',\n", + " 'valence_mean': '2.6299999999999999',\n", + " 'valence_sd': '1.6399999999999999',\n", + " 'arousal_mean': '4.8300000000000001',\n", + " 'arousal_sd': '2.3100000000000001',\n", + " 'dominance_mean': '3.8500000000000001',\n", + " 'dominance_sd': '2.29',\n", + " 'word_frequency': '12',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'manure',\n", + " 'word_no': '865',\n", + " 'valence_mean': '3.1000000000000001',\n", + " 'valence_sd': '1.74',\n", + " 'arousal_mean': '4.1699999999999999',\n", + " 'arousal_sd': '2.0899999999999999',\n", + " 'dominance_mean': '4.6699999999999999',\n", + " 'dominance_sd': '1.3600000000000001',\n", + " 'word_frequency': '6',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'abortion',\n", + " 'word_no': '622',\n", + " 'valence_mean': '3.5',\n", + " 'valence_sd': '2.2999999999999998',\n", + " 'arousal_mean': '5.3899999999999997',\n", + " 'arousal_sd': '2.7999999999999998',\n", + " 'dominance_mean': '4.5899999999999999',\n", + " 'dominance_sd': '2.54',\n", + " 'word_frequency': '6',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'}],\n", + " 'test': [{'valence': 'neg',\n", + " 'description': 'hell',\n", + " 'word_no': '788',\n", + " 'valence_mean': '2.2400000000000002',\n", + " 'valence_sd': '1.6200000000000001',\n", + " 'arousal_mean': '5.3799999999999999',\n", + " 'arousal_sd': '2.6200000000000001',\n", + " 'dominance_mean': '3.2400000000000002',\n", + " 'dominance_sd': '2.3599999999999999',\n", + " 'word_frequency': '95',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'agony',\n", + " 'word_no': '10',\n", + " 'valence_mean': '2.4300000000000002',\n", + " 'valence_sd': '2.1699999999999999',\n", + " 'arousal_mean': '6.0599999999999996',\n", + " 'arousal_sd': '2.6699999999999999',\n", + " 'dominance_mean': '4.0199999999999996',\n", + " 'dominance_sd': '2.4900000000000002',\n", + " 'word_frequency': '9',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'slave',\n", + " 'word_no': '398',\n", + " 'valence_mean': '1.8400000000000001',\n", + " 'valence_sd': '1.1299999999999999',\n", + " 'arousal_mean': '6.21',\n", + " 'arousal_sd': '2.9300000000000002',\n", + " 'dominance_mean': '3.29',\n", + " 'dominance_sd': '2.7599999999999998',\n", + " 'word_frequency': '30',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'neglect',\n", + " 'word_no': '898',\n", + " 'valence_mean': '2.6299999999999999',\n", + " 'valence_sd': '1.6399999999999999',\n", + " 'arousal_mean': '4.8300000000000001',\n", + " 'arousal_sd': '2.3100000000000001',\n", + " 'dominance_mean': '3.8500000000000001',\n", + " 'dominance_sd': '2.29',\n", + " 'word_frequency': '12',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'cell',\n", + " 'word_no': '587',\n", + " 'valence_mean': '3.8199999999999998',\n", + " 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'valence_sd': '1.6000000000000001',\n", + " 'arousal_mean': '5.2800000000000002',\n", + " 'arousal_sd': '2.1099999999999999',\n", + " 'dominance_mean': '5.2000000000000002',\n", + " 'dominance_sd': '1.95',\n", + " 'word_frequency': '2',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'butterfly',\n", + " 'word_no': '58',\n", + " 'valence_mean': '7.1699999999999999',\n", + " 'valence_sd': '1.2',\n", + " 'arousal_mean': '3.4700000000000002',\n", + " 'arousal_sd': '2.3900000000000001',\n", + " 'dominance_mean': '4.6500000000000004',\n", + " 'dominance_sd': '2.27',\n", + " 'word_frequency': '2',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'rescue',\n", + " 'word_no': '352',\n", + " 'valence_mean': '7.7000000000000002',\n", + " 'valence_sd': '1.24',\n", + " 'arousal_mean': '6.5300000000000002',\n", + " 'arousal_sd': '2.5600000000000001',\n", + " 'dominance_mean': '6.4500000000000002',\n", + " 'dominance_sd': '2.29',\n", + " 'word_frequency': '15',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'mobility',\n", + " 'word_no': '881',\n", + " 'valence_mean': '6.8300000000000001',\n", + " 'valence_sd': '1.79',\n", + " 'arousal_mean': '5.0',\n", + " 'arousal_sd': '2.1800000000000002',\n", + " 'dominance_mean': '6.4299999999999997',\n", + " 'dominance_sd': '1.48',\n", + " 'word_frequency': '8',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'soothe',\n", + " 'word_no': '988',\n", + " 'valence_mean': '7.2999999999999998',\n", + " 'valence_sd': '1.8500000000000001',\n", + " 'arousal_mean': '4.4000000000000004',\n", + " 'arousal_sd': '3.0800000000000001',\n", + " 'dominance_mean': '5.3600000000000003',\n", + " 'dominance_sd': '2.2400000000000002',\n", + " 'word_frequency': '2',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'radiant',\n", + " 'word_no': '954',\n", + " 'valence_mean': '6.7300000000000004',\n", + " 'valence_sd': '2.1699999999999999',\n", + " 'arousal_mean': '5.3899999999999997',\n", + " 'arousal_sd': '2.8199999999999998',\n", + " 'dominance_mean': '5.6100000000000003',\n", + " 'dominance_sd': '2.1699999999999999',\n", + " 'word_frequency': '8',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'dream',\n", + " 'word_no': '132',\n", + " 'valence_mean': '6.7300000000000004',\n", + " 'valence_sd': '1.75',\n", + " 'arousal_mean': '4.5300000000000002',\n", + " 'arousal_sd': '2.7200000000000002',\n", + " 'dominance_mean': '5.5300000000000002',\n", + " 'dominance_sd': '1.98',\n", + " 'word_frequency': '64',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'engaged',\n", + " 'word_no': '143',\n", + " 'valence_mean': '8.0',\n", + " 'valence_sd': '1.3799999999999999',\n", + " 'arousal_mean': '6.7699999999999996',\n", + " 'arousal_sd': '2.0699999999999998',\n", + " 'dominance_mean': '6.4900000000000002',\n", + " 'dominance_sd': '2.2200000000000002',\n", + " 'word_frequency': '47',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'profit',\n", + " 'word_no': '331',\n", + " 'valence_mean': '7.6299999999999999',\n", + " 'valence_sd': '1.3',\n", + " 'arousal_mean': '6.6799999999999997',\n", + " 'arousal_sd': '1.78',\n", + " 'dominance_mean': '5.8499999999999996',\n", + " 'dominance_sd': '2.4700000000000002',\n", + " 'word_frequency': '28',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'muscular',\n", + " 'word_no': '290',\n", + " 'valence_mean': '6.8200000000000003',\n", + " 'valence_sd': '1.6299999999999999',\n", + " 'arousal_mean': '5.4699999999999998',\n", + " 'arousal_sd': '2.2000000000000002',\n", + " 'dominance_mean': '6.5800000000000001',\n", + " 'dominance_sd': '2.2799999999999998',\n", + " 'word_frequency': '16',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'garden',\n", + " 'word_no': '761',\n", + " 'valence_mean': '6.71',\n", + " 'valence_sd': '1.74',\n", + " 'arousal_mean': '4.3899999999999997',\n", + " 'arousal_sd': '2.3500000000000001',\n", + " 'dominance_mean': '6.0199999999999996',\n", + " 'dominance_sd': '1.71',\n", + " 'word_frequency': '60',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'honey',\n", + " 'word_no': '792',\n", + " 'valence_mean': '6.7300000000000004',\n", + " 'valence_sd': '1.7',\n", + " 'arousal_mean': '4.5099999999999998',\n", + " 'arousal_sd': '2.25',\n", + " 'dominance_mean': '5.4400000000000004',\n", + " 'dominance_sd': '1.47',\n", + " 'word_frequency': '25',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'education',\n", + " 'word_no': '137',\n", + " 'valence_mean': '6.6900000000000004',\n", + " 'valence_sd': '1.77',\n", + " 'arousal_mean': '5.7400000000000002',\n", + " 'arousal_sd': '2.46',\n", + " 'dominance_mean': '6.1500000000000004',\n", + " 'dominance_sd': '2.3500000000000001',\n", + " 'word_frequency': '214',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'astonished',\n", + " 'word_no': '28',\n", + " 'valence_mean': '6.5599999999999996',\n", + " 'valence_sd': '1.6100000000000001',\n", + " 'arousal_mean': '6.5800000000000001',\n", + " 'arousal_sd': '2.2200000000000002',\n", + " 'dominance_mean': '5.1600000000000001',\n", + " 'dominance_sd': '1.79',\n", + " 'word_frequency': '6',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'}]}]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pickle\n", + "blocks = pickle.load(open('Option_1_solution.pickle', 'rb'))\n", + "blocks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Option 2: Scene Study\n", + "\n", + "This study will test whether recognition memory for indoor and outdoor\n", + "scenes is modulated by the structure of the study lists.\n", + "Specifically, participants will study lists that either have indoor\n", + "and outdoor scenes that come in pure blocks or intermixed (similar to\n", + "the Valence study above). The participants will then be given a\n", + "recognition test over all the studied target images plus a matched set\n", + "of non-studied lures. You can access the lists of stimuli available:\n", + "\n", + "- [Indoor Pool](./indoor.csv)\n", + "- [Outdoor Pool](./outdoor.csv)\n", + "\n", + "You will need to read these files in as lists of dictionaries (hint,\n", + "use the ``DictReader`` from the ``csv`` module that was covered in\n", + "class.) For the actual experiment we will give you the images that\n", + "are referenced by the file names in these pools, but for the list\n", + "generation you do not need the images, themselves and should identify\n", + "the image you will be presenting using the file name. Use these pools\n", + "to create lists of trials for two experimental conditions: pure or\n", + "mixed. In the *pure* condition, all of the trials should be images\n", + "from the same category (be sure to have the same number of indoor\n", + "and outdoor pure lists.) In the *mixed* condition, each\n", + "list should contain an equal number of indoor and outdoor\n", + "images in *random* order (hint, use the ``shuffle`` function provided\n", + "by the ``random`` module.)\n", + "\n", + "You will need to generate a matching test list for each study list\n", + "that includes all the studied items, plus a set of lures that match\n", + "the image categories from the studied items.\n", + "\n", + "Be sure to add in information to each trial dictionary that identifies\n", + "the file name, the category of the image, the condition of the list,\n", + "and whether it is a target or a lure.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Option 3: Your own study\n", + "\n", + "You may also generate lists for a study specifically relevant to your\n", + "own work. We are extremely supportive of this, but the study must be\n", + "approved by the professor.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/assignments/A04_ListGen.ipynb b/assignments/A04_ListGen.ipynb index f9d7e27..8fa8c99 100644 --- a/assignments/A04_ListGen.ipynb +++ b/assignments/A04_ListGen.ipynb @@ -162,7 +162,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.3" } }, "nbformat": 4, diff --git a/assignments/A05_SMILE_Experiment-jhr3kd.ipynb b/assignments/A05_SMILE_Experiment-jhr3kd.ipynb new file mode 100644 index 0000000..92aa4b2 --- /dev/null +++ b/assignments/A05_SMILE_Experiment-jhr3kd.ipynb @@ -0,0 +1,1293 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment 5: SMILE Experiment\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Objectives\n", + "\n", + "Upon completion of this assignment, the student will have:\n", + "\n", + "1. Used the list generation code to make experimental blocks.\n", + "\n", + "2. Created a full-fledged experiment for collecting data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment\n", + "\n", + "* Write SMILE code in a Jupyter notebook (after making a copy and renaming it to have your userid in the title --- e.g., A05_SMILE_Experiment_mst3k).\n", + "\n", + "## Details\n", + "\n", + "Your assignment is to turn the lists generated by code from the previous assignment into an experiment. As a reminder, regardless of whether you selected option 1 or option 2, this is a recognition memory experiment. This means that participants will study a list of items one at a time, and then, after a short delay, be tested for their memory of those items. In the test phase of each block, participants will see the study items again, along with an equal number of new items, and for each item they must specify whether the item is an old target item (i.e., one that was on the study list) or a new lure item. \n", + "\n", + "The high level structure of the experiment is as follows:\n", + "\n", + "- Present the participant some instructions explaining the task\n", + "- Optionally provide some practice making responses\n", + "- Loop over the blocks of study--test lists\n", + "\n", + "Each block of study--test lists will have the following structure:\n", + "\n", + "- Wait for the participant to press a key to start the block\n", + "- Loop over the study list presenting the study items, one at a time\n", + "- Wait for a delay (we may eventually fill this with some simple math problems)\n", + "- Loop over the test list to present the test items, one at a time, waiting for a keyboard response on each item\n", + "\n", + "Each study item trial will:\n", + "\n", + "- Present the item for a specified duration (this should be a configuration variable at the top of your code)\n", + "- Wait an inter-stimulus duration plus some amount of jitter (these, too, should be config variables)\n", + "- Log the stimulus information, including when it appeared on the screen\n", + "\n", + "Each test item trial will:\n", + "\n", + "- Present the item on the screen (with either a Label or Image state) until the participant makes a keyboard response of either the key you have selected to indicate the item is \"old\" or the key that indicates the item is \"new\"\n", + "- Log the stimulus information, including when the stimulus appeared on the screen, when the participant made their response, and what response they made\n", + "\n", + "It is possible to write the entire experiment in one big state machine, but it may be easier to break up these different sections into subroutines.\n", + "\n", + "Be sure to refer to the class notebooks to help guide how to do all the steps above. We have some code below to help you get started.\n", + "\n", + " \n", + "* ***When you are done, save this notebook as HTML (`File -> Download as -> HTML`) and upload it to the matching assignment on UVACollab.*** " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pos_pool: 301\n", + "neg_pool: 292\n", + "neu_pool: 208\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'study': [{'valence': 'pos',\n", + " 'description': 'rescue',\n", + " 'word_no': '352',\n", + " 'valence_mean': '7.7000000000000002',\n", + " 'valence_sd': '1.24',\n", + " 'arousal_mean': '6.5300000000000002',\n", + " 'arousal_sd': '2.5600000000000001',\n", + " 'dominance_mean': '6.4500000000000002',\n", + " 'dominance_sd': '2.29',\n", + " 'word_frequency': '15',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'luxury',\n", + " 'word_no': '268',\n", + " 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'4.3300000000000001',\n", + " 'arousal_sd': '1.78',\n", + " 'dominance_mean': '5.6699999999999999',\n", + " 'dominance_sd': '1.6200000000000001',\n", + " 'word_frequency': '40',\n", + " 'novelty': 'target',\n", + " 'cond': 'neu'},\n", + " {'valence': 'neu',\n", + " 'description': 'barrel',\n", + " 'word_no': '651',\n", + " 'valence_mean': '5.0499999999999998',\n", + " 'valence_sd': '1.46',\n", + " 'arousal_mean': '3.3599999999999999',\n", + " 'arousal_sd': '2.2799999999999998',\n", + " 'dominance_mean': '4.8899999999999997',\n", + " 'dominance_sd': '1.5700000000000001',\n", + " 'word_frequency': '24',\n", + " 'novelty': 'target',\n", + " 'cond': 'neu'}],\n", + " 'test': [{'valence': 'neu',\n", + " 'description': 'poster',\n", + " 'word_no': '942',\n", + " 'valence_mean': '5.3399999999999999',\n", + " 'valence_sd': '1.75',\n", + " 'arousal_mean': '3.9300000000000002',\n", + " 'arousal_sd': '2.5600000000000001',\n", + " 'dominance_mean': '4.9100000000000001',\n", + " 'dominance_sd': '1.8700000000000001',\n", + " 'word_frequency': '4',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neu'},\n", + " {'valence': 'neu',\n", + " 'description': 'context',\n", + " 'word_no': '84',\n", + " 'valence_mean': '5.2000000000000002',\n", + " 'valence_sd': '1.3799999999999999',\n", + " 'arousal_mean': '4.2199999999999998',\n", + " 'arousal_sd': '2.2400000000000002',\n", + " 'dominance_mean': '5.1699999999999999',\n", + " 'dominance_sd': '1.3899999999999999',\n", + " 'word_frequency': '2',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neu'},\n", + " {'valence': 'neu',\n", + " 'description': 'avenue',\n", + " 'word_no': '646',\n", + " 'valence_mean': '5.5',\n", + " 'valence_sd': '1.3700000000000001',\n", + " 'arousal_mean': '4.1200000000000001',\n", + " 'arousal_sd': '2.0099999999999998',\n", + " 'dominance_mean': '5.4000000000000004',\n", + " 'dominance_sd': '1.53',\n", + " 'word_frequency': '46',\n", + " 'novelty': 'target',\n", + " 'cond': 'neu'},\n", + " {'valence': 'neu',\n", + " 'description': 'tool',\n", + " 'word_no': '1015',\n", + " 'valence_mean': '5.1900000000000004',\n", + " 'valence_sd': '1.27',\n", + " 'arousal_mean': '4.3300000000000001',\n", + " 'arousal_sd': '1.78',\n", + " 'dominance_mean': '5.6699999999999999',\n", + " 'dominance_sd': '1.6200000000000001',\n", + " 'word_frequency': '40',\n", + " 'novelty': 'target',\n", + " 'cond': 'neu'},\n", + " {'valence': 'neu',\n", + " 'description': 'watch',\n", + " 'word_no': '580',\n", + " 'valence_mean': '5.7800000000000002',\n", + " 'valence_sd': '1.51',\n", + " 'arousal_mean': '4.0999999999999996',\n", + " 'arousal_sd': '2.1200000000000001',\n", + " 'dominance_mean': '5.3700000000000001',\n", + " 'dominance_sd': '1.75',\n", + " 'word_frequency': '81',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neu'},\n", + " {'valence': 'neu',\n", + " 'description': 'barrel',\n", + " 'word_no': '651',\n", + " 'valence_mean': '5.0499999999999998',\n", + " 'valence_sd': '1.46',\n", + " 'arousal_mean': '3.3599999999999999',\n", + " 'arousal_sd': '2.2799999999999998',\n", + " 'dominance_mean': '4.8899999999999997',\n", + " 'dominance_sd': '1.5700000000000001',\n", + " 'word_frequency': '24',\n", + " 'novelty': 'target',\n", + " 'cond': 'neu'}]},\n", + " {'study': [{'valence': 'neg',\n", + " 'description': 'troubled',\n", + " 'word_no': '455',\n", + " 'valence_mean': '2.1699999999999999',\n", + " 'valence_sd': '1.21',\n", + " 'arousal_mean': '5.9400000000000004',\n", + " 'arousal_sd': '2.3599999999999999',\n", + " 'dominance_mean': '3.9100000000000001',\n", + " 'dominance_sd': '2.3300000000000001',\n", + " 'word_frequency': '31',\n", + " 'novelty': 'target',\n", + " 'cond': 'mixed'},\n", + " {'valence': 'neu',\n", + " 'description': 'violin',\n", + " 'word_no': '579',\n", + " 'valence_mean': '5.4299999999999997',\n", + " 'valence_sd': '1.98',\n", + " 'arousal_mean': '3.4900000000000002',\n", + " 'arousal_sd': '2.2599999999999998',\n", + " 'dominance_mean': '5.1799999999999997',\n", + " 'dominance_sd': '2.0099999999999998',\n", + " 'word_frequency': '11',\n", + " 'novelty': 'target',\n", + " 'cond': 'mixed'},\n", + " {'valence': 'pos',\n", + " 'description': 'millionaire',\n", + " 'word_no': '278',\n", + " 'valence_mean': '8.0299999999999994',\n", + " 'valence_sd': '1.4199999999999999',\n", + " 'arousal_mean': '6.1399999999999997',\n", + " 'arousal_sd': '2.7000000000000002',\n", + " 'dominance_mean': '6.9699999999999998',\n", + " 'dominance_sd': '2.3999999999999999',\n", + " 'word_frequency': '2',\n", + " 'novelty': 'target',\n", + " 'cond': 'mixed'}],\n", + " 'test': [{'valence': 'neg',\n", + " 'description': 'crime',\n", + " 'word_no': '704',\n", + " 'valence_mean': '2.8900000000000001',\n", + " 'valence_sd': '2.0600000000000001',\n", + " 'arousal_mean': '5.4100000000000001',\n", + " 'arousal_sd': '2.6899999999999999',\n", + " 'dominance_mean': '4.1200000000000001',\n", + " 'dominance_sd': '2.2400000000000002',\n", + " 'word_frequency': '34',\n", + " 'novelty': 'lure',\n", + " 'cond': 'mixed'},\n", + " {'valence': 'neg',\n", + " 'description': 'troubled',\n", + " 'word_no': '455',\n", + " 'valence_mean': '2.1699999999999999',\n", + " 'valence_sd': '1.21',\n", + " 'arousal_mean': '5.9400000000000004',\n", + " 'arousal_sd': '2.3599999999999999',\n", + " 'dominance_mean': '3.9100000000000001',\n", + " 'dominance_sd': '2.3300000000000001',\n", + " 'word_frequency': '31',\n", + " 'novelty': 'target',\n", + " 'cond': 'mixed'},\n", + " {'valence': 'pos',\n", + " 'description': 'millionaire',\n", + " 'word_no': '278',\n", + " 'valence_mean': '8.0299999999999994',\n", + " 'valence_sd': '1.4199999999999999',\n", + " 'arousal_mean': '6.1399999999999997',\n", + " 'arousal_sd': '2.7000000000000002',\n", + " 'dominance_mean': '6.9699999999999998',\n", + " 'dominance_sd': '2.3999999999999999',\n", + " 'word_frequency': '2',\n", + " 'novelty': 'target',\n", + " 'cond': 'mixed'},\n", + " {'valence': 'neu',\n", + " 'description': 'ink',\n", + " 'word_no': '229',\n", + " 'valence_mean': '5.0499999999999998',\n", + " 'valence_sd': '0.81000000000000005',\n", + " 'arousal_mean': '3.8399999999999999',\n", + " 'arousal_sd': '1.8799999999999999',\n", + " 'dominance_mean': '4.6100000000000003',\n", + " 'dominance_sd': '2.1299999999999999',\n", + " 'word_frequency': '7',\n", + " 'novelty': 'lure',\n", + " 'cond': 'mixed'},\n", + " {'valence': 'neu',\n", + " 'description': 'violin',\n", + " 'word_no': '579',\n", + " 'valence_mean': '5.4299999999999997',\n", + " 'valence_sd': '1.98',\n", + " 'arousal_mean': '3.4900000000000002',\n", + " 'arousal_sd': '2.2599999999999998',\n", + " 'dominance_mean': '5.1799999999999997',\n", + " 'dominance_sd': '2.0099999999999998',\n", + " 'word_frequency': '11',\n", + " 'novelty': 'target',\n", + " 'cond': 'mixed'},\n", + " {'valence': 'pos',\n", + " 'description': 'wealthy',\n", + " 'word_no': '488',\n", + " 'valence_mean': '7.7000000000000002',\n", + " 'valence_sd': '1.3400000000000001',\n", + " 'arousal_mean': '5.7999999999999998',\n", + " 'arousal_sd': '2.73',\n", + " 'dominance_mean': '6.7699999999999996',\n", + " 'dominance_sd': '2.5699999999999998',\n", + " 'word_frequency': '12',\n", + " 'novelty': 'lure',\n", + " 'cond': 'mixed'}]},\n", + " {'study': [{'valence': 'neg',\n", + " 'description': 'upset',\n", + " 'word_no': '465',\n", + " 'valence_mean': '2.0',\n", + " 'valence_sd': '1.1799999999999999',\n", + " 'arousal_mean': '5.8600000000000003',\n", + " 'arousal_sd': '2.3999999999999999',\n", + " 'dominance_mean': '4.0800000000000001',\n", + " 'dominance_sd': '2.3100000000000001',\n", + " 'word_frequency': '14',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'coffin',\n", + " 'word_no': '76',\n", + " 'valence_mean': '2.5600000000000001',\n", + " 'valence_sd': '1.96',\n", + " 'arousal_mean': '5.0300000000000002',\n", + " 'arousal_sd': '2.79',\n", + " 'dominance_mean': '4.0800000000000001',\n", + " 'dominance_sd': '2.54',\n", + " 'word_frequency': '7',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'cell',\n", + " 'word_no': '587',\n", + " 'valence_mean': '3.8199999999999998',\n", + " 'valence_sd': '1.7',\n", + " 'arousal_mean': '4.0800000000000001',\n", + " 'arousal_sd': '2.1899999999999999',\n", + " 'dominance_mean': '4.1200000000000001',\n", + " 'dominance_sd': '2.1299999999999999',\n", + " 'word_frequency': '65',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'}],\n", + " 'test': [{'valence': 'neg',\n", + " 'description': 'maggot',\n", + " 'word_no': '269',\n", + " 'valence_mean': '2.0600000000000001',\n", + " 'valence_sd': '1.47',\n", + " 'arousal_mean': '5.2800000000000002',\n", + " 'arousal_sd': '2.96',\n", + " 'dominance_mean': '4.0300000000000002',\n", + " 'dominance_sd': '2.0899999999999999',\n", + " 'word_frequency': '2',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'lost',\n", + " 'word_no': '852',\n", + " 'valence_mean': '2.8199999999999998',\n", + " 'valence_sd': '1.8300000000000001',\n", + " 'arousal_mean': '5.8200000000000003',\n", + " 'arousal_sd': '2.6200000000000001',\n", + " 'dominance_mean': '2.8599999999999999',\n", + " 'dominance_sd': '1.6399999999999999',\n", + " 'word_frequency': '173',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'cell',\n", + " 'word_no': '587',\n", + " 'valence_mean': '3.8199999999999998',\n", + " 'valence_sd': '1.7',\n", + " 'arousal_mean': '4.0800000000000001',\n", + " 'arousal_sd': '2.1899999999999999',\n", + " 'dominance_mean': '4.1200000000000001',\n", + " 'dominance_sd': '2.1299999999999999',\n", + " 'word_frequency': '65',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'upset',\n", + " 'word_no': '465',\n", + " 'valence_mean': '2.0',\n", + " 'valence_sd': '1.1799999999999999',\n", + " 'arousal_mean': '5.8600000000000003',\n", + " 'arousal_sd': '2.3999999999999999',\n", + " 'dominance_mean': '4.0800000000000001',\n", + " 'dominance_sd': '2.3100000000000001',\n", + " 'word_frequency': '14',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'coffin',\n", + " 'word_no': '76',\n", + " 'valence_mean': '2.5600000000000001',\n", + " 'valence_sd': '1.96',\n", + " 'arousal_mean': '5.0300000000000002',\n", + " 'arousal_sd': '2.79',\n", + " 'dominance_mean': '4.0800000000000001',\n", + " 'dominance_sd': '2.54',\n", + " 'word_frequency': '7',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'death',\n", + " 'word_no': '100',\n", + " 'valence_mean': '1.6100000000000001',\n", + " 'valence_sd': '1.3999999999999999',\n", + " 'arousal_mean': '4.5899999999999999',\n", + " 'arousal_sd': '3.0699999999999998',\n", + " 'dominance_mean': '3.4700000000000002',\n", + " 'dominance_sd': '2.5',\n", + " 'word_frequency': '277',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neg'}]}]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import random\n", + "from csv import DictReader\n", + "import copy\n", + "\n", + "# function to make a study/test block from the pools past in\n", + "def gen_block(pools, cond, num_items):\n", + " # fill the study list\n", + " study_list = []\n", + " \n", + " # loop over pools\n", + " for pool in pools:\n", + " # loop over items to add from that pool\n", + " # this will be num_items/num_types for mixed lists\n", + " for i in range(num_items):\n", + " study_item = pool.pop()\n", + " study_item.update({'novelty': 'target', \n", + " 'cond': cond})\n", + " study_list.append(study_item)\n", + "\n", + " # shuffle the study_list\n", + " random.shuffle(study_list)\n", + " \n", + " # copy the study list to be the start of the test list\n", + " test_list = copy.deepcopy(study_list)\n", + " \n", + " # loop over pools\n", + " for pool in pools:\n", + " # loop over items to add from that pool\n", + " # this will be num_items/num_types for mixed lists\n", + " for i in range(num_items):\n", + " test_item = pool.pop()\n", + " test_item.update({'novelty': 'lure', \n", + " 'cond': cond})\n", + " test_list.append(test_item)\n", + " \n", + " # shuffle the test list\n", + " random.shuffle(test_list)\n", + " \n", + " return {'study': study_list, 'test': test_list}\n", + "\n", + "\n", + "# config variables\n", + "pos_file = 'pos_pool.csv'\n", + "neg_file = 'neg_pool.csv'\n", + "neu_file = 'neu_pool.csv'\n", + "\n", + "# number of pools\n", + "num_pools = 3\n", + "\n", + "# number of items in pure lists (must be evenly divisible by num_pools)\n", + "num_items_pure = 3\n", + "\n", + "# number of repetitions of each block type\n", + "num_reps = 1\n", + "\n", + "# verify these numbers make sense\n", + "num_items_mixed = int(num_items_pure / num_pools)\n", + "assert num_items_mixed * num_pools == num_items_pure\n", + "\n", + "\n", + "# load in the pools (must add in valence)\n", + "pos_pool = [dict({'valence': 'pos'}, **i) \n", + " for i in DictReader(open(pos_file, 'r'))]\n", + "neg_pool = [dict({'valence': 'neg'}, **i) \n", + " for i in DictReader(open(neg_file, 'r'))]\n", + "neu_pool = [dict({'valence': 'neu'}, **i) \n", + " for i in DictReader(open(neu_file, 'r'))]\n", + "\n", + "# print out number of items in each pool\n", + "print('pos_pool:', len(pos_pool))\n", + "print('neg_pool:', len(neg_pool))\n", + "print('neu_pool:', len(neu_pool))\n", + "\n", + "# shuffle the pools\n", + "random.shuffle(pos_pool)\n", + "random.shuffle(neg_pool)\n", + "random.shuffle(neu_pool)\n", + "\n", + "\n", + "# generate the blocks\n", + "blocks = []\n", + "for r in range(num_reps):\n", + " # generate a pure pos block\n", + " blocks.append(gen_block([pos_pool], 'pos', \n", + " num_items_pure))\n", + " \n", + " # generate a pure neg block\n", + " blocks.append(gen_block([neg_pool], 'neg', \n", + " num_items_pure))\n", + " \n", + " # generate a pure neu block\n", + " blocks.append(gen_block([neu_pool], 'neu', \n", + " num_items_pure))\n", + " \n", + " # generate a mixed pos/neg/neu block\n", + " blocks.append(gen_block([pos_pool, neg_pool, neu_pool], \n", + " 'mixed', num_items_mixed))\n", + "\n", + "# shuffle the blocks\n", + "random.shuffle(blocks)\n", + "\n", + "# let's see how many items we have left\n", + "\n", + "\n", + "blocks" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO ] [Logger ] Record log in /Users/uva/.kivy/logs/kivy_20-10-21_0.txt\n", + "[INFO ] [Kivy ] v1.11.1\n", + "[INFO ] [Kivy ] Installed at \"/Users/uva/opt/anaconda3/envs/compsy/lib/python3.7/site-packages/kivy/__init__.py\"\n", + "[INFO ] [Python ] v3.7.7 (default, May 6 2020, 04:59:01) \n", + "[Clang 4.0.1 (tags/RELEASE_401/final)]\n", + "[INFO ] [Python ] Interpreter at \"/Users/uva/opt/anaconda3/envs/compsy/bin/python\"\n", + "[INFO ] [Factory ] 184 symbols loaded\n", + "[INFO ] [Image ] Providers: img_tex, img_imageio, img_dds, img_sdl2, img_pil, img_gif (img_ffpyplayer ignored)\n", + "[INFO ] [Text ] Provider: sdl2\n", + "[INFO ] [Camera ] Provider: avfoundation\n", + "[INFO ] [VideoGstplayer] Using Gstreamer 1.14.5.0\n", + "[INFO ] [Video ] Provider: gstplayer\n", + "[WARNING] [SMILE ] Unable to import PYO!\n", + "[WARNING] [SMILE ] Durations will be maintained, unless none are specified\n", + "[INFO ] [Window ] Provider: sdl2\n", + "[INFO ] [GL ] Using the \"OpenGL ES 2\" graphics system\n", + "[INFO ] [GL ] Backend used \n", + "[INFO ] [GL ] OpenGL version \n", + "[INFO ] [GL ] OpenGL vendor \n", + "[INFO ] [GL ] OpenGL renderer \n", + "[INFO ] [GL ] OpenGL parsed version: 2, 1\n", + "[INFO ] [GL ] Shading version \n", + "[INFO ] [GL ] Texture max size <16384>\n", + "[INFO ] [GL ] Texture max units <16>\n", + "[INFO ] [Window ] auto add sdl2 input provider\n", + "[INFO ] [Window ] virtual keyboard not allowed, single mode, not docked\n", + "[INFO ] [Base ] Start application main loop\n", + "[INFO ] [GL ] NPOT texture support is available\n", + "[INFO ] [Base ] Leaving application in progress...\n", + "[INFO ] [WindowSDL ] exiting mainloop and closing.\n" + ] + } + ], + "source": [ + "# Load in the most common SMILE states\n", + "from smile.common import * \n", + "from smile.scale import scale as s\n", + "from smile.startup import InputSubject\n", + "\n", + "# enter configuration variables here (including the listgen variables)\n", + "font_size = 75\n", + "resp_keys = ['LEFT', 'RIGHT']\n", + "resp_map = {'lure': 'LEFT', 'target': 'RIGHT'}\n", + "ISI_dur = 0.5\n", + "ISI_jitter = 0.5\n", + "LOC_X_jitter = 200\n", + "LOC_Y_jitter = 100\n", + "inst_font_size = 50\n", + "stim_time = 2\n", + "inst_text = \"\"\"\n", + " \n", + "[u]STUDY PORTION:[/u] You will be presented with a list of study words \n", + " - Try to remember the presented words\n", + " \n", + " - You do not need to click anything in the section of the experiment\n", + " \n", + "[u]TEST PORTION:[/u] You will then be tested with another list of words\n", + " - Once a word is presented respond as quickly as you can with the following:\n", + " \n", + " - Hit the RIGHT ARROW key if you HAVE seen the word (OLD/Studied Words)\n", + " \n", + " - Hit the LEFT ARROW key if you HAVE NOT seen the word (NEW/ Unstudied Words)\n", + " \n", + " \n", + "\"\"\"\n", + "\n", + "\n", + "study_text = \"\"\"\n", + "YOU ARE ABOUT TO START THE [u]STUDY PORTION[/u] OF THIS BLOCK\n", + "\n", + "- Words will appear on the screen\n", + "- Try to remember the presented words\n", + "- Do not press anything\n", + "\n", + "Press the ENTER key to\\nstart. \n", + "\n", + "\n", + "\n", + "\"\"\"\n", + "\n", + "test_text = '''\n", + "YOU ARE ABOUT TO START THE [u]TEST PORTION[/u] OF THIS BLOCK\n", + "\n", + "- Words will appear on the screen\n", + "- Hit the RIGHT ARROW key if you HAVE seen the word (OLD/Studied Words)\n", + "- Hit the LEFT ARROW key if you HAVE NOT seen the word (NEW/ Unstudied Words)\n", + "\n", + "Press the ENTER key to\\nstart. \n", + "\n", + "\n", + "\n", + "'''\n", + "# listgen solution added to previous block\n", + "# create an experiment instance\n", + "exp = Experiment(name=\"VALENCE\", fullscreen=False,show_splash=False ,resolution=(1024,768), scale_box=(1024, 768))\n", + "\n", + "# YOUR CODE HERE TO BUILD THE STATE MACHINE\n", + "@Subroutine\n", + "def Instruct(self):\n", + " # show the instructions\n", + " with Parallel():\n", + " top = Label(text=inst_text, font_size=inst_font_size,\n", + " text_size=(exp.screen.width*0.75, None),halign=\"left\",\n", + " markup=True)\n", + " Label(text=\"[u]MEMORY RECOGNITION TEST INSTRUCTIONS[/u]\", font_size=font_size,\n", + " text_size=(exp.screen.width*0.75, None),halign=\"center\",\n", + " markup=True, center_bottom = top.center_top)\n", + " Label(text=\"Press ENTER key to continue.\", font_size=inst_font_size,\n", + " text_size=(exp.screen.width*0.75, None),halign=\"center\",\n", + " markup=True, center_top=top.center_bottom)\n", + " with UntilDone():\n", + " KeyPress(keys=['ENTER'])\n", + "\n", + " \n", + "@Subroutine\n", + "def Trial(self, block_num, trial_num, cur_trial):\n", + " # present the stimulus\n", + " stim = Label(text=cur_trial['description'],\n", + " font_size=font_size)\n", + " with UntilDone():\n", + " # make sure the stimulus has appeared on the screen\n", + " Wait(until=stim.appear_time)\n", + " \n", + " # collect a response (with no timeout)\n", + " kp = KeyPress(keys=resp_keys, \n", + " base_time=stim.appear_time['time'],\n", + " correct_resp=Ref.object(resp_map)[cur_trial['novelty']])\n", + "\n", + " # log the result of the trial\n", + " Log(name='valence_test', \n", + " log_dict=cur_trial,\n", + " block_num=block_num,\n", + " trial_num=trial_num,\n", + " stim_on=stim.appear_time,\n", + " resp=kp.pressed,\n", + " resp_time=kp.press_time,\n", + " rt=kp.rt,\n", + " correct=kp.correct\n", + " )\n", + "\n", + "@Subroutine\n", + "def Study(self, block_num, trial_num, cur_trial):\n", + " # present the stimulus\n", + " stim = Label(text=cur_trial['description'],\n", + " font_size=font_size)\n", + " with UntilDone():\n", + " # make sure the stimulus has appeared on the screen\n", + " Wait(until=stim.appear_time)\n", + " Wait(1)\n", + " \n", + " # collect a response (with no timeout)\n", + " \n", + " # wait the ISI with jitter\n", + " Wait(ISI_dur, jitter=ISI_jitter)\n", + " # log the result of the trial\n", + " Log(name='valence_study', \n", + " log_dict=cur_trial,\n", + " block_num=block_num,\n", + " trial_num=trial_num,\n", + " stim_on=stim.appear_time\n", + " )\n", + "\n", + " \n", + "\n", + "\n", + "# Get the subj id information\n", + "InputSubject('VALENCE')\n", + "\n", + "Label(text='Welcome!\\n\\nPress the ENTER key to view the instructions ',\n", + " font_size=font_size, halign='center',text_size=(exp.screen.width*0.75, None))\n", + "with UntilDone():\n", + " KeyPress()\n", + "\n", + " \n", + " \n", + "# show the instructions\n", + "Instruct()\n", + "\n", + "\n", + "Wait(0.5)\n", + "# Study(0, 0, blocks[0]['test'][0])\n", + "# Trial(0, 0, blocks[0]['test'][0])\n", + "\n", + "# # loop over the blocks\n", + "with Loop(blocks) as block:\n", + " # make sure they are ready to continue\n", + " Label(text='Press the ENTER key to\\nstart the next block.', \n", + " font_size=font_size, text_size=(exp.screen.width*0.75, None),halign='center')\n", + " with UntilDone():\n", + " KeyPress(keys=['ENTER'])\n", + " \n", + " \n", + " Label(text=study_text, \n", + " font_size=font_size, text_size=(exp.screen.width*0.75, None),markup=True,halign=\"center\")\n", + " with UntilDone():\n", + " KeyPress(keys=['ENTER'])\n", + " \n", + " with Loop(block.current['study']) as study:\n", + " Study(block.i, study.i, study.current)\n", + "\n", + " \n", + " \n", + " # delay before the start of the test block\n", + " Wait(ISI_dur, jitter=ISI_jitter)\n", + " \n", + " \n", + " Label(text=test_text, \n", + " font_size=font_size, text_size=(exp.screen.width*0.75, None),markup=True,halign=\"center\")\n", + " with UntilDone():\n", + " KeyPress(keys=['ENTER'])\n", + " \n", + " # loop over the trials\n", + " with Loop(block.current['test']) as trial:\n", + " Trial(block.i, trial.i, trial.current)\n", + "\n", + "# make sure they are ready to continue\n", + "Label(text='You are all done!!!\\nPress the ENTER key to go celebrate.', \n", + " font_size=font_size, halign='center',text_size=(exp.screen.width*0.75, None))\n", + "with UntilDone():\n", + " KeyPress(keys=['ENTER'])\n", + "\n", + "\n", + "# run the experiment\n", + "exp.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log_sysinfo_0.slog state_MouseCursor_0.slog\r\n", + "log_valence_study_0.slog state_Parallel_0.slog\r\n", + "log_valence_test_0.slog state_ParentSet_0.slog\r\n", + "state_ButtonPress_0.slog state_ProgressBar_0.slog\r\n", + "state_Button_0.slog state_Rectangle_0.slog\r\n", + "state_Elif_0.slog state_ResetClock_0.slog\r\n", + "state_Func_0.slog state_Serial_0.slog\r\n", + "state_If_0.slog state_SubroutineState_0.slog\r\n", + "state_Image_0.slog state_TextInput_0.slog\r\n", + "state_KeyPress_0.slog state_UpdateWidget_0.slog\r\n", + "state_Label_0.slog state_Wait_0.slog\r\n", + "state_Loop_0.slog\r\n" + ] + }, + { + "data": { + "text/html": [ + "
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block_numtrial_numstim_on_timestim_on_errorlog_timevalencedescriptionword_novalence_meanvalence_sdarousal_meanarousal_sddominance_meandominance_sdword_frequencynoveltycondlog_num
00066.9464380.068.935449negfeeble1643.25999999999999981.474.09999999999999962.06999999999999982.711.63999999999999998targetneg0
10168.9629510.070.633295negsissy3943.14000000000000011.965.16999999999999992.56999999999999983.58000000000000012.7400000000000002.targetneg0
20270.6464510.072.364484negdreadful1312.25999999999999981.90999999999999995.83999999999999992.62000000000000014.09999999999999962.359999999999999910targetneg0
310106.9461750.0108.722277posintercourse8197.36000000000000031.57000000000000017.02.06999999999999986.40000000000000041.789targetneg0
411108.7461830.0110.480243posmoney2827.58999999999999991.39999999999999995.70000000000000022.66000000000000016.252.3300000000000001265targetneg0
512110.4964670.0112.413917posrestaurant9606.75999999999999981.85000000000000015.41000000000000012.54999999999999985.73000000000000041.409999999999999941targetneg0
620124.3791760.0126.282652neucat5045.71999999999999982.43000000000000024.37999999999999992.24000000000000026.16000000000000012.0499999999999998.targetneg0
721126.2959420.0127.981473neubarrel6515.04999999999999981.463.35999999999999992.27999999999999984.88999999999999971.570000000000000124targetneg0
822127.9958120.0129.694722neunonsense9054.61000000000000031.62999999999999994.16999999999999992.024.90000000000000041.5513targetneg0
930142.0622150.0143.973046pospromotion3328.19999999999999931.14999999999999996.44000000000000042.58000000000000016.792.279999999999999826targetmixed0
1031143.9958180.0145.778513negtorture4451.56000000000000010.790000000000000046.09999999999999962.773.33000000000000012.37000000000000013targetmixed0
1132145.7955380.0147.561860neuhotel7956.01.774.79999999999999982.52999999999999985.12000000000000011.8400000000000001126targetmixed0
\n", + "
" + ], + "text/plain": [ + " block_num trial_num stim_on_time stim_on_error log_time valence \\\n", + "0 0 0 66.946438 0.0 68.935449 neg \n", + "1 0 1 68.962951 0.0 70.633295 neg \n", + "2 0 2 70.646451 0.0 72.364484 neg \n", + "3 1 0 106.946175 0.0 108.722277 pos \n", + "4 1 1 108.746183 0.0 110.480243 pos \n", + "5 1 2 110.496467 0.0 112.413917 pos \n", + "6 2 0 124.379176 0.0 126.282652 neu \n", + "7 2 1 126.295942 0.0 127.981473 neu \n", + "8 2 2 127.995812 0.0 129.694722 neu \n", + "9 3 0 142.062215 0.0 143.973046 pos \n", + "10 3 1 143.995818 0.0 145.778513 neg \n", + "11 3 2 145.795538 0.0 147.561860 neu \n", + "\n", + " description word_no valence_mean valence_sd \\\n", + "0 feeble 164 3.2599999999999998 1.47 \n", + "1 sissy 394 3.1400000000000001 1.96 \n", + "2 dreadful 131 2.2599999999999998 1.9099999999999999 \n", + "3 intercourse 819 7.3600000000000003 1.5700000000000001 \n", + "4 money 282 7.5899999999999999 1.3999999999999999 \n", + "5 restaurant 960 6.7599999999999998 1.8500000000000001 \n", + "6 cat 504 5.7199999999999998 2.4300000000000002 \n", + "7 barrel 651 5.0499999999999998 1.46 \n", + "8 nonsense 905 4.6100000000000003 1.6299999999999999 \n", + "9 promotion 332 8.1999999999999993 1.1499999999999999 \n", + "10 torture 445 1.5600000000000001 0.79000000000000004 \n", + "11 hotel 795 6.0 1.77 \n", + "\n", + " arousal_mean arousal_sd dominance_mean \\\n", + "0 4.0999999999999996 2.0699999999999998 2.71 \n", + "1 5.1699999999999999 2.5699999999999998 3.5800000000000001 \n", + "2 5.8399999999999999 2.6200000000000001 4.0999999999999996 \n", + "3 7.0 2.0699999999999998 6.4000000000000004 \n", + "4 5.7000000000000002 2.6600000000000001 6.25 \n", + "5 5.4100000000000001 2.5499999999999998 5.7300000000000004 \n", + "6 4.3799999999999999 2.2400000000000002 6.1600000000000001 \n", + "7 3.3599999999999999 2.2799999999999998 4.8899999999999997 \n", + "8 4.1699999999999999 2.02 4.9000000000000004 \n", + "9 6.4400000000000004 2.5800000000000001 6.79 \n", + "10 6.0999999999999996 2.77 3.3300000000000001 \n", + "11 4.7999999999999998 2.5299999999999998 5.1200000000000001 \n", + "\n", + " dominance_sd word_frequency novelty cond log_num \n", + "0 1.6399999999999999 8 target neg 0 \n", + "1 2.7400000000000002 . target neg 0 \n", + "2 2.3599999999999999 10 target neg 0 \n", + "3 1.78 9 target neg 0 \n", + "4 2.3300000000000001 265 target neg 0 \n", + "5 1.4099999999999999 41 target neg 0 \n", + "6 2.0499999999999998 . target neg 0 \n", + "7 1.5700000000000001 24 target neg 0 \n", + "8 1.55 13 target neg 0 \n", + "9 2.2799999999999998 26 target mixed 0 \n", + "10 2.3700000000000001 3 target mixed 0 \n", + "11 1.8400000000000001 126 target mixed 0 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from smile.log import log2dl\n", + "!ls data/VALENCE/Maya/20201020_134457\n", + "dl = log2dl('data/VALENCE/Maya/20201020_134457/log_valence_study_0.slog')\n", + "dl\n", + "\n", + "import pandas as pd\n", + "pd.set_option('display.max_columns', None)\n", + "df = pd.DataFrame(dl)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/assignments/A05_SMILE_Experiment.ipynb b/assignments/A05_SMILE_Experiment.ipynb index be2c599..6773e66 100644 --- a/assignments/A05_SMILE_Experiment.ipynb +++ b/assignments/A05_SMILE_Experiment.ipynb @@ -110,7 +110,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.7" } }, "nbformat": 4, diff --git a/assignments/A07_Recog_Plots-jhr3kd.ipynb b/assignments/A07_Recog_Plots-jhr3kd.ipynb new file mode 100644 index 0000000..5a8e98b --- /dev/null +++ b/assignments/A07_Recog_Plots-jhr3kd.ipynb @@ -0,0 +1,725 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Assignment 7: Recog Task Plots\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Objectives\n", + "\n", + "Upon completion of this assignment, students will have:\n", + "\n", + "1. Read in all the recognition memory data\n", + "2. Performed some simple data clean-up (code provided)\n", + "3. Plotted general recognition results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Assignment\n", + "\n", + "* Write code in a Jupyter notebook (after making a copy and renaming it to have your userid in the title --- e.g., A06_Recog_Plots_mst3k).\n", + "\n", + "\n", + "## Details\n", + "\n", + "Below is code that will load in the data from the two recognition memory experiments. As long as you have updated this repository from GitHub and unzipped the `all_data.zip` file in the `lessons` directory, the code should work unchanged to load in the data, create two data frames, and perform some minor clean-up of the data.\n", + "\n", + "Your task is to make some plots that explore some initial questions about the data (we'll perform some statistical tests on the data next week, taking a deeper dive into how to analyze recognition memory experiments.)\n", + "\n", + "For each of the two tasks (i.e., you will be generating two total figures), you will be plotting performance as a function of type (e.g., indoor/outdoor or valence), split out by condition (mixed or pure). These plots must have within-subject corrected error bars that you calculate making use of the `ci_within` function.\n", + "\n", + "Be sure to refer to the class notebooks to help guide how to do all the steps above. We have some code below to help you get started, along with demonstrations of approximately what the figures will look like when done.\n", + "\n", + "* ***When you are done, save this notebook as HTML (`File -> Download as -> HTML`) and upload it to the matching assignment on UVACollab.*** " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## General Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy import stats\n", + "import pandas as pd\n", + "from glob import glob\n", + "import os\n", + "\n", + "from smile.log import log2dl\n", + "\n", + "from ci_within import ci_within" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Custom SLOG loading function" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# custom function to load slogs\n", + "def load_all_subj_logs(task_dir, log_file):\n", + " # load in a list of all the subj\n", + " subjs = [os.path.split(subj_dir)[-1] \n", + " for subj_dir in glob(os.path.join(task_dir, 's*'))]\n", + " subjs.sort()\n", + "\n", + " # loop over subj and their data\n", + " all_dat = []\n", + " for subj in subjs:\n", + " # set the file\n", + " log_path = os.path.join(task_dir, subj, log_file)\n", + " #print(log_path)\n", + "\n", + " # load the data\n", + " all_dat.extend(log2dl(log_path, subj=subj))\n", + "\n", + " df = pd.DataFrame(all_dat)\n", + " \n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Load in all the data" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " resp_map_lure resp_map_target block_num trial_num stim_on_time \\\n", + "0 F J 0 0 2361.470167 \n", + "1 F J 0 1 2363.392059 \n", + "2 F J 0 2 2364.572868 \n", + "3 F J 0 3 2365.874493 \n", + "4 F J 0 4 2367.592558 \n", + "\n", + " stim_on_error resp resp_time_time resp_time_error rt correct \\\n", + "0 0.0 F 2362.502265 0.000391 1.032098 True \n", + "1 0.0 J 2363.993073 0.001033 0.601014 True \n", + "2 0.0 F 2365.363671 0.000197 0.790803 True \n", + "3 0.0 F 2366.714544 0.000191 0.840051 True \n", + "4 0.0 F 2368.463209 0.000248 0.870651 True \n", + "\n", + " log_time filename in_out novelty cond subj log_num \n", + "0 2363.385215 out2646.jpg outdoor lure outdoor s001 0 \n", + "1 2364.559602 out0031_new.jpg outdoor target outdoor s001 0 \n", + "2 2365.870152 out1227.jpg outdoor lure outdoor s001 0 \n", + "3 2367.588254 out0134_new.jpg outdoor lure outdoor s001 0 \n", + "4 2369.152451 out2086.jpg outdoor lure outdoor s001 0 " + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data from each task\n", + "task_dir = os.path.join('..', 'lessons', 'data2', 'Taskapalooza')\n", + "\n", + "df_i = load_all_subj_logs(task_dir, 'log_image_test')\n", + "df_w = load_all_subj_logs(task_dir, 'log_word_test')\n", + "df_i.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Some data clean-up" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# it turns out the cond is easier to visualize as pure and mixed\n", + "def fix_conds(df, type_col):\n", + " # loop over the unique subjects\n", + " usubj = df.subj.unique()\n", + " for s in usubj:\n", + " # loop over their blocks\n", + " ublocks = df.loc[df['subj']==s, 'block_num'].unique()\n", + " for b in ublocks:\n", + " # grab the data for that subj and block\n", + " dfb = df.loc[(df['subj']==s)&(df['block_num']==b)]\n", + " \n", + " # get the unique types in that block\n", + " uval = dfb[type_col].unique()\n", + " if len(uval) > 1:\n", + " # it's mixed\n", + " df.loc[(df['subj']==s)&(df.block_num==b), 'cond'] = 'mixed'\n", + " else:\n", + " # it's the pure\n", + " df.loc[(df['subj']==s)&(df.block_num==b), 'cond'] = 'pure'\n", + "\n", + "# fix the conds in the recog experiments (updated in place)\n", + "fix_conds(df_i, type_col='in_out')\n", + "fix_conds(df_w, type_col='valence')\n", + "\n", + "\n", + "# add in log_rt columns\n", + "df_i['log_rt'] = np.log(df_i['rt'])\n", + "df_w['log_rt'] = np.log(df_w['rt'])\n", + "\n", + "# must make correct an int\n", + "df_i['correct'] = df_i['correct'].astype(np.int)\n", + "df_w['correct'] = df_w['correct'].astype(np.int)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Image Recognition Figure\n", + "\n", + "Replace sections marked with `XXX` with the proper code to generate and plot the correct data.\n", + "\n", + "You are making a plot of recognition performance as a function of location (indoor vs. outdoor), grouped by condition (mixed vs. pure)." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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meanstdsemcilen
condin_out
mixedindoor0.8236710.4196730.0103130.0202281656.0
outdoor0.8007250.4458280.0109560.0214881656.0
pureindoor0.8260870.4308970.0074870.0146803312.0
outdoor0.7750600.4709160.0081830.0160443312.0
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" + ], + "text/plain": [ + " mean std sem ci len\n", + "cond in_out \n", + "mixed indoor 0.823671 0.419673 0.010313 0.020228 1656.0\n", + " outdoor 0.800725 0.445828 0.010956 0.021488 1656.0\n", + "pure indoor 0.826087 0.430897 0.007487 0.014680 3312.0\n", + " outdoor 0.775060 0.470916 0.008183 0.016044 3312.0" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get the error corrected by condition and whether they got it correct\n", + "res = ci_within(df_i, \n", + " indexvar='subj', # column that identifies a subject\n", + " withinvars=['cond','in_out'], # list of columns for grouping within subject\n", + " measvar='correct') # dependent variable averaging over\n", + "res" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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condmeanstdsemcilen
in_outindooroutdoorindooroutdoorindooroutdoorindooroutdoorindooroutdoor
0mixed0.8236710.8007250.4196730.4458280.0103130.0109560.0202280.0214881656.01656.0
1pure0.8260870.7750600.4308970.4709160.0074870.0081830.0146800.0160443312.03312.0
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" + ], + "text/plain": [ + " cond mean std sem \\\n", + "in_out indoor outdoor indoor outdoor indoor outdoor \n", + "0 mixed 0.823671 0.800725 0.419673 0.445828 0.010313 0.010956 \n", + "1 pure 0.826087 0.775060 0.430897 0.470916 0.007487 0.008183 \n", + "\n", + " ci len \n", + "in_out indoor outdoor indoor outdoor \n", + "0 0.020228 0.021488 1656.0 1656.0 \n", + "1 0.014680 0.016044 3312.0 3312.0 " + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# must unstack and reset index to plot properly\n", + "res.unstack().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Performance')" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the results\n", + "ax = res.unstack().reset_index().plot(x='cond', y='mean', yerr='ci', kind=\"bar\")\n", + "#ax.get_legend().remove()\n", + "ax.set_xlabel('Condition')\n", + "ax.set_ylabel('Performance')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Word Recognition Figure\n", + "\n", + "Replace the sections marked with `XXX` to generate and plot the correct data.\n", + "\n", + "You are plotting recognition performance as a function of valence (negative, neutral, positive), grouped by condition (mixed vs. pure)." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Performance')" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# get the error corrected by condition and whether they got it correct\n", + "res = ci_within(df_w, \n", + " indexvar='subj', # column that identifies a subject\n", + " withinvars=['cond','valence'], # list of columns for grouping within subject\n", + " measvar='correct') # dependent variable averaging over\n", + "\n", + "# generate the plot\n", + "ax = res.unstack().reset_index().plot(x='cond', y='mean', yerr='ci', kind=\"bar\")\n", + "ax.set_xlabel('Condition')\n", + "ax.set_ylabel('Performance')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assignments/A07_Recog_Plots.ipynb b/assignments/A07_Recog_Plots.ipynb index 3aced98..2adb362 100644 --- a/assignments/A07_Recog_Plots.ipynb +++ b/assignments/A07_Recog_Plots.ipynb @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -136,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -313,14 +313,14 @@ "4 2369.152451 out2086.jpg outdoor lure outdoor s001 0 " ] }, - "execution_count": 3, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# load the data from each task\n", - "task_dir = os.path.join('..', 'lessons', 'data', 'Taskapalooza')\n", + "task_dir = os.path.join('..', 'lessons','data', 'Taskapalooza')\n", "\n", "df_i = load_all_subj_logs(task_dir, 'log_image_test')\n", "df_w = load_all_subj_logs(task_dir, 'log_word_test')\n", @@ -340,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -395,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -428,7 +428,7 @@ " \n", " \n", " cond\n", - " in_out\n", + " correct\n", " \n", " \n", " \n", @@ -439,37 +439,37 @@ " \n", " \n", " mixed\n", - " indoor\n", - " 0.823671\n", - " 0.419673\n", - " 0.010313\n", - " 0.020228\n", - " 1656.0\n", + " 0\n", + " -0.082751\n", + " 0.419876\n", + " 0.016835\n", + " 0.033061\n", + " 622.0\n", " \n", " \n", - " outdoor\n", - " 0.800725\n", - " 0.445828\n", - " 0.010956\n", - " 0.021488\n", - " 1656.0\n", + " 1\n", + " -0.202933\n", + " 0.341504\n", + " 0.006584\n", + " 0.012911\n", + " 2690.0\n", " \n", " \n", " pure\n", - " indoor\n", - " 0.826087\n", - " 0.430897\n", - " 0.007487\n", - " 0.014680\n", - " 3312.0\n", + " 0\n", + " -0.070994\n", + " 0.429014\n", + " 0.011804\n", + " 0.023156\n", + " 1321.0\n", " \n", " \n", - " outdoor\n", - " 0.775060\n", - " 0.470916\n", - " 0.008183\n", - " 0.016044\n", - " 3312.0\n", + " 1\n", + " -0.198215\n", + " 0.344054\n", + " 0.004725\n", + " 0.009262\n", + " 5303.0\n", " \n", " \n", "\n", @@ -477,14 +477,14 @@ ], "text/plain": [ " mean std sem ci len\n", - "cond in_out \n", - "mixed indoor 0.823671 0.419673 0.010313 0.020228 1656.0\n", - " outdoor 0.800725 0.445828 0.010956 0.021488 1656.0\n", - "pure indoor 0.826087 0.430897 0.007487 0.014680 3312.0\n", - " outdoor 0.775060 0.470916 0.008183 0.016044 3312.0" + "cond correct \n", + "mixed 0 -0.082751 0.419876 0.016835 0.033061 622.0\n", + " 1 -0.202933 0.341504 0.006584 0.012911 2690.0\n", + "pure 0 -0.070994 0.429014 0.011804 0.023156 1321.0\n", + " 1 -0.198215 0.344054 0.004725 0.009262 5303.0" ] }, - "execution_count": 16, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -494,7 +494,7 @@ "res = ci_within(df_i, \n", " indexvar='XXX', # column that identifies a subject\n", " withinvars=[XXX], # list of columns for grouping within subject\n", - " measvar='XXX') # dependent variable averaging over\n", + " measvar='xxx') # dependent variable averaging over\n", "res" ] }, diff --git a/assignments/A08_DPrime_Plot-jhr3kd.ipynb b/assignments/A08_DPrime_Plot-jhr3kd.ipynb new file mode 100644 index 0000000..037c6d4 --- /dev/null +++ b/assignments/A08_DPrime_Plot-jhr3kd.ipynb @@ -0,0 +1,1173 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Assignment 8: D-Prime Plot\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Objectives\n", + "\n", + "Upon completion of this assignment, students will have:\n", + "\n", + "1. Read in all the recognition memory data\n", + "2. Performed some simple data clean-up (code provided)\n", + "3. Calculated d-prime for the word recognition task\n", + "4. Plotted d-prime as a function of valence and condition" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Assignment\n", + "\n", + "* Write code in a Jupyter notebook (after making a copy and renaming it to have your userid in the title --- e.g., A08_DPrime_Plot_mst3k).\n", + "\n", + "\n", + "## Details\n", + "\n", + "Below is code that will load in the data from the two recognition memory experiments. As long as you have updated this repository from GitHub and unzipped the `all_data.zip` file in the `lessons` directory, the code should work unchanged to load in the data, create two data frames, and perform some minor clean-up of the data.\n", + "\n", + "Your task is to calculate d-prime for the word recognition data and then plot the result as a function of valence (negative, neutral, positive) and condition (mixed and pure).\n", + "\n", + "All the code you need to perform this analysis is in the most recent lesson notebook. You will need to identify the correct pieces of code to copy into this notebook and how to modify it to examine valence as opposed to image location. \n", + "\n", + "We have some code below to help you get started reading in the data, so that you can focus on the d-prime calculation and plot.\n", + "\n", + "* ***When you are done, save this notebook as HTML (`File -> Download as -> HTML`) and upload it to the matching assignment on UVACollab.*** " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# New library to install\n", + "\n", + "You're going to need a new plotting library, so run this line at your Anaconda Prompt/Terminal:\n", + "\n", + "`conda install -c conda-forge plotnine` " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## General Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "# import some useful libraries\n", + "import numpy as np # numerical analysis linear algebra\n", + "import pandas as pd # efficient tables\n", + "import matplotlib.pyplot as plt # plotting\n", + "import plotnine as pn \n", + "import scipy.stats.distributions as dists # probability distributions\n", + "from scipy import stats\n", + "from glob import glob\n", + "import os\n", + "\n", + "from smile.log import log2dl\n", + "\n", + "from ci_within import ci_within" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Custom SLOG loading function" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "# custom function to load slogs\n", + "def load_all_subj_logs(task_dir, log_file):\n", + " # load in a list of all the subj\n", + " subjs = [os.path.split(subj_dir)[-1] \n", + " for subj_dir in glob(os.path.join(task_dir, 's*'))]\n", + " subjs.sort()\n", + "\n", + " # loop over subj and their data\n", + " all_dat = []\n", + " for subj in subjs:\n", + " # set the file\n", + " log_path = os.path.join(task_dir, subj, log_file)\n", + " #print(log_path)\n", + "\n", + " # load the data\n", + " all_dat.extend(log2dl(log_path, subj=subj))\n", + "\n", + " df = pd.DataFrame(all_dat)\n", + " \n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Load in all the data" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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resp_map_lureresp_map_targetblock_numtrial_numstim_on_timestim_on_errorrespresp_time_timeresp_time_errorrt...valence_sdarousal_meanarousal_sddominance_meandominance_sdword_frequencynoveltycondsubjlog_num
0FJ00234.3955110.0J235.2848330.0001800.889323...1.57000000000000015.30999999999999962.235.462.04999999999999983targetneus0010
1FJ01235.8856540.0F237.0346700.0001821.149016...1.54.12000000000000011.83000000000000015.66000000000000011.7812lureneus0010
2FJ02237.6168690.0F238.7674060.0002381.150537...1.82000000000000015.45000000000000022.14999999999999994.63999999999999972.069999999999999816lureneus0010
3FJ03239.6249330.0F240.4322950.0001820.807362...1.243.95000000000000022.58000000000000015.37000000000000011.639999999999999919lureneus0010
4FJ04241.4322090.0F242.5452270.0001921.113017...2.16000000000000013.68000000000000022.56999999999999985.83000000000000011.549lureneus0010
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5 rows × 26 columns

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" + ], + "text/plain": [ + " resp_map_lure resp_map_target block_num trial_num stim_on_time \\\n", + "0 F J 0 0 234.395511 \n", + "1 F J 0 1 235.885654 \n", + "2 F J 0 2 237.616869 \n", + "3 F J 0 3 239.624933 \n", + "4 F J 0 4 241.432209 \n", + "\n", + " stim_on_error resp resp_time_time resp_time_error rt ... \\\n", + "0 0.0 J 235.284833 0.000180 0.889323 ... \n", + "1 0.0 F 237.034670 0.000182 1.149016 ... \n", + "2 0.0 F 238.767406 0.000238 1.150537 ... \n", + "3 0.0 F 240.432295 0.000182 0.807362 ... \n", + "4 0.0 F 242.545227 0.000192 1.113017 ... \n", + "\n", + " valence_sd arousal_mean arousal_sd \\\n", + "0 1.5700000000000001 5.3099999999999996 2.23 \n", + "1 1.5 4.1200000000000001 1.8300000000000001 \n", + "2 1.8200000000000001 5.4500000000000002 2.1499999999999999 \n", + "3 1.24 3.9500000000000002 2.5800000000000001 \n", + "4 2.1600000000000001 3.6800000000000002 2.5699999999999998 \n", + "\n", + " dominance_mean dominance_sd word_frequency novelty cond subj \\\n", + "0 5.46 2.0499999999999998 3 target neu s001 \n", + "1 5.6600000000000001 1.78 12 lure neu s001 \n", + "2 4.6399999999999997 2.0699999999999998 16 lure neu s001 \n", + "3 5.3700000000000001 1.6399999999999999 19 lure neu s001 \n", + "4 5.8300000000000001 1.5 49 lure neu s001 \n", + "\n", + " log_num \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "\n", + "[5 rows x 26 columns]" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data from the word recog task\n", + "task_dir = os.path.join('..', 'lessons', 'data2', 'Taskapalooza')\n", + "\n", + "df_w = load_all_subj_logs(task_dir, 'log_word_test')\n", + "df_w.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Some data clean-up" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "# it turns out the cond is easier to visualize as pure and mixed\n", + "def fix_conds(df, type_col):\n", + " # loop over the unique subjects\n", + " usubj = df.subj.unique()\n", + " for s in usubj:\n", + " # loop over their blocks\n", + " ublocks = df.loc[df['subj']==s, 'block_num'].unique()\n", + " for b in ublocks:\n", + " # grab the data for that subj and block\n", + " dfb = df.loc[(df['subj']==s)&(df['block_num']==b)]\n", + " \n", + " # get the unique types in that block\n", + " uval = dfb[type_col].unique()\n", + " if len(uval) > 1:\n", + " # it's mixed\n", + " df.loc[(df['subj']==s)&(df.block_num==b), 'cond'] = 'mixed'\n", + " else:\n", + " # it's the pure\n", + " df.loc[(df['subj']==s)&(df.block_num==b), 'cond'] = 'pure'\n", + "\n", + "# fix the conds in the recog experiments (updated in place)\n", + "fix_conds(df_w, type_col='valence')\n", + "\n", + "\n", + "# add in log_rt columns\n", + "df_w['log_rt'] = np.log(df_w['rt'])\n", + "\n", + "# must make correct an int\n", + "df_w['correct'] = df_w['correct'].astype(np.int)\n", + "\n", + "# add in a column for whether they made an 'old' response\n", + "df_w['old_resp'] = (df_w['resp_map_target'] == df_w['resp']).astype(np.int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculating sensitivity\n", + "\n", + "- Under assumptions of equal variance for both the signal and noise distributions, the d' (d-prime) is the measure of sensitivity\n", + "\n", + "$$d' = ((\\mu + \\alpha) - \\mu) / \\sigma$$\n", + "$$d' = \\alpha / \\sigma$$\n", + "\n", + "- Thus, $d'$ is the difference between the two distributions in units of the standard deviation\n", + "- Note, this is independent of the criterion\n" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "def calc_dprime(n_hits, n_targets, n_false_alarms, n_lures):\n", + " # calculate corrected hit rate and false alarm rate (to avoid zeros)\n", + " hr_trans = (n_hits+.5)/(n_targets+1)\n", + " far_trans = (n_false_alarms+.5)/(n_lures+1)\n", + " \n", + " # calculate dprime\n", + " Z = dists.norm.ppf\n", + " dprime = Z(hr_trans) - Z(far_trans)\n", + " return dprime" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Your code goes below here\n", + "\n", + "All code above should work without modification." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjcondvalencesumcountmean
noveltyluretargetluretargetluretarget
0s001mixedneg22632320.0625000.812500
1s001mixedneu32832320.0937500.875000
2s001mixedpos12632320.0312500.812500
3s001pureneg43748480.0833330.770833
4s001pureneu24048480.0416670.833333
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" + ], + "text/plain": [ + " subj cond valence sum count mean \n", + "novelty lure target lure target lure target\n", + "0 s001 mixed neg 2 26 32 32 0.062500 0.812500\n", + "1 s001 mixed neu 3 28 32 32 0.093750 0.875000\n", + "2 s001 mixed pos 1 26 32 32 0.031250 0.812500\n", + "3 s001 pure neg 4 37 48 48 0.083333 0.770833\n", + "4 s001 pure neu 2 40 48 48 0.041667 0.833333" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# use the agg method to get the counts\n", + "iperf = df_w.groupby(['subj', 'cond', 'valence', 'novelty'])['old_resp'].agg(['sum', 'count', 'mean'])\n", + "iperf = iperf.unstack().reset_index()\n", + "iperf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjcondvalencesum_luresum_targetcount_lurecount_targetmean_luremean_target
0s001mixedneg22632320.0625000.812500
1s001mixedneu32832320.0937500.875000
2s001mixedpos12632320.0312500.812500
3s001pureneg43748480.0833330.770833
4s001pureneu24048480.0416670.833333
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" + ], + "text/plain": [ + " subj cond valence sum_lure sum_target count_lure count_target \\\n", + "0 s001 mixed neg 2 26 32 32 \n", + "1 s001 mixed neu 3 28 32 32 \n", + "2 s001 mixed pos 1 26 32 32 \n", + "3 s001 pure neg 4 37 48 48 \n", + "4 s001 pure neu 2 40 48 48 \n", + "\n", + " mean_lure mean_target \n", + "0 0.062500 0.812500 \n", + "1 0.093750 0.875000 \n", + "2 0.031250 0.812500 \n", + "3 0.083333 0.770833 \n", + "4 0.041667 0.833333 " + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# collapse the multi-index\n", + "\n", + "\n", + "iperf.columns = ['_'.join(col).strip() if len(col[1]) > 0 else col[0] \n", + " for col in iperf.columns.values]\n", + "iperf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjcondvalencesum_luresum_targetcount_lurecount_targetmean_luremean_targetdprime
0s001mixedneg22632320.0625000.8125002.286695
1s001mixedneu32832320.0937500.8750002.344557
2s001mixedpos12632320.0312500.8125002.543117
3s001pureneg43748480.0833330.7708332.053005
4s001pureneu24048480.0416670.8333332.575583
.................................
133s023mixedneu32232320.0937500.6875001.720543
134s023mixedpos82832320.2500000.8750001.747641
135s023pureneg193048480.3958330.6250000.570552
136s023pureneu153848480.3125000.7916671.269635
137s023purepos53448480.1041670.7083331.750852
\n", + "

138 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " subj cond valence sum_lure sum_target count_lure count_target \\\n", + "0 s001 mixed neg 2 26 32 32 \n", + "1 s001 mixed neu 3 28 32 32 \n", + "2 s001 mixed pos 1 26 32 32 \n", + "3 s001 pure neg 4 37 48 48 \n", + "4 s001 pure neu 2 40 48 48 \n", + ".. ... ... ... ... ... ... ... \n", + "133 s023 mixed neu 3 22 32 32 \n", + "134 s023 mixed pos 8 28 32 32 \n", + "135 s023 pure neg 19 30 48 48 \n", + "136 s023 pure neu 15 38 48 48 \n", + "137 s023 pure pos 5 34 48 48 \n", + "\n", + " mean_lure mean_target dprime \n", + "0 0.062500 0.812500 2.286695 \n", + "1 0.093750 0.875000 2.344557 \n", + "2 0.031250 0.812500 2.543117 \n", + "3 0.083333 0.770833 2.053005 \n", + "4 0.041667 0.833333 2.575583 \n", + ".. ... ... ... \n", + "133 0.093750 0.687500 1.720543 \n", + "134 0.250000 0.875000 1.747641 \n", + "135 0.395833 0.625000 0.570552 \n", + "136 0.312500 0.791667 1.269635 \n", + "137 0.104167 0.708333 1.750852 \n", + "\n", + "[138 rows x 10 columns]" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# use apply to add the dprime as a new column (axis=1 tells it to go by row)\n", + "\n", + "# add the dprime as a new column (axis=1 tells it to go by row)\n", + "iperf['dprime'] = iperf.apply(lambda x: calc_dprime(x['sum_target'], x['count_target'],\n", + " x['sum_lure'], x['count_lure']),\n", + " axis=1)\n", + "\n", + "iperf" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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condvalencemeanstdsemcilen
0mixedneg2.1371700.4303400.0897320.18609323.0
1mixedneu2.1612650.4314110.0899550.18655623.0
2mixedpos1.9328440.4437660.0925320.19189923.0
3pureneg2.0680160.4731880.0986670.20462223.0
4pureneu2.1735170.6215850.1296090.26879323.0
5purepos2.0852440.6336260.1321200.27400023.0
\n", + "
" + ], + "text/plain": [ + " cond valence mean std sem ci len\n", + "0 mixed neg 2.137170 0.430340 0.089732 0.186093 23.0\n", + "1 mixed neu 2.161265 0.431411 0.089955 0.186556 23.0\n", + "2 mixed pos 1.932844 0.443766 0.092532 0.191899 23.0\n", + "3 pure neg 2.068016 0.473188 0.098667 0.204622 23.0\n", + "4 pure neu 2.173517 0.621585 0.129609 0.268793 23.0\n", + "5 pure pos 2.085244 0.633626 0.132120 0.274000 23.0" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# use ci_within to calcuate the mean and confidence interval of d-prime\n", + "\n", + "\n", + "res = ci_within(iperf, indexvar='subj', \n", + " withinvars=['cond', 'valence'], \n", + " measvar='dprime').reset_index()\n", + "res" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# use plotnine to plot dprime as a function of condition, with a fill-color defined by valence\n", + "# be sure to label your axes correctly and add the confidence interval with error bars\n", + "\n", + "p = (pn.ggplot(res, pn.aes('cond', 'mean', fill='valence'))\n", + " + pn.geom_errorbar(pn.aes(ymin='mean-ci', ymax='mean+ci', width=0.2), \n", + " position=pn.position_dodge(.9))\n", + " + pn.geom_point(position=pn.position_dodge(.9), size=4)\n", + " + pn.labs(x=\"Condition\", y = \"d'\", fill='Valence')\n", + " )\n", + "p" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assignments/A09_Final_Project-jhr3kd-Copy1.ipynb b/assignments/A09_Final_Project-jhr3kd-Copy1.ipynb new file mode 100644 index 0000000..67dcab8 --- /dev/null +++ b/assignments/A09_Final_Project-jhr3kd-Copy1.ipynb @@ -0,0 +1,1224 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Assignment 9: Final Project\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Objectives\n", + "\n", + "Upon completion of this assignment, students will have:\n", + "\n", + "1. Described the list generation process in detail\n", + "2. Described the experiment details\n", + "3. Visualized processed data\n", + "4. Performed a statistical analysis to test the hypothesis" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Assignment\n", + "\n", + "Write text (in MarkDown cells) and code (in Code cells) in a Jupyter notebook (after making a copy and renaming it to have your userid in the title --- e.g., A09_Final_Project_mst3k).\n", + "\n", + "\n", + "## Details\n", + "\n", + "The goal of the final project is to synthesize material covered in the class and produce part of what would go into an actual scientific publication based on *one* of the experiments we ran in the class. Specifically, you will be writing part of the Methods and Results sections.\n", + "\n", + "The basic template is below the code for loading and processing the data. There we outline what each section should include. As always, make sure to label all figures and be sure to refer to the code in the lesson notebooks as a guide for your analyses.\n", + "\n", + "Please feel free to reach out to us on Slack if you have any questions along the way.\n", + "\n", + "* ***When you are done, save this notebook as HTML (`File -> Download as -> HTML`) and upload it to the matching assignment on UVACollab.*** " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## General Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "# import some useful libraries\n", + "import numpy as np # numerical analysis linear algebra\n", + "import pandas as pd # efficient tables\n", + "import matplotlib.pyplot as plt # plotting\n", + "import plotnine as pn \n", + "import scipy.stats.distributions as dists # probability distributions\n", + "from scipy import stats\n", + "from glob import glob\n", + "import os\n", + "\n", + "from smile.log import log2dl\n", + "\n", + "from ci_within import ci_within\n", + "\n", + "import statsmodels.formula.api as smf\n", + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Custom SLOG loading function" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "# custom function to load slogs\n", + "def load_all_subj_logs(task_dir, log_file):\n", + " # load in a list of all the subj\n", + " subjs = [os.path.split(subj_dir)[-1] \n", + " for subj_dir in glob(os.path.join(task_dir, 's*'))]\n", + " subjs.sort()\n", + "\n", + " # loop over subj and their data\n", + " all_dat = []\n", + " for subj in subjs:\n", + " # set the file\n", + " log_path = os.path.join(task_dir, subj, log_file)\n", + " #print(log_path)\n", + "\n", + " # load the data\n", + " all_dat.extend(log2dl(log_path, subj=subj))\n", + "\n", + " df = pd.DataFrame(all_dat)\n", + " \n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Load in all the data" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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resp_map_lureresp_map_targetblock_numtrial_numstim_on_timestim_on_errorrespresp_time_timeresp_time_errorrt...valence_sdarousal_meanarousal_sddominance_meandominance_sdword_frequencynoveltycondsubjlog_num
0FJ00234.3955110.0J235.2848330.0001800.889323...1.57000000000000015.30999999999999962.235.462.04999999999999983targetneus0010
1FJ01235.8856540.0F237.0346700.0001821.149016...1.54.12000000000000011.83000000000000015.66000000000000011.7812lureneus0010
2FJ02237.6168690.0F238.7674060.0002381.150537...1.82000000000000015.45000000000000022.14999999999999994.63999999999999972.069999999999999816lureneus0010
3FJ03239.6249330.0F240.4322950.0001820.807362...1.243.95000000000000022.58000000000000015.37000000000000011.639999999999999919lureneus0010
4FJ04241.4322090.0F242.5452270.0001921.113017...2.16000000000000013.68000000000000022.56999999999999985.83000000000000011.549lureneus0010
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5 rows × 26 columns

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" + ], + "text/plain": [ + " resp_map_lure resp_map_target block_num trial_num stim_on_time \\\n", + "0 F J 0 0 234.395511 \n", + "1 F J 0 1 235.885654 \n", + "2 F J 0 2 237.616869 \n", + "3 F J 0 3 239.624933 \n", + "4 F J 0 4 241.432209 \n", + "\n", + " stim_on_error resp resp_time_time resp_time_error rt ... \\\n", + "0 0.0 J 235.284833 0.000180 0.889323 ... \n", + "1 0.0 F 237.034670 0.000182 1.149016 ... \n", + "2 0.0 F 238.767406 0.000238 1.150537 ... \n", + "3 0.0 F 240.432295 0.000182 0.807362 ... \n", + "4 0.0 F 242.545227 0.000192 1.113017 ... \n", + "\n", + " valence_sd arousal_mean arousal_sd \\\n", + "0 1.5700000000000001 5.3099999999999996 2.23 \n", + "1 1.5 4.1200000000000001 1.8300000000000001 \n", + "2 1.8200000000000001 5.4500000000000002 2.1499999999999999 \n", + "3 1.24 3.9500000000000002 2.5800000000000001 \n", + "4 2.1600000000000001 3.6800000000000002 2.5699999999999998 \n", + "\n", + " dominance_mean dominance_sd word_frequency novelty cond subj \\\n", + "0 5.46 2.0499999999999998 3 target neu s001 \n", + "1 5.6600000000000001 1.78 12 lure neu s001 \n", + "2 4.6399999999999997 2.0699999999999998 16 lure neu s001 \n", + "3 5.3700000000000001 1.6399999999999999 19 lure neu s001 \n", + "4 5.8300000000000001 1.5 49 lure neu s001 \n", + "\n", + " log_num \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "\n", + "[5 rows x 26 columns]" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data from the word recog task\n", + "task_dir = os.path.join('..', 'lessons', 'data2', 'Taskapalooza')\n", + "\n", + "df_f = load_all_subj_logs(task_dir, 'log_flanker')\n", + "df_i = load_all_subj_logs(task_dir, 'log_image_test')\n", + "df_w = load_all_subj_logs(task_dir, 'log_word_test')\n", + "df_w.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Some data clean-up" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "# it turns out the cond is easier to visualize as pure and mixed\n", + "def fix_conds(df, type_col):\n", + " # loop over the unique subjects\n", + " usubj = df.subj.unique()\n", + " for s in usubj:\n", + " # loop over their blocks\n", + " ublocks = df.loc[df['subj']==s, 'block_num'].unique()\n", + " for b in ublocks:\n", + " # grab the data for that subj and block\n", + " dfb = df.loc[(df['subj']==s)&(df['block_num']==b)]\n", + " \n", + " # get the unique types in that block\n", + " uval = dfb[type_col].unique()\n", + " if len(uval) > 1:\n", + " # it's mixed\n", + " df.loc[(df['subj']==s)&(df.block_num==b), 'cond'] = 'mixed'\n", + " else:\n", + " # it's the pure\n", + " df.loc[(df['subj']==s)&(df.block_num==b), 'cond'] = 'pure'\n", + "\n", + "# fix the conds in the recog experiments (updated in place)\n", + "fix_conds(df_i, type_col='in_out')\n", + "fix_conds(df_w, type_col='valence')\n", + "\n", + "# add in log_rt columns\n", + "df_f['log_rt'] = np.log(df_f['rt'])\n", + "df_i['log_rt'] = np.log(df_i['rt'])\n", + "df_w['log_rt'] = np.log(df_w['rt'])\n", + "\n", + "# must make correct an int\n", + "df_f['correct'] = df_f['correct'].astype(np.int)\n", + "df_i['correct'] = df_i['correct'].astype(np.int)\n", + "df_w['correct'] = df_w['correct'].astype(np.int)\n", + "\n", + "# add in a column for whether they made an 'old' response\n", + "df_i['old_resp'] = (df_i['resp_map_target'] == df_i['resp']).astype(np.int)\n", + "df_w['old_resp'] = (df_w['resp_map_target'] == df_w['resp']).astype(np.int)\n", + "\n", + "# process some of the valence info\n", + "df_w['valence_mean'] = df_w['valence_mean'].astype(np.float)\n", + "df_w['arousal_mean'] = df_w['arousal_mean'].astype(np.float)\n", + "df_w['dominance_mean'] = df_w['dominance_mean'].astype(np.float)\n", + "df_w['abs_valence'] = np.abs(df_w['valence_mean'] - 5.0)\n", + "df_w['abs_arousal'] = np.abs(df_w['arousal_mean'] - 5.0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Your text and code goes below here\n", + "\n", + "*All code above should work without modification.*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Hypothesis\n", + "\n", + "There is an effect of valence (potentially interacting with condition) and correctness on response times" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Methods\n", + "\n", + "\n", + "\n", + "\n", + "## List generation\n", + "\n", + "### Objective\n", + "Read in a pool of stimuli and create lists of dictionaries that can be presented to participants as part of the SMILE experiment below.\n", + "\n", + "The stimuli are contained in three seperate CSV files:\n", + "- [Positive Pool](./pos_pool.csv)\n", + "- [Negative Pool](./neg_pool.csv)\n", + "- [Neutral Pool](./neu_pool.csv)\n", + "\n", + "\n", + "Read these files in as lists of dictionaries (using `DictReader` imported from the `csv` module). \n", + "\n", + "Using these pools, create study lists of trials for two experimental conditions: **pure** or **mixed**. \n", + "- **pure**\n", + " - all trials should have words of the same valence\n", + " - should have the same number of positive, negative, and neutral pure lists\n", + "- **mixed**\n", + " - each list should contain an equal number of positive, negative, and neutral words in random order\n", + "\n", + "Each trial (or study list item) is represented as a **dictionary** containing all the information necessary to identify the stimulus to be presented, details about that stimulus, and the condition in which to present it. This information will be experiment-specific, as outlined below.\n", + "\n", + "Every study list should have a matching test list that includes all the study list items, plus a set of lures that match the valence of the studied words.\n", + "\n", + "Each block should contain a **pure** study list and test list pair (stored in a dict) for **each** valence (pos, neg, neu), as well as one **mixed** study list and test list pair (stored as a dict) for a total of **four** dicts. \n", + "\n", + "\n", + "\n", + "The final output called `blocks` should be a shuffled list of the dicts contained within each of it's blocks. \n", + "\n", + "***IMPORTANT: `blocks` should end up as a list of dicts of lists of dicts***\n", + "\n", + "Finally, the script should be configurable such that you can specify different numbers of lists and trials, along with other details specific to the experiment you decide to do.\n", + "\n", + "\n", + "\n", + "\n", + "### Requirements\n", + "- Important packages to `import`\n", + " - `import random`\n", + " - `from csv import DictReader`\n", + " - `import copy`\n", + "- Have the external `csv` files downloaded on the same directory \n", + " - [Positive Pool](./pos_pool.csv)\n", + " - [Negative Pool](./neg_pool.csv)\n", + " - [Neutral Pool](./neu_pool.csv)\n", + " \n", + "### Procedure\n", + "\n", + "1. Start by initiializing the configuration variables\n", + " - `pos_file` = 'pos_pool.csv'\n", + " - `neg_file` = 'neg_pool.csv'\n", + " - `neu_file` = 'neu_pool.csv'\n", + " - `num_pools` = 3 \n", + " - `num_items_pure` = number of items in pure lists (must be evenly divisible by num_pools)\n", + " - `num_reps` = number of repetitions of each block type\n", + " - Verify the following (make sure number of mixed items is valid): \n", + " - `num_items_mixed = int(num_items_pure / num_pools)`\n", + " - `assert num_items_mixed * num_pools == num_items_pure`\n", + "2. Load in the pools of data \n", + "3. Shuffle the pools \n", + "4. Define the `gen_block` function that creates a study/test block from the pools that are passed in:\n", + " - should take in `pools`, `cond`, and `num_items` as input\n", + " - should initialize an empty `study_list`\n", + " - should loop through the pools (being careful not to add duplicates) and look for `num_items` study items and add them to the study list; should update the study item's novelty and cond before adding to the `study_list`\n", + " - should shuffle the current state of the `study_list` after adding the elements\n", + " - should set `test_list` as a deep copy of `study_list`\n", + " - should loop over the pools again(being careful not to add duplicates) and look for `num_items` more test items and add them to the test list; should update the test item's novelty and cond before adding to the `test_list`\n", + " - should shuffle the current state of the `test_list` after adding the elements\n", + " - should return a dict containing `study_list` and `test_list`\n", + "5. Generate the blocks \n", + " - create an empty list `blocks`\n", + " - loop through the `num_reps` and for each iteration:\n", + " - create a positive pure block using the `gen_block` function and using the appropriate parameters\n", + " - create a negative pure block using the `gen_block` function and using the appropriate parameters\n", + " - create a positive neutral block using the `gen_block` function and using the appropriate parameters\n", + " - create a mixed pos/neg/neu block using the `gen_block` function and using the appropriate parameters (be careful of the parameters here)\n", + "6. Shuffle `blocks` and return \n", + " \n", + "\n", + "\n", + "\n", + "## SMILE Experiment Details\n", + "\n", + "### Objective\n", + "Utilize the lists generated by code from the list generation method above to create an experiment for collecting data. For this Recognition Memory Test, participants will study a list of items one at a time, and then, after a short delay, be tested for their memory of those items. \n", + "\n", + "The main objectives of this experiment are the following:\n", + "- Present an instruction screen to the participant that explains the tasks\n", + "- Loop over the blocks of study--test lists. Each block has the following structure:\n", + " - Wait for the participant to press a key to start the block\n", + " - Loop over the study list presenting all the study items, one at a time; Each study item is presented like so:\n", + " - Present the item for a specified duration (this should be a configuration variable at the top of your code)\n", + " - Wait an inter-stimulus duration plus some amount of jitter (these, too, should be config variables)\n", + " - Log the stimulus information, including when it appeared on the screen\n", + " - Wait for a delay \n", + " - Loop over the test list presenting all the test items, one at a time; Each test item is presented like so:\n", + " - Present the item on the screen (with its Label) **until the participant makes a keyboard response of either the key you have selected to indicate the item is \"old\" or the key that indicates the item is \"new\"**\n", + " - Log the stimulus information, including when the stimulus appeared on the screen, when the participant made their response, and what response they made\n", + "- Optional: Present an exit screen to the participant with a fun message!\n", + "\n", + "In the test phase of each block, participants will see the study items again, along with an equal number of new items, and for each item they must specify whether the item is an old target item (i.e., one that was on the study list) or a new lure item.\n", + "\n", + "\n", + "\n", + "### Requirements\n", + "- Important packages to `import`\n", + " - `from smile.common import * `\n", + " - `from smile.scale import scale as s`\n", + " - `from smile.startup import InputSubject`\n", + "\n", + "\n", + "### Procedure\n", + "1. Start by initializing the configuration variables (including the listgen variables)\n", + " - Important variables include (replace empty or 0 variables with your desired configuration): \n", + " - `font_size = 0`\n", + " - `resp_keys = ['', '']` ***these are your keys to indicate if the item is \"old\" or if the item is \"new\"***\n", + " - `resp_map = {'': '', '': ''}`***this is used to associate the two keys chosen for `resp_keys` with \"old\" or \"new\"***\n", + " - `ISI_dur = 0`\n", + " - `ISI_jitter = 0` \n", + " - `LOC_X_jitter = 0`\n", + " - `LOC_Y_jitter = 0`\n", + " - `inst_font_size = 0 `\n", + " - `stim_time = 0`\n", + " - `inst_text = \"\"`\n", + " - `study_text = \"\"`\n", + " - `test_text = \"\"`\n", + " - `blocks` ***this should be created using the list_gen method above***\n", + "2. Create your subroutines for `Instruct`, `Trial` and `Study`\n", + " - `Instruct` \n", + " - should use `inst_text` to render the instructions on the screen\n", + " - should wait for user input to continue\n", + " - `Trial` (This is an individual test item within the test list)\n", + " - should take in a `block_num`, `trial_num` and `cur_trial`\n", + " - should present the stimulus on the screen \n", + " - should wait for a response using the selected `resp_keys` from the participant (no timeout) \n", + " - should collect and log relevant data from the participant's response\n", + " - `Study` (This is an individual study item within the test list)\n", + " - should take in a `block_num`, `trial_num` and `cur_trial`\n", + " - should present the stimulus on the screen\n", + " - should wait the `ISI_dur` with `ISI_jitter`\n", + " - should log relevant data based on the stimulus\n", + "3. Run Subroutines \n", + " 1. Take subject id information using `InputSubject`\n", + " 2. Use `Instruct` to present the instructions to the participant\n", + " 3. `Wait` ***could use a configured amount or input 0.5 as default***\n", + " 4. Loop over `blocks`; For each block:\n", + " 1. Take user input to make sure they are ready to start the block\n", + " 2. Present them with the `study_text` and wait for user input to make sure they are ready to begin the study portion of the block\n", + " 3. Loop over the current block's study list using the `Study` subroutine\n", + " 4. Wait the `ISI_dur` with `ISI_jitter`\n", + " 5. Loop over the current block's study list using the `Trial` subroutine\n", + " 4. Optional: Present an exit screen to the participant with a fun message!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Results\n", + "\n", + "## Primary Question: \n", + "Is there an effect of valence (potentially interacting with condition) and correctness on response times?\n", + "\n", + "## Motivations \n", + "Before running this analysis, I first want to introduce the motivations for this hypothesis. Thinking about the nature of the recognition memory test, there is some reason to believe that people process different words at diifferent rates. Particularly, positive, negative and neutral words may be percieved diffently based on one's personal experiences and could subsequently be processed differently. Additionally, there is reason to believe that incorrect responses had longer response times bcause the individual was potentially unsure of their response. \n", + "\n", + "Though causality can never be guaranteed in either of these cases, this analysis looks to see if there is some tangible effects in the data. From there, further analyses can be run to find more conclsive findings. \n", + "\n", + "## Important Variables\n", + "Here the dependent variable will be `log_rt` instead of `rt` because ________. The independent variables will be `valence` and `correct`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Data processing and visualization\n" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# creating the right dataframe to plot and visualize the problem stated above\n", + "# get the error corrected by condition and whether they answered correctly\n", + "res = ci_within(df_w, \n", + " indexvar='subj', # column that identifies a subject\n", + " withinvars=['cond', 'correct', 'valence'], # list of columns for grouping within subject\n", + " measvar='log_rt') # dependent variable averaging over\n", + "\n", + "res = res.reset_index()\n", + "\n", + "p = (pn.ggplot(res, pn.aes('cond', 'mean', fill='valence'))\n", + " + pn.geom_errorbar(pn.aes(ymin='mean-ci', ymax='mean+ci', width=0.2), \n", + " position=pn.position_dodge(.7))\n", + " + pn.geom_point(position=pn.position_dodge(.7), size=4)\n", + " + pn.facet_wrap('~ correct')\n", + " + pn.labs(title=\"Plot of log_rt by valence across correctness\", x=\"Condition\", y = \"P(log_rt)\", fill='Valence')\n", + " )\n", + "p" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "In the figure above, we can see a plot of `log_rt` as a function of valence. The plot is split into correct and incorrect responses (labeled by the `1` and `0` at the top of each plot respectively). Furthermore, to account for differences in condition, two sets of plots (`mixed` and `pure`) were included for each valence and corectness. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Statistical test and interpretation" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjcondvalencecorrectlog_rt
0s001mixedneg00.167138
1s001mixedneg1-0.217212
2s001mixedneu0-0.109757
3s001mixedneu1-0.215241
4s001mixedpos00.166381
..................
271s023pureneg1-0.284881
272s023pureneu0-0.249797
273s023pureneu1-0.347838
274s023purepos0-0.357336
275s023purepos1-0.447838
\n", + "

276 rows × 5 columns

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" + ], + "text/plain": [ + " subj cond valence correct log_rt\n", + "0 s001 mixed neg 0 0.167138\n", + "1 s001 mixed neg 1 -0.217212\n", + "2 s001 mixed neu 0 -0.109757\n", + "3 s001 mixed neu 1 -0.215241\n", + "4 s001 mixed pos 0 0.166381\n", + ".. ... ... ... ... ...\n", + "271 s023 pure neg 1 -0.284881\n", + "272 s023 pure neu 0 -0.249797\n", + "273 s023 pure neu 1 -0.347838\n", + "274 s023 pure pos 0 -0.357336\n", + "275 s023 pure pos 1 -0.447838\n", + "\n", + "[276 rows x 5 columns]" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Code for statistical test using statsmodel\n", + "\n", + "# use the agg method to get the means\n", + "perf = df_w.groupby(['subj', 'cond', 'valence', 'correct'])['log_rt'].mean()\n", + "perf = perf.reset_index()\n", + "perf" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: log_rt R-squared: 0.144
Model: OLS Adj. R-squared: 0.109
Method: Least Squares F-statistic: 4.053
Date: Wed, 02 Dec 2020 Prob (F-statistic): 1.70e-05
Time: 22:59:04 Log-Likelihood: -52.458
No. Observations: 276 AIC: 128.9
Df Residuals: 264 BIC: 172.4
Df Model: 11
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept 0.0358 0.062 0.574 0.566 -0.087 0.159
cond[T.pure] -0.0377 0.088 -0.427 0.670 -0.211 0.136
valence[T.neu] -0.0979 0.088 -1.110 0.268 -0.272 0.076
valence[T.pos] 0.0480 0.088 0.544 0.587 -0.126 0.222
cond[T.pure]:valence[T.neu] 0.1971 0.125 1.580 0.115 -0.049 0.443
cond[T.pure]:valence[T.pos] -0.0671 0.125 -0.538 0.591 -0.313 0.179
correct -0.2348 0.088 -2.661 0.008 -0.409 -0.061
cond[T.pure]:correct 0.0233 0.125 0.186 0.852 -0.222 0.269
valence[T.neu]:correct 0.0741 0.125 0.594 0.553 -0.172 0.320
valence[T.pos]:correct -0.0286 0.125 -0.229 0.819 -0.274 0.217
cond[T.pure]:valence[T.neu]:correct -0.1750 0.176 -0.992 0.322 -0.522 0.172
cond[T.pure]:valence[T.pos]:correct 0.0680 0.176 0.385 0.700 -0.279 0.415
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Omnibus: 120.457 Durbin-Watson: 0.866
Prob(Omnibus): 0.000 Jarque-Bera (JB): 535.822
Skew: 1.794 Prob(JB): 4.44e-117
Kurtosis: 8.807 Cond. No. 25.6


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: log_rt R-squared: 0.144\n", + "Model: OLS Adj. R-squared: 0.109\n", + "Method: Least Squares F-statistic: 4.053\n", + "Date: Wed, 02 Dec 2020 Prob (F-statistic): 1.70e-05\n", + "Time: 22:59:04 Log-Likelihood: -52.458\n", + "No. Observations: 276 AIC: 128.9\n", + "Df Residuals: 264 BIC: 172.4\n", + "Df Model: 11 \n", + "Covariance Type: nonrobust \n", + "=======================================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "-------------------------------------------------------------------------------------------------------\n", + "Intercept 0.0358 0.062 0.574 0.566 -0.087 0.159\n", + "cond[T.pure] -0.0377 0.088 -0.427 0.670 -0.211 0.136\n", + "valence[T.neu] -0.0979 0.088 -1.110 0.268 -0.272 0.076\n", + "valence[T.pos] 0.0480 0.088 0.544 0.587 -0.126 0.222\n", + "cond[T.pure]:valence[T.neu] 0.1971 0.125 1.580 0.115 -0.049 0.443\n", + "cond[T.pure]:valence[T.pos] -0.0671 0.125 -0.538 0.591 -0.313 0.179\n", + "correct -0.2348 0.088 -2.661 0.008 -0.409 -0.061\n", + "cond[T.pure]:correct 0.0233 0.125 0.186 0.852 -0.222 0.269\n", + "valence[T.neu]:correct 0.0741 0.125 0.594 0.553 -0.172 0.320\n", + "valence[T.pos]:correct -0.0286 0.125 -0.229 0.819 -0.274 0.217\n", + "cond[T.pure]:valence[T.neu]:correct -0.1750 0.176 -0.992 0.322 -0.522 0.172\n", + "cond[T.pure]:valence[T.pos]:correct 0.0680 0.176 0.385 0.700 -0.279 0.415\n", + "==============================================================================\n", + "Omnibus: 120.457 Durbin-Watson: 0.866\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 535.822\n", + "Skew: 1.794 Prob(JB): 4.44e-117\n", + "Kurtosis: 8.807 Cond. No. 25.6\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# build a linear regression of the full model\n", + "m0 = smf.ols(\"log_rt ~ cond * valence * correct\", perf).fit()\n", + "m0.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Running an ANOVA on the linear model results\n", + "\n", + "- The results of such a complicated linear model is hard to unpack\n", + "- The most common approach to modeling the data would be a repeated measures ANOVA\n", + "- Luckily, a linear regress is really just an ANOVA if you make the right comparisons\n", + "- To simplify the results of the regression, we will run an annova below" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sum_sqdfFPR(>F)
cond0.0000181.00.0002029.886632e-01
valence0.0265832.00.1484748.620946e-01
cond:valence0.2566832.01.4336682.402830e-01
correct3.5196081.039.3165591.463755e-09
cond:correct0.0026581.00.0296888.633310e-01
valence:correct0.0043182.00.0241199.761714e-01
cond:valence:correct0.1807612.01.0096133.657622e-01
Residual23.633208264.0NaNNaN
\n", + "
" + ], + "text/plain": [ + " sum_sq df F PR(>F)\n", + "cond 0.000018 1.0 0.000202 9.886632e-01\n", + "valence 0.026583 2.0 0.148474 8.620946e-01\n", + "cond:valence 0.256683 2.0 1.433668 2.402830e-01\n", + "correct 3.519608 1.0 39.316559 1.463755e-09\n", + "cond:correct 0.002658 1.0 0.029688 8.633310e-01\n", + "valence:correct 0.004318 2.0 0.024119 9.761714e-01\n", + "cond:valence:correct 0.180761 2.0 1.009613 3.657622e-01\n", + "Residual 23.633208 264.0 NaN NaN" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run a type-II repeated measures ANOVA based on the linear model results\n", + "sm.stats.anova_lm(m0, typ=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Findings\n", + "\n", + "Looking at the results of the statistical analysis we can see that there is a big effect of corectness on response times. However there do not seem to be any other significant effects with valence and condition. This result seems fitting given the plots presented at the beginning of the analysis: big discrepancies in `log_rt` are obvious in incorrect vs correct plots. This is a very promising finding from our analysis, and with further study, more robust causal effects of correctness on response times may be found. \n", + "\n", + "Additionaly, it is worth noting that though there were no significant findings of valence effects on response times, in the plots above, there seemed to be some effect of positive and negative valence vs neutral valence on response times. That is to say that in the plots, positive and negative valences seemed to have slower response times when compared to neutral valences. Further analysis using a linear mixed effects regression will be needed to utilize `abs_valence` to see if this is a valid and significant effect. \n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Discussion\n", + "\n", + "***Graduate students only!!!***\n", + "\n", + "*In one to two paragraphs do the following: a) Place the study in the larger literature, summarizing some of the similar work in the field and how this study compares, b) write some analysis of the findings from the study (even if they are null results) and then describe a follow-up study with a new variant of the experiment that you think might help answer further questions on the topic." + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assignments/A09_Final_Project-jhr3kd.ipynb b/assignments/A09_Final_Project-jhr3kd.ipynb new file mode 100644 index 0000000..38460ed --- /dev/null +++ b/assignments/A09_Final_Project-jhr3kd.ipynb @@ -0,0 +1,1141 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Assignment 9: Final Project\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Objectives\n", + "\n", + "Upon completion of this assignment, students will have:\n", + "\n", + "1. Described the list generation process in detail\n", + "2. Described the experiment details\n", + "3. Visualized processed data\n", + "4. Performed a statistical analysis to test the hypothesis" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Assignment\n", + "\n", + "Write text (in MarkDown cells) and code (in Code cells) in a Jupyter notebook (after making a copy and renaming it to have your userid in the title --- e.g., A09_Final_Project_mst3k).\n", + "\n", + "\n", + "## Details\n", + "\n", + "The goal of the final project is to synthesize material covered in the class and produce part of what would go into an actual scientific publication based on *one* of the experiments we ran in the class. Specifically, you will be writing part of the Methods and Results sections.\n", + "\n", + "The basic template is below the code for loading and processing the data. There we outline what each section should include. As always, make sure to label all figures and be sure to refer to the code in the lesson notebooks as a guide for your analyses.\n", + "\n", + "Please feel free to reach out to us on Slack if you have any questions along the way.\n", + "\n", + "* ***When you are done, save this notebook as HTML (`File -> Download as -> HTML`) and upload it to the matching assignment on UVACollab.*** " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## General Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "# import some useful libraries\n", + "import numpy as np # numerical analysis linear algebra\n", + "import pandas as pd # efficient tables\n", + "import matplotlib.pyplot as plt # plotting\n", + "import plotnine as pn \n", + "import scipy.stats.distributions as dists # probability distributions\n", + "from scipy import stats\n", + "from glob import glob\n", + "import os\n", + "\n", + "from smile.log import log2dl\n", + "\n", + "from ci_within import ci_within\n", + "\n", + "import statsmodels.formula.api as smf\n", + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Custom SLOG loading function" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "# custom function to load slogs\n", + "def load_all_subj_logs(task_dir, log_file):\n", + " # load in a list of all the subj\n", + " subjs = [os.path.split(subj_dir)[-1] \n", + " for subj_dir in glob(os.path.join(task_dir, 's*'))]\n", + " subjs.sort()\n", + "\n", + " # loop over subj and their data\n", + " all_dat = []\n", + " for subj in subjs:\n", + " # set the file\n", + " log_path = os.path.join(task_dir, subj, log_file)\n", + " #print(log_path)\n", + "\n", + " # load the data\n", + " all_dat.extend(log2dl(log_path, subj=subj))\n", + "\n", + " df = pd.DataFrame(all_dat)\n", + " \n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Load in all the data" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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resp_map_lureresp_map_targetblock_numtrial_numstim_on_timestim_on_errorrespresp_time_timeresp_time_errorrt...valence_sdarousal_meanarousal_sddominance_meandominance_sdword_frequencynoveltycondsubjlog_num
0FJ00234.3955110.0J235.2848330.0001800.889323...1.57000000000000015.30999999999999962.235.462.04999999999999983targetneus0010
1FJ01235.8856540.0F237.0346700.0001821.149016...1.54.12000000000000011.83000000000000015.66000000000000011.7812lureneus0010
2FJ02237.6168690.0F238.7674060.0002381.150537...1.82000000000000015.45000000000000022.14999999999999994.63999999999999972.069999999999999816lureneus0010
3FJ03239.6249330.0F240.4322950.0001820.807362...1.243.95000000000000022.58000000000000015.37000000000000011.639999999999999919lureneus0010
4FJ04241.4322090.0F242.5452270.0001921.113017...2.16000000000000013.68000000000000022.56999999999999985.83000000000000011.549lureneus0010
\n", + "

5 rows × 26 columns

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" + ], + "text/plain": [ + " resp_map_lure resp_map_target block_num trial_num stim_on_time \\\n", + "0 F J 0 0 234.395511 \n", + "1 F J 0 1 235.885654 \n", + "2 F J 0 2 237.616869 \n", + "3 F J 0 3 239.624933 \n", + "4 F J 0 4 241.432209 \n", + "\n", + " stim_on_error resp resp_time_time resp_time_error rt ... \\\n", + "0 0.0 J 235.284833 0.000180 0.889323 ... \n", + "1 0.0 F 237.034670 0.000182 1.149016 ... \n", + "2 0.0 F 238.767406 0.000238 1.150537 ... \n", + "3 0.0 F 240.432295 0.000182 0.807362 ... \n", + "4 0.0 F 242.545227 0.000192 1.113017 ... \n", + "\n", + " valence_sd arousal_mean arousal_sd \\\n", + "0 1.5700000000000001 5.3099999999999996 2.23 \n", + "1 1.5 4.1200000000000001 1.8300000000000001 \n", + "2 1.8200000000000001 5.4500000000000002 2.1499999999999999 \n", + "3 1.24 3.9500000000000002 2.5800000000000001 \n", + "4 2.1600000000000001 3.6800000000000002 2.5699999999999998 \n", + "\n", + " dominance_mean dominance_sd word_frequency novelty cond subj \\\n", + "0 5.46 2.0499999999999998 3 target neu s001 \n", + "1 5.6600000000000001 1.78 12 lure neu s001 \n", + "2 4.6399999999999997 2.0699999999999998 16 lure neu s001 \n", + "3 5.3700000000000001 1.6399999999999999 19 lure neu s001 \n", + "4 5.8300000000000001 1.5 49 lure neu s001 \n", + "\n", + " log_num \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "\n", + "[5 rows x 26 columns]" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data from the word recog task\n", + "task_dir = os.path.join('..', 'lessons', 'data2', 'Taskapalooza')\n", + "\n", + "df_f = load_all_subj_logs(task_dir, 'log_flanker')\n", + "df_i = load_all_subj_logs(task_dir, 'log_image_test')\n", + "df_w = load_all_subj_logs(task_dir, 'log_word_test')\n", + "df_w.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Some data clean-up" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "# it turns out the cond is easier to visualize as pure and mixed\n", + "def fix_conds(df, type_col):\n", + " # loop over the unique subjects\n", + " usubj = df.subj.unique()\n", + " for s in usubj:\n", + " # loop over their blocks\n", + " ublocks = df.loc[df['subj']==s, 'block_num'].unique()\n", + " for b in ublocks:\n", + " # grab the data for that subj and block\n", + " dfb = df.loc[(df['subj']==s)&(df['block_num']==b)]\n", + " \n", + " # get the unique types in that block\n", + " uval = dfb[type_col].unique()\n", + " if len(uval) > 1:\n", + " # it's mixed\n", + " df.loc[(df['subj']==s)&(df.block_num==b), 'cond'] = 'mixed'\n", + " else:\n", + " # it's the pure\n", + " df.loc[(df['subj']==s)&(df.block_num==b), 'cond'] = 'pure'\n", + "\n", + "# fix the conds in the recog experiments (updated in place)\n", + "fix_conds(df_i, type_col='in_out')\n", + "fix_conds(df_w, type_col='valence')\n", + "\n", + "# add in log_rt columns\n", + "df_f['log_rt'] = np.log(df_f['rt'])\n", + "df_i['log_rt'] = np.log(df_i['rt'])\n", + "df_w['log_rt'] = np.log(df_w['rt'])\n", + "\n", + "# must make correct an int\n", + "df_f['correct'] = df_f['correct'].astype(np.int)\n", + "df_i['correct'] = df_i['correct'].astype(np.int)\n", + "df_w['correct'] = df_w['correct'].astype(np.int)\n", + "\n", + "# add in a column for whether they made an 'old' response\n", + "df_i['old_resp'] = (df_i['resp_map_target'] == df_i['resp']).astype(np.int)\n", + "df_w['old_resp'] = (df_w['resp_map_target'] == df_w['resp']).astype(np.int)\n", + "\n", + "# process some of the valence info\n", + "df_w['valence_mean'] = df_w['valence_mean'].astype(np.float)\n", + "df_w['arousal_mean'] = df_w['arousal_mean'].astype(np.float)\n", + "df_w['dominance_mean'] = df_w['dominance_mean'].astype(np.float)\n", + "df_w['abs_valence'] = np.abs(df_w['valence_mean'] - 5.0)\n", + "df_w['abs_arousal'] = np.abs(df_w['arousal_mean'] - 5.0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Hypothesis\n", + "\n", + "The hypothesis of this experiment is the following:\n", + "\n", + " **There is an effect of valence and correctness (potentially interacting with condition) on response times.**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Methods\n", + "\n", + "\n", + "\n", + "## List generation\n", + "\n", + "\n", + "### Objective\n", + "\n", + "The goal of the List Generation procedure is to create usable test and study lists that can be presented to participants in the Memory Recognition task below. The inputs of this procedure are pools of stimuli split up by valence and the output is a list of blocks containing study and test list pairs of stimuli. \n", + "\n", + "\n", + "### Requirements and Data\n", + "\n", + "This List Generation procedure uses imported python libraries such as `csv` to read in the pools of stimuli that are split up by valence. \n", + "\n", + "The primary pools of data used were contained in the following three CSV files:\n", + "- [Positive Pool](./pos_pool.csv)\n", + "- [Negative Pool](./neg_pool.csv)\n", + "- [Neutral Pool](./neu_pool.csv)\n", + "\n", + "The underlying Memory Recognition task is set up as a study-test design with two primary conditions: `pure` and `mixed`. The pure condition contains study and test lists of the same valence: either `positive`, `negative` or `neutral` valences. The mixed condition contains study and test lists with varying valences. \n", + "\n", + "Important requirements for the lists are as follows:\n", + "1. All study lists for both `pure` and `mixed` block conditions should have an equal number of words\n", + "2. All test lists for both `pure` and `mixed` block conditions should have an equal number of words\n", + "3. There should be an equal number of `positive`, `negative` or `neutral` valenced words in both the study and test lists of the mixed condition blocks. \n", + "4. There should be no repeated words across the blocks of the experiment\n", + "\n", + "\n", + "### Design \n", + "\n", + "To produce the appropriate data for this experiment, the List Generation procedure builds the experimental blocks from the bottom-up, starting with individual study and test words. Individual words were stored such that all the relevant information of the particular stimulus including its novelty, condition and valence were saved as well. For this particular procedure we utilized a python dictionary to accomplish this, but any similar data structure would do. \n", + "\n", + "\n", + "These dictionaries are then grouped into into study and test lists that are further grouped into study and test list pairs representing blocks. **Note: each block was a pair containing a study list and a test list**. Because the Memory Recognition task requires an equal number of positive pure, negative pure, neutral pure and mixed blocks, blocks are added to the `blocks` list in increments of 4 (1 for each block type). `blocks` was the finalized list and output for this procedure\n", + "\n", + "\n", + "\n", + "### Procedure \n", + "\n", + "Once the data pools are read in, the appropriate configuration variables are set up including the number of pools(or valences), number of items per pure list, and number of repetitions for each block type. \n", + "\n", + "Another important variable is the number of items per valence in the mixed list. This should be set equal to number of items per pure list divided by the number of pools. This is done to ensure that every type of block has eqiuivalent list sizes. It is thus crucial to make sure that our number of items per pure list is configured so that it is divisible by the number of pools. For this experiment, 3 pools were used (positive, negative and neutral), the number of items per pure list was 24 and the number of repeitions for each block type was 2. Because we have 4 types of blocks (positive pure, negative pure, neutral pure and mixed blocks) our experiment had 8 blocks total. \n", + "\n", + "Once the configuration variables were set up appropriately, the data pools for each valence were taken in as lists and a valence attribute was added to each element in the respective valence pools to signify whether they were positive, negative or neutral. The blocks were subsequently shuffled to maintain randomization. \n", + "\n", + "Then, using a dedicated block generation function and the number of repetitions per block type (2 in this case), the 8 blocks of the experiment were created. The block generation function took in a list of pools, a valence, and a number of items. For the pure lists, only one pool assosiated with the valence was passed in and the number of items passed in was the pre-configured number of items per pure list. For the mixed lists, all three pools were passed in and the number of items passed in was the pre-configured number of items per mixed list. \n", + "\n", + "Using the passed in parameters, the block generation function looped through the provided pools (note: mixed lists had 3x more iterations to accomodate for the smaller number of items passed in) and created **indiviual dictionaries for every study item**, using the data from the given pools and storing relevant information such as the item's condition, novelty and valence. These study word dictionaries were then added to a study list containing all the study words for the particular block. Once every study word was added, the list was shuffled to maintain randomization. \n", + "\n", + "The test list for a particular block was created by creating a copy of the study list, and looping through the pools of data again to add an equivalent number of new words (lures). The test list was shuffled at the end to maintain randomization. \n", + "\n", + "Finally, the block generation function returned a dictionary containing both the study and test lists. This is was repeated over the number of required blocks (8 blocks total with 2 of each block type), and the final output was shuffled to maintain inter-block randomization. \n", + "\n", + "\n", + "\n", + "## SMILE Experiment Details\n", + "\n", + "### Objective\n", + "\n", + "This Memory Recognition task utilizes the lists generated by code from the list generation procedure above to create an experiment for collecting the desired data. For this Recognition Memory task, participants will study a list of items one at a time, and then, after a short math distractor, wil be tested for their memory of those items. \n", + "\n", + "\n", + "### Requirements and Data\n", + "\n", + "The Recognition Memory task was created using an open-source, Python-based experient bulding library: State Machine Interface Library for Experiments (SMILE; https://github.com/compmem/smile). Subjects completed the task on their local computers that were capable of running the provided experiments. \n", + "\n", + "\n", + "All data imported for the study and test lists are taken from the \"List Generation\" procedure above. The required study and test lists are taken in as a variable `blocks` which represents the blocks of trials of this experiment. \n", + "\n", + "\n", + " \n", + "### Design \n", + "\n", + "The Recognition Memory task utilizes a study-test design with two primary conditions: pure and mixed. The pure condition contains study and test words of the same valence: either positive, negative or neutral valences. The mixed condition contains study and test words of varying valences. \n", + "\n", + "On each trial, subjects were presented with a list of study words and were tested on a matching list of test words that contained the study words (targets) and a set of new unstudied words (lures). In between the study and test phases, subjects were given a short (set time) math distractor in which they signified whether a presented math equation was valid or invalid by pressing the \"F\" key for valid equations and \"J\" key for invalid equations. In the testing phase, subjects were asked to make a decision on whether thay had studied the presented words by pressing either the \"F\" key for targets or the \"J\" key for lures. \n", + "\n", + "Subjects completed 8 blocks, consiting of 24 study words and 48 test words each. There were 4 primary block types and there were an equal number (2 each) of each block type: positive pure, negative pure, neutral pure and mixed. \n", + "\n", + "\n", + "### Procedure\n", + "\n", + "After inputing their subject id information, subjects were presented with a brief instruction screen for the experiment. The instructions contained information about the Memory Recognition task, introducing the study phase, testing phase and the 30 seconds of math problems in between them. After the subjects clicked through the instruction screen, they were taken to the start of the first block.\n", + "\n", + "At the start of a block, the subjects are taken to a pre-study-phase screen prompting the users to hit the \"ENTER\" key prior to beginning the study phase. Every study phase started with a pre-configured delay time including a jitter. Within the study phase itself, each item is presented for 1s and after each study item was presented, there was an inter stimulus delay (default was 0.5s) along with a randomized jitter (random value up to 0.5s)\n", + "\n", + "Following the study phase, the subjects were taken to an instruction screen for the math portion of the block, which included a prompt for the users to hit the \"ENTER\" key prior to beginning the math portion. \n", + "\n", + "Finally, after the math portion of the block, subjects were taken to an instruction screen for the testing phase of the block, including a prompt for the users to hit the \"ENTER\" key prior to beginning the testing phase. \n", + "\n", + "\n", + "During the testing phase, subjects were presented with a stimulus that was either a studied item (target) or a new item (lure). Using the designated response keys, \"F\" for targets, \"J\" for lures, subjects made decisions on whether they had studied the presented words. After each test item was presented and responded to, there was an inter stimulus delay (default was 0.5s) along with a randomized jitter (random value up to 0.5s)\n", + "\n", + "\n", + "Upon completion of the testing phase, the next block starts in the same way, and the cycle is repeated until all the blocks are finished. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Results\n", + "\n", + "## Primary Question: \n", + "Is there an effect of valence (potentially interacting with condition) and correctness on response times?\n", + "\n", + "## Motivations \n", + "Before running this analysis, I first want to introduce the motivations for this hypothesis. Thinking about the nature of the recognition memory test, there is some reason to believe that people process different words at different rates. Particularly, positive, negative and neutral words may be percieved diffently based on one's personal experiences and could subsequently be processed differently. Additionally, there is reason to believe that incorrect responses had longer response times bcause the individual was potentially unsure of their response. \n", + "Though causality can never be guaranteed in either of these cases, this analysis looks to see if there is some tangible effects in the data. From there, further analyses can be run to find more conclsive findings. \n", + "\n", + "## Important Variables\n", + "In this analysis, the dependent variable will be `log_rt` instead of `rt` because logarithmic values are easier to analyze and interpret when using statistical analyses. For the independent variables, we will use `valence` and `correct`. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Data processing and visualization\n" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# creating the right dataframe to plot and visualize the problem stated above\n", + "# get the error corrected by condition and whether they answered correctly\n", + "res = ci_within(df_w, \n", + " indexvar='subj', # column that identifies a subject\n", + " withinvars=['cond', 'correct', 'valence'], # list of columns for grouping within subject\n", + " measvar='log_rt') # dependent variable averaging over\n", + "\n", + "res = res.reset_index()\n", + "\n", + "p = (pn.ggplot(res, pn.aes('cond', 'mean', fill='valence'))\n", + " + pn.geom_errorbar(pn.aes(ymin='mean-ci', ymax='mean+ci', width=0.2), \n", + " position=pn.position_dodge(.7))\n", + " + pn.geom_point(position=pn.position_dodge(.7), size=4)\n", + " + pn.facet_wrap('~ correct')\n", + " + pn.labs(title=\"Figure 1: Plot of log_rt by valence across correctness\", x=\"Condition\", y = \"P(log_rt)\", fill='Valence')\n", + " )\n", + "p" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "In the figure above, we can see a plot of `log_rt` as a function of valence. The plot is split into correct and incorrect responses (labeled by the `1` and `0` at the top of each plot respectively). Furthermore, to account for differences in condition, two sets of plots (`mixed` and `pure`) were included for each valence and corectness. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Statistical test and interpretation" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjcondvalencecorrectlog_rt
0s001mixedneg00.167138
1s001mixedneg1-0.217212
2s001mixedneu0-0.109757
3s001mixedneu1-0.215241
4s001mixedpos00.166381
..................
271s023pureneg1-0.284881
272s023pureneu0-0.249797
273s023pureneu1-0.347838
274s023purepos0-0.357336
275s023purepos1-0.447838
\n", + "

276 rows × 5 columns

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" + ], + "text/plain": [ + " subj cond valence correct log_rt\n", + "0 s001 mixed neg 0 0.167138\n", + "1 s001 mixed neg 1 -0.217212\n", + "2 s001 mixed neu 0 -0.109757\n", + "3 s001 mixed neu 1 -0.215241\n", + "4 s001 mixed pos 0 0.166381\n", + ".. ... ... ... ... ...\n", + "271 s023 pure neg 1 -0.284881\n", + "272 s023 pure neu 0 -0.249797\n", + "273 s023 pure neu 1 -0.347838\n", + "274 s023 pure pos 0 -0.357336\n", + "275 s023 pure pos 1 -0.447838\n", + "\n", + "[276 rows x 5 columns]" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Code for statistical test using statsmodel\n", + "\n", + "# use the agg method to get the means\n", + "perf = df_w.groupby(['subj', 'cond', 'valence', 'correct'])['log_rt'].mean()\n", + "perf = perf.reset_index()\n", + "perf" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: log_rt R-squared: 0.144
Model: OLS Adj. R-squared: 0.109
Method: Least Squares F-statistic: 4.053
Date: Wed, 02 Dec 2020 Prob (F-statistic): 1.70e-05
Time: 22:59:04 Log-Likelihood: -52.458
No. Observations: 276 AIC: 128.9
Df Residuals: 264 BIC: 172.4
Df Model: 11
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept 0.0358 0.062 0.574 0.566 -0.087 0.159
cond[T.pure] -0.0377 0.088 -0.427 0.670 -0.211 0.136
valence[T.neu] -0.0979 0.088 -1.110 0.268 -0.272 0.076
valence[T.pos] 0.0480 0.088 0.544 0.587 -0.126 0.222
cond[T.pure]:valence[T.neu] 0.1971 0.125 1.580 0.115 -0.049 0.443
cond[T.pure]:valence[T.pos] -0.0671 0.125 -0.538 0.591 -0.313 0.179
correct -0.2348 0.088 -2.661 0.008 -0.409 -0.061
cond[T.pure]:correct 0.0233 0.125 0.186 0.852 -0.222 0.269
valence[T.neu]:correct 0.0741 0.125 0.594 0.553 -0.172 0.320
valence[T.pos]:correct -0.0286 0.125 -0.229 0.819 -0.274 0.217
cond[T.pure]:valence[T.neu]:correct -0.1750 0.176 -0.992 0.322 -0.522 0.172
cond[T.pure]:valence[T.pos]:correct 0.0680 0.176 0.385 0.700 -0.279 0.415
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Omnibus: 120.457 Durbin-Watson: 0.866
Prob(Omnibus): 0.000 Jarque-Bera (JB): 535.822
Skew: 1.794 Prob(JB): 4.44e-117
Kurtosis: 8.807 Cond. No. 25.6


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: log_rt R-squared: 0.144\n", + "Model: OLS Adj. R-squared: 0.109\n", + "Method: Least Squares F-statistic: 4.053\n", + "Date: Wed, 02 Dec 2020 Prob (F-statistic): 1.70e-05\n", + "Time: 22:59:04 Log-Likelihood: -52.458\n", + "No. Observations: 276 AIC: 128.9\n", + "Df Residuals: 264 BIC: 172.4\n", + "Df Model: 11 \n", + "Covariance Type: nonrobust \n", + "=======================================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "-------------------------------------------------------------------------------------------------------\n", + "Intercept 0.0358 0.062 0.574 0.566 -0.087 0.159\n", + "cond[T.pure] -0.0377 0.088 -0.427 0.670 -0.211 0.136\n", + "valence[T.neu] -0.0979 0.088 -1.110 0.268 -0.272 0.076\n", + "valence[T.pos] 0.0480 0.088 0.544 0.587 -0.126 0.222\n", + "cond[T.pure]:valence[T.neu] 0.1971 0.125 1.580 0.115 -0.049 0.443\n", + "cond[T.pure]:valence[T.pos] -0.0671 0.125 -0.538 0.591 -0.313 0.179\n", + "correct -0.2348 0.088 -2.661 0.008 -0.409 -0.061\n", + "cond[T.pure]:correct 0.0233 0.125 0.186 0.852 -0.222 0.269\n", + "valence[T.neu]:correct 0.0741 0.125 0.594 0.553 -0.172 0.320\n", + "valence[T.pos]:correct -0.0286 0.125 -0.229 0.819 -0.274 0.217\n", + "cond[T.pure]:valence[T.neu]:correct -0.1750 0.176 -0.992 0.322 -0.522 0.172\n", + "cond[T.pure]:valence[T.pos]:correct 0.0680 0.176 0.385 0.700 -0.279 0.415\n", + "==============================================================================\n", + "Omnibus: 120.457 Durbin-Watson: 0.866\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 535.822\n", + "Skew: 1.794 Prob(JB): 4.44e-117\n", + "Kurtosis: 8.807 Cond. No. 25.6\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# build a linear regression of the full model\n", + "m0 = smf.ols(\"log_rt ~ cond * valence * correct\", perf).fit()\n", + "m0.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Running an ANOVA on the linear model results\n", + "\n", + "- The results of such a complicated linear model is hard to unpack\n", + "- The most common approach to modeling the data would be a repeated measures ANOVA\n", + "- Luckily, a linear regress is effectively just an ANOVA if you make the right comparisons\n", + "- To simplify the results of the regression, we will run an ANOVA analysis below" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sum_sqdfFPR(>F)
cond0.0000181.00.0002029.886632e-01
valence0.0265832.00.1484748.620946e-01
cond:valence0.2566832.01.4336682.402830e-01
correct3.5196081.039.3165591.463755e-09
cond:correct0.0026581.00.0296888.633310e-01
valence:correct0.0043182.00.0241199.761714e-01
cond:valence:correct0.1807612.01.0096133.657622e-01
Residual23.633208264.0NaNNaN
\n", + "
" + ], + "text/plain": [ + " sum_sq df F PR(>F)\n", + "cond 0.000018 1.0 0.000202 9.886632e-01\n", + "valence 0.026583 2.0 0.148474 8.620946e-01\n", + "cond:valence 0.256683 2.0 1.433668 2.402830e-01\n", + "correct 3.519608 1.0 39.316559 1.463755e-09\n", + "cond:correct 0.002658 1.0 0.029688 8.633310e-01\n", + "valence:correct 0.004318 2.0 0.024119 9.761714e-01\n", + "cond:valence:correct 0.180761 2.0 1.009613 3.657622e-01\n", + "Residual 23.633208 264.0 NaN NaN" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run a type-II repeated measures ANOVA based on the linear model results\n", + "sm.stats.anova_lm(m0, typ=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Findings\n", + "\n", + "Starting with the plots presented in Figure 1 above, it is clear that there is a discrepancy in response times for responses across corectness. Specfically, we see that incorrect responses are significantly slower than correct responses: big discrepancies in `log_rt` are obvious in incorrect vs correct plots. \n", + "\n", + "This is also supported by the results of our linear regression and ANOVA tests which show a significant F value of 39.316559 for corectness. This finding seems to support part of our hypothesis that corecntess has some positive effect on response times (larger response time = slower). This is a very promising finding from our analysis, and with further study, more robust causal effects of correctness on response times may be found. \n", + "\n", + "\n", + "However, when looking at the the plots presented in Figure 1 again, there do not seem to be similar significant effects of valence and condition on response times. In the plots, there seem to be very minimal (if any) discrepancies in response times when comapring data across valence and condition. This is also supported in the linear regression and ANOVA results showing very small F values for both condition and valence. This finding seems to go against part of our initial hypothesis that valence and condition may have some effect on response times. Though this is not conlusive, it gives some evidence representing the data points in this experiment. Additional analyes will be required to show a more accurate effect of valence and condition on participants' response times. \n", + "\n", + "\n", + "On that note, it is worth noting that though there were no significant findings of valence effects on response times, in the plots in Figure 1, there seemed to be some effect of positive and negative valence vs neutral valence on response times. That is to say that, in the plots, positive and negative valences seemed to have slower response times when compared to neutral valences. This is a very interesting observation, that would require further analysis to verify: perhaps using a linear mixed effects regression will be needed to analyze `abs_valence` to see if this is a valid and significant effect. \n", + "\n", + "In sum, we have found reason to believe that corectness has some tangible effect on response times based on the data points we observed. Additionally, valence and condition appeared to have no effects on response times based on our analysis. Though there are some promising plots showing some mild effects of `abs_valence` on response times, it is not conclusive enough to determine a statstically significant effect. More study and analyses will be needed to confirm the accuracy of our findings on both correctness and valence (potentially interacting with condition). \n" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assignments/Option_1_solution.pickle b/assignments/Option_1_solution.pickle new file mode 100644 index 0000000..f1d2437 Binary files /dev/null and b/assignments/Option_1_solution.pickle differ diff --git a/assignments/data/MEMORY RECOGNITION/Jerome/20201019_144812/log_memory_recognition_study_0 2.slog b/assignments/data/MEMORY 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b/assignments/data/VALENCE/test_again/20201019_170848/state_Wait_0.slog differ diff --git a/assignments/quotes.txt b/assignments/quotes.txt new file mode 100644 index 0000000..e9697c0 --- /dev/null +++ b/assignments/quotes.txt @@ -0,0 +1,6 @@ + +"Strange things are afoot at the Circle-K." - Ted, Bill & Ted's Excellent Adventure + +"Be water my friend" - Bruce Lee + +"Just keep swimming" - Dory \ No newline at end of file diff --git a/lessons/.DS_Store b/lessons/.DS_Store new file mode 100644 index 0000000..3fc650f Binary files /dev/null and b/lessons/.DS_Store differ diff --git a/lessons/02b_Jupyter_Python.ipynb b/lessons/02b_Jupyter_Python.ipynb index 8081662..8e9749f 100644 --- a/lessons/02b_Jupyter_Python.ipynb +++ b/lessons/02b_Jupyter_Python.ipynb @@ -1570,7 +1570,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.7" }, "rise": { "scroll": true diff --git a/lessons/03_More_Python.ipynb b/lessons/03_More_Python.ipynb index 70ccbf3..7e354ea 100644 --- a/lessons/03_More_Python.ipynb +++ b/lessons/03_More_Python.ipynb @@ -1408,7 +1408,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.7" }, "rise": { "scroll": true diff --git a/lessons/04_Python_Forever.ipynb b/lessons/04_Python_Forever.ipynb index 994b9be..c227f3b 100644 --- a/lessons/04_Python_Forever.ipynb +++ b/lessons/04_Python_Forever.ipynb @@ -2152,7 +2152,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.7" }, "rise": { "scroll": true diff --git a/lessons/05_FileIO_ExpDesign.ipynb b/lessons/05_FileIO_ExpDesign.ipynb index b797621..9428a22 100644 --- a/lessons/05_FileIO_ExpDesign.ipynb +++ b/lessons/05_FileIO_ExpDesign.ipynb @@ -528,25 +528,25 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[OrderedDict([('Subject', '0'), ('Performance', '0.19839211032002024')]),\n", - " OrderedDict([('Subject', '1'), ('Performance', '0.41428489112125344')]),\n", - " OrderedDict([('Subject', '2'), ('Performance', '0.027715898314496612')]),\n", - " OrderedDict([('Subject', '3'), ('Performance', '0.05627103270567213')]),\n", - " OrderedDict([('Subject', '4'), ('Performance', '0.27079871696692148')]),\n", - " OrderedDict([('Subject', '5'), ('Performance', '0.93739232241039394')]),\n", - " OrderedDict([('Subject', '6'), ('Performance', '0.49069767020105493')]),\n", - " OrderedDict([('Subject', '7'), ('Performance', '0.24287893232441449')]),\n", - " OrderedDict([('Subject', '8'), ('Performance', '0.97942327679701313')]),\n", - " OrderedDict([('Subject', '9'), ('Performance', '0.3229346781148571')])]" + "[{'Subject': '0'},\n", + " {'Subject': '1'},\n", + " {'Subject': '2'},\n", + " {'Subject': '3'},\n", + " {'Subject': '4'},\n", + " {'Subject': '5'},\n", + " {'Subject': '6'},\n", + " {'Subject': '7'},\n", + " {'Subject': '8'},\n", + " {'Subject': '9'}]" ] }, - "execution_count": 44, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -557,11 +557,19 @@ "# create a dictionary reader\n", "dr = csv.DictReader(open('exp_res.csv','r'))\n", "\n", + "d=[]\n", + "for l in dr:\n", + " x = {}\n", + " x['Subject'] = l['Subject']\n", + " d.append(x)\n", + " \n", + " \n", + "d\n", "# read in all the lines into a list of dicts\n", - "d = [l for l in dr]\n", + "# d = [l for l in dr]\n", "\n", - "# note it creates OrderedDict instances!!!\n", - "d" + "# # note it creates OrderedDict instances!!!\n", + "# d" ] }, { @@ -1244,7 +1252,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.7" }, "rise": { "scroll": true diff --git a/lessons/06_SMILE.ipynb b/lessons/06_SMILE.ipynb index b96b8ac..76cf014 100644 --- a/lessons/06_SMILE.ipynb +++ b/lessons/06_SMILE.ipynb @@ -856,7 +856,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.7" }, "rise": { "scroll": true diff --git a/lessons/07_More_SMILE.ipynb b/lessons/07_More_SMILE.ipynb index 6b99758..a81111d 100644 --- a/lessons/07_More_SMILE.ipynb +++ b/lessons/07_More_SMILE.ipynb @@ -471,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -511,14 +511,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[{'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}]\n" + "[{'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}]\n" ] } ], @@ -586,44 +586,13 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "metadata": { "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DEBUG (file: 'Turn on Debug Mode for this information', line: 0) - lag=0.010451s\n", - " loc: (553.337226902715, 419.57599905019185)\n", - "DEBUG (file: 'Turn on Debug Mode for this information', line: 0) - lag=0.000335s\n", - " loc: (468.42978777578685, 483.73067860311903)\n", - "DEBUG (file: 'Turn on Debug Mode for this information', line: 0) - lag=0.000258s\n", - " loc: (652.9613072769196, 478.74401154346725)\n", - "DEBUG (file: 'Turn on Debug Mode for this information', line: 0) - lag=0.010434s\n", - " loc: (552.3354316062297, 474.33882746618247)\n", - "DEBUG (file: 'Turn on Debug Mode for this information', line: 0) - lag=0.000182s\n", - " loc: (524.8795750812591, 445.0314250378785)\n", - "DEBUG (file: 'Turn on Debug Mode for this information', line: 0) - lag=0.005016s\n", - " loc: (589.2237897769287, 404.12712633909507)\n", - "DEBUG (file: 'Turn on Debug Mode for this information', line: 0) - lag=0.010199s\n", - " loc: (319.90906601013745, 381.84150839137203)\n", - "DEBUG (file: 'Turn on Debug Mode for this information', line: 0) - lag=0.001981s\n", - " loc: (373.96437022792094, 394.5253647231236)\n", - "DEBUG (file: 'Turn on Debug Mode for this information', line: 0) - lag=0.000616s\n", - " loc: (472.68955672599174, 401.1222078319125)\n", - "DEBUG (file: 'Turn on Debug Mode for this information', line: 0) - lag=0.009787s\n", - " loc: (710.6248072118226, 391.8421775039566)\n", - "DEBUG (file: 'Turn on Debug Mode for this information', line: 0) - lag=0.003806s\n", - " loc: (544.0254167387299, 296.01733524071847)\n", - "DEBUG (file: 'Turn on Debug Mode for this information', line: 0) - lag=0.000449s\n", - " loc: (393.7753980116059, 346.94324705023996)\n" - ] - } - ], + "outputs": [], "source": [ "from smile.common import *\n", "\n", @@ -707,7 +676,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.7" }, "rise": { "scroll": true diff --git a/lessons/08_Exp_to_Data-Notes.ipynb b/lessons/08_Exp_to_Data-Notes.ipynb new file mode 100644 index 0000000..21329be --- /dev/null +++ b/lessons/08_Exp_to_Data-Notes.ipynb @@ -0,0 +1,662 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# From Experiments to Data\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020\n", + "By: Per B. Sederberg, PhD\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Lesson Objectives\n", + "\n", + "Upon completion of this lesson, students should have learned:\n", + "\n", + "1. How to polish up a complete experiment\n", + "2. How SMILE stores data\n", + "3. How to read in slog files\n", + "4. An introduction to Pandas\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Review list generation solution\n", + "\n", + "- Let's go through the list gen code you'll use for your next assignment!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Updating SMILE\n", + "\n", + "- First you can test whether there is a new version Kivy, which is the primary dependency of SMILE:\n", + "\n", + "```bash\n", + "conda install -c conda-forge kivy\n", + "```\n", + "\n", + "- Then you can update SMILE right from the GitHub repository (note the upgrade option at the end):\n", + "\n", + "```bash\n", + "pip install git+https://github.com/compmem/smile --upgrade\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Subject Info\n", + "\n", + "- When collecting data from a lot of participants in a lab setting, you need an easy way to enter subject information once the experiment has started." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO ] [Logger ] Record log in /Users/uva/.kivy/logs/kivy_20-10-15_1.txt\n", + "[INFO ] [Kivy ] v1.11.1\n", + "[INFO ] [Kivy ] Installed at \"/Users/uva/opt/anaconda3/envs/compsy/lib/python3.7/site-packages/kivy/__init__.py\"\n", + "[INFO ] [Python ] v3.7.7 (default, May 6 2020, 04:59:01) \n", + "[Clang 4.0.1 (tags/RELEASE_401/final)]\n", + "[INFO ] [Python ] Interpreter at \"/Users/uva/opt/anaconda3/envs/compsy/bin/python\"\n", + "[INFO ] [Factory ] 184 symbols loaded\n", + "[INFO ] [Image ] Providers: img_tex, img_imageio, img_dds, img_sdl2, img_pil, img_gif (img_ffpyplayer ignored)\n", + "[INFO ] [Text ] Provider: sdl2\n", + "[INFO ] [Camera ] Provider: avfoundation\n", + "[INFO ] [VideoGstplayer] Using Gstreamer 1.14.5.0\n", + "[INFO ] [Video ] Provider: gstplayer\n", + "[WARNING] [SMILE ] Unable to import PYO!\n", + "[WARNING] [SMILE ] Durations will be maintained, unless none are specified\n", + "[INFO ] [Window ] Provider: sdl2\n", + "[INFO ] [GL ] Using the \"OpenGL ES 2\" graphics system\n", + "[INFO ] [GL ] Backend used \n", + "[INFO ] [GL ] OpenGL version \n", + "[INFO ] [GL ] OpenGL vendor \n", + "[INFO ] [GL ] OpenGL renderer \n", + "[INFO ] [GL ] OpenGL parsed version: 2, 1\n", + "[INFO ] [GL ] Shading version \n", + "[INFO ] [GL ] Texture max size <16384>\n", + "[INFO ] [GL ] Texture max units <16>\n", + "[INFO ] [Window ] auto add sdl2 input provider\n", + "[INFO ] [Window ] virtual keyboard not allowed, single mode, not docked\n", + "[INFO ] [Base ] Start application main loop\n", + "[INFO ] [GL ] NPOT texture support is available\n", + "[INFO ] [WindowSDL ] exiting mainloop and closing.\n", + "[INFO ] [Base ] Leaving application in progress...\n", + "[INFO ] [Window ] Provider: sdl2\n", + "[INFO ] [GL ] Using the \"OpenGL ES 2\" graphics system\n", + "[INFO ] [GL ] Backend used \n", + "[INFO ] [GL ] OpenGL version \n", + "[INFO ] [GL ] OpenGL vendor \n", + "[INFO ] [GL ] OpenGL renderer \n", + "[INFO ] [GL ] OpenGL parsed version: 2, 1\n", + "[INFO ] [GL ] Shading version \n", + "[INFO ] [GL ] Texture max size <16384>\n", + "[INFO ] [GL ] Texture max units <16>\n", + "[INFO ] [Window ] auto add sdl2 input provider\n", + "[INFO ] [Window ] virtual keyboard not allowed, single mode, not docked\n", + "[INFO ] [Base ] Start application main loop\n", + "[INFO ] [Base ] Leaving application in progress...\n" + ] + } + ], + "source": [ + "from smile.common import *\n", + "from smile.startup import InputSubject\n", + "\n", + "exp = Experiment(show_splash=False, resolution=(1024,768))\n", + "\n", + "InputSubject('Flanker')\n", + "\n", + "Label(text='Hello!')\n", + "with UntilDone():\n", + " KeyPress()\n", + " \n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Instructions\n", + "\n", + "- Participants need some guidance as to how they should perform the task\n", + "- This can be just text via `Label` states, but even better would be a visual description, and even better would be a full tutorial.\n", + "- ***NOTE: If you provide a tutorial, do not use items that you will use for the actual task!***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from smile.common import *\n", + "\n", + "exp = Experiment(show_splash=False, resolution=(1024,768))\n", + "\n", + "# Labels can be multi-line with markup!\n", + "with Parallel():\n", + " Label(text=\"This is multi-line text aligned to the left\", \n", + " font_size=50, text_size=(400, None),\n", + " left=exp.screen.left)\n", + " Label(text=\"This is multi-line text aligned to the right\", \n", + " font_size=50, text_size=(400, None), \n", + " right=exp.screen.right, halign='right')\n", + "with UntilDone():\n", + " KeyPress()\n", + " \n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Scaling to different screen sizes/densities\n", + "\n", + "- Screen sizes and pixel densities can vary across devices (especially on laptops, tablets, and phones)\n", + "- But all sizes in Kivy/SMILE are defined by number of pixels\n", + "- We provide a means to scale across devices:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " SMILE Traceback:\n", + " Serial (EXPERIMENT BODY) - file: , line: 9\n", + " instantiation_filename: ''\n", + " instantiation_lineno: 9\n", + " name: 'EXPERIMENT BODY'\n", + " start_time: 0.238272s from now\n", + " end_time: None\n", + " enter_time: 0.011704s ago\n", + " leave_time: NotAvailable\n", + " finalize_time: NotAvailable\n", + " Wait - file: Turn on Debug Mode for this information, line: 0\n", + " instantiation_filename: 'Turn on Debug Mode for this information'\n", + " instantiation_lineno: 0\n", + " name: None\n", + " start_time: 0.238272s from now\n", + " end_time: 0.738272s from now\n", + " enter_time: 0.008963s ago\n", + " leave_time: NotAvailable\n", + " finalize_time: NotAvailable\n", + " until_value: None\n", + " event_time: None\n" + ] + }, + { + "ename": "KeyError", + "evalue": "'ParentSet'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mKeyPress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m 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"\u001b[0;32m~/opt/anaconda3/envs/compsy/lib/python3.7/site-packages/kivy/base.py\u001b[0m in \u001b[0;36mrunTouchApp\u001b[0;34m(widget, slave)\u001b[0m\n\u001b[1;32m 502\u001b[0m \u001b[0m_run_mainloop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 503\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m \u001b[0mEventLoop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwindow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmainloop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 505\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 506\u001b[0m \u001b[0mstopTouchApp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/envs/compsy/lib/python3.7/site-packages/kivy/core/window/window_sdl2.py\u001b[0m in \u001b[0;36mmainloop\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 745\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mEventLoop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquit\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mEventLoop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'started'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 747\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mainloop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 748\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0minst\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 749\u001b[0m \u001b[0;31m# use exception manager first\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/envs/compsy/lib/python3.7/site-packages/kivy/core/window/window_sdl2.py\u001b[0m in \u001b[0;36m_mainloop\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 478\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_mainloop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 479\u001b[0;31m \u001b[0mEventLoop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 481\u001b[0m \u001b[0;31m# for android/iOS, we don't want to have any event nor executing our\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/envs/compsy/lib/python3.7/site-packages/smile/kivy_overrides.py\u001b[0m in \u001b[0;36midle\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0midle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_callback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_callback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;31m# don't loop if we don't have listeners !\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/envs/compsy/lib/python3.7/site-packages/smile/main.py\u001b[0m in \u001b[0;36m_idle_callback\u001b[0;34m(self, event_loop)\u001b[0m\n\u001b[1;32m 250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[0;31m# call any of our scheduled events that are ready\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m \u001b[0mclock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[0;31m# dispatch input events\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/envs/compsy/lib/python3.7/site-packages/smile/clock.py\u001b[0m in \u001b[0;36mtick\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevent_time\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat_interval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_schedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0mevent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 35\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/envs/compsy/lib/python3.7/site-packages/smile/state.py\u001b[0m in \u001b[0;36mfinalize\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 794\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_active\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 795\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__save_log\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 796\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_log\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 797\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 798\u001b[0m \u001b[0mclock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchild_finalize_callback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/envs/compsy/lib/python3.7/site-packages/smile/state.py\u001b[0m in \u001b[0;36msave_log\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 781\u001b[0m self._exp.write_to_state_log(\n\u001b[1;32m 782\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 783\u001b[0;31m {name : getattr(self, \"_\" + name) for name in self._log_attrs})\n\u001b[0m\u001b[1;32m 784\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 785\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfinalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m#TODO: call a _finalize method?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/envs/compsy/lib/python3.7/site-packages/smile/experiment.py\u001b[0m in \u001b[0;36mwrite_to_state_log\u001b[0;34m(self, state_class_name, record)\u001b[0m\n\u001b[1;32m 545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 546\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mwrite_to_state_log\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate_class_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 547\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state_loggers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstate_class_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_record\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_flush_state_loggers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'ParentSet'" + ] + } + ], + "source": [ + "# load in smile states\n", + "from smile.common import *\n", + "from smile.scale import scale as s\n", + "\n", + "# create an experiment instance (pick different resolutions)\n", + "resolution = (800,600)\n", + "#resolution = (1024,768)\n", + "exp = Experiment(show_splash=False, resolution=resolution,\n", + " scale_up=True, scale_down=True, scale_box=(400, 300))\n", + "Wait(.5)\n", + "with Parallel():\n", + " # add a scaled rectangle\n", + " Rectangle(width=s(250),height=s(250))\n", + " \n", + " # and a fixed size rectangle\n", + " Rectangle(width=100, height=100, color='blue')\n", + "with UntilDone():\n", + " KeyPress()\n", + "\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Logging and Slogging\n", + "\n", + "- SMILE states automatically log themselves, so you rarely will lose information if you forget to log it.\n", + "- It's still much easier to analyze a well-organized log file from your experiment.\n", + "- Make use of the `Log` state to save out data.\n", + "- All logs are stored in `.slog` files, which is short for SMILE Log.\n", + "- These are read in as a list of dictionaries (or a dict-list):\n", + "\n", + "```python\n", + "from smile.log import log2dl\n", + "dl = log2dl('flanker_log.slog')\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Where have all the data gone?\n", + "\n", + "- All the data files are stored in the `data` directory\n", + " - NOTE: Even when you don't specify a subj, it saves to a `test000` directory, so you can delete that sometimes to save space.\n", + "- There is a hierarchical structure to the data directory:\n", + " - Experiment -> SubjID -> Date/Time" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "state_KeyPress_0.slog state_Rectangle_0.slog state_Wait_0.slog\r\n", + "state_Parallel_0.slog state_Serial_0.slog sysinfo.slog\r\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'fullscreen': False,\n", + " 'locked': 0,\n", + " 'density': '1.0',\n", + " 'font_size': 45.0,\n", + " 'font_name': 'Roboto',\n", + " 'frame_rate': 60.0,\n", + " 'default_data_dir': './data',\n", + " 'data_time': '20201015_154135',\n", + " 'debug': False,\n", + " 'resolution_0': 800,\n", + " 'resolution_1': 600,\n", + " 'background_color': None,\n", + " 'scale_box_0': 400,\n", + " 'scale_box_1': 300,\n", + " 'scale_up': True,\n", + " 'scale_down': True,\n", + " 'expname': 'SMILE',\n", + " 'processor': 'i386',\n", + " 'python_version': '3.7.7',\n", + " 'system': 'Darwin',\n", + " 'version': '',\n", + " 'author': '',\n", + " 'email': '',\n", + " 'date_last_update': '',\n", + " 'uname_0': 'Darwin',\n", + " 'uname_1': 'Jeromes-MacBook-Pro.local',\n", + " 'uname_2': '19.2.0',\n", + " 'uname_3': 'Darwin Kernel Version 19.2.0: Sat Nov 9 03:47:04 PST 2019; root:xnu-6153.61.1~20/RELEASE_X86_64',\n", + " 'uname_4': 'x86_64',\n", + " 'uname_5': 'i386',\n", + " 'screen_size_0': 1600,\n", + " 'screen_size_1': 1200,\n", + " 'log_num': 0}]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from smile.log import log2dl\n", + "!ls data/SMILE/test000/20201015_154135\n", + "\n", + "dl = log2dl(\"data/SMILE/test000/20201015_154135/sysinfo.slog\")\n", + "dl" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Let's continue learning by building together!\n", + "\n", + "- Remaining features to add:\n", + " - Instructions\n", + " - Loop over blocks and then trials\n", + " - Verify logging" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# List Gen Function\n", + "\n", + "- NOTE: New addition for multiple blocks of trials" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import random \n", + "import copy\n", + "\n", + "# define the conditions\n", + "conds = [{'condition': 'congruent',\n", + " 'direction': 'left',\n", + " 'stimulus': '<<<<<<<'\n", + " },\n", + " {'condition': 'congruent',\n", + " 'direction': 'right',\n", + " 'stimulus': '>>>>>>>'\n", + " },\n", + " {'condition': 'incongruent',\n", + " 'direction': 'left',\n", + " 'stimulus': '>>><>>>'\n", + " },\n", + " {'condition': 'incongruent',\n", + " 'direction': 'right',\n", + " 'stimulus': '<<<><<<'\n", + " },\n", + " {'condition': 'neutral',\n", + " 'direction': 'left',\n", + " 'stimulus': '===<==='\n", + " },\n", + " {'condition': 'neutral',\n", + " 'direction': 'right',\n", + " 'stimulus': '===>==='\n", + " },]\n", + "\n", + "# specify number of reps of these conditions\n", + "num_reps = 2\n", + "num_blocks = 3\n", + "\n", + "# loop and create the blocks\n", + "blocks = []\n", + "for b in range(num_blocks):\n", + " # loop and create the list\n", + " trials = []\n", + " for i in range(num_reps):\n", + " # extend the trials with copies of the conditions\n", + " trials.extend(copy.deepcopy(conds))\n", + " \n", + " # shuffle the trials\n", + " random.shuffle(trials)\n", + " \n", + " # append the trials to the blocks\n", + " blocks.append(trials)\n", + "\n", + "print(blocks)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Goal for task\n", + "\n", + "## High level structure\n", + "\n", + "- Present instructions\n", + "- Loop over blocks\n", + "- Within each block, loop over trials\n", + "\n", + "## Trial structure\n", + "\n", + "- Present the correct stimulus as text on the screen\n", + "- Wait for a response\n", + "- Remove the stimulus\n", + "- Wait for an inter-stimulus interval\n", + "- Log the result of the trial" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "from smile.common import *\n", + "from smile.scale import scale as s\n", + "\n", + "# config section\n", + "font_size = s(75)\n", + "resp_keys = ['F', 'J']\n", + "resp_map = {'left': 'F', 'right': 'J'}\n", + "ISI_dur = 0.5\n", + "ISI_jitter = 0.5\n", + "LOC_X_jitter = s(200)\n", + "LOC_Y_jitter = s(100)\n", + "inst_font_size = s(25)\n", + "inst_text = \"\"\"[size=40]FLANKER INSTRUCTIONS[/size]\n", + " \n", + "Press ENTER key to continue.\"\"\"\n", + "\n", + "# create the experiment\n", + "exp = Experiment(name='FLANKER', show_splash=False, \n", + " fullscreen=False,\n", + " resolution=(1024, 768))\n", + "\n", + "@Subroutine\n", + "def Instruct(self):\n", + " # show the instructions\n", + " Label(text=inst_text, font_size=inst_font_size,\n", + " text_size=(exp.screen.width*0.75, None),\n", + " markup=True)\n", + " with UntilDone():\n", + " KeyPress(keys=['ENTER'])\n", + "\n", + "@Subroutine\n", + "def Trial(self, block_num, trial_num, cur_trial):\n", + " # pick the new stimulus location\n", + " self.location = (jitter(self.exp.screen.center_x-LOC_X_jitter,\n", + " LOC_X_jitter*2),\n", + " jitter(self.exp.screen.center_y-LOC_Y_jitter,\n", + " LOC_Y_jitter*2))\n", + " # present the stimulus\n", + " stim = Label(text=cur_trial['stimulus'],\n", + " font_size=font_size,\n", + " center=self.location)\n", + " with UntilDone():\n", + " # make sure the stimulus has appeared on the screen\n", + " Wait(until=stim.appear_time)\n", + " \n", + " # collect a response (with no timeout)\n", + " kp = KeyPress(keys=resp_keys, \n", + " base_time=stim.appear_time['time'],\n", + " correct_resp=Ref.object(resp_map)[cur_trial['direction']])\n", + " \n", + " # wait the ISI with jitter\n", + " Wait(ISI_dur, jitter=ISI_jitter)\n", + "\n", + " # log the result of the trial\n", + " Log(name='flanker', \n", + " log_dict=cur_trial,\n", + " block_num=block_num,\n", + " trial_num=trial_num,\n", + " stim_on=stim.appear_time,\n", + " resp=kp.pressed,\n", + " resp_time=kp.press_time,\n", + " rt=kp.rt,\n", + " correct=kp.correct,\n", + " location=self.location\n", + " )\n", + " \n", + "# show the instructions\n", + "Instruct()\n", + "Wait(0.5)\n", + "\n", + "# loop over the blocks\n", + "with Loop(blocks) as block:\n", + " # make sure they are ready to continue\n", + " Label(text='Press the ENTER key to\\nstart the next block.', \n", + " font_size=font_size, halign='center')\n", + " with UntilDone():\n", + " KeyPress(keys=['ENTER'])\n", + "\n", + " # add in some delay before the start of the block\n", + " Wait(ISI_dur, jitter=ISI_jitter)\n", + " \n", + " # loop over the trials\n", + " with Loop(block.current) as trial:\n", + " Trial(block.i, trial.i, trial.current)\n", + "\n", + "# run the experiment\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Read in some data from the logs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from smile.log import log2dl\n", + "log2dl('data/SMILE/test000/20201015_002203/log_flanker_0.slog')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment before next class\n", + "\n", + "- Your SMILE experiment is due next Thursday!\n", + "\n", + "### See you next week!!!" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lessons/08_Exp_to_Data.ipynb b/lessons/08_Exp_to_Data.ipynb index 9a21cb5..4c43e59 100644 --- a/lessons/08_Exp_to_Data.ipynb +++ b/lessons/08_Exp_to_Data.ipynb @@ -84,9 +84,46 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO ] [Logger ] Record log in /Users/uva/.kivy/logs/kivy_20-10-19_18.txt\n", + "[INFO ] [Kivy ] v1.11.1\n", + "[INFO ] [Kivy ] Installed at \"/Users/uva/opt/anaconda3/envs/compsy/lib/python3.7/site-packages/kivy/__init__.py\"\n", + "[INFO ] [Python ] v3.7.7 (default, May 6 2020, 04:59:01) \n", + "[Clang 4.0.1 (tags/RELEASE_401/final)]\n", + "[INFO ] [Python ] Interpreter at \"/Users/uva/opt/anaconda3/envs/compsy/bin/python\"\n", + "[INFO ] [Factory ] 184 symbols loaded\n", + "[INFO ] [Image ] Providers: img_tex, img_imageio, img_dds, img_sdl2, img_pil, img_gif (img_ffpyplayer ignored)\n", + "[INFO ] [Text ] Provider: sdl2\n", + "[INFO ] [Camera ] Provider: avfoundation\n", + "[INFO ] [VideoGstplayer] Using Gstreamer 1.14.5.0\n", + "[INFO ] [Video ] Provider: gstplayer\n", + "[WARNING] [SMILE ] Unable to import PYO!\n", + "[WARNING] [SMILE ] Durations will be maintained, unless none are specified\n", + "[INFO ] [Window ] Provider: sdl2\n", + "[INFO ] [GL ] Using the \"OpenGL ES 2\" graphics system\n", + "[INFO ] [GL ] Backend used \n", + "[INFO ] [GL ] OpenGL version \n", + "[INFO ] [GL ] OpenGL vendor \n", + "[INFO ] [GL ] OpenGL renderer \n", + "[INFO ] [GL ] OpenGL parsed version: 2, 1\n", + "[INFO ] [GL ] Shading version \n", + "[INFO ] [GL ] Texture max size <16384>\n", + "[INFO ] [GL ] Texture max units <16>\n", + "[INFO ] [Window ] auto add sdl2 input provider\n", + "[INFO ] [Window ] virtual keyboard not allowed, single mode, not docked\n", + "[INFO ] [Base ] Start application main loop\n", + "[INFO ] [GL ] NPOT texture support is available\n", + "[INFO ] [WindowSDL ] exiting mainloop and closing.\n", + "[INFO ] [Base ] Leaving application in progress...\n" + ] + } + ], "source": [ "from smile.common import *\n", "from smile.startup import InputSubject\n", @@ -324,14 +361,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[{'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}], [{'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}], [{'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}]]\n" + "[[{'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}], [{'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}], [{'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}]]\n" ] } ], @@ -414,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "metadata": { "slideshow": { "slide_type": "slide" @@ -1342,7 +1379,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.7" }, "rise": { "scroll": true diff --git a/lessons/09_Data_Processing 2.ipynb b/lessons/09_Data_Processing 2.ipynb new file mode 100644 index 0000000..69fb4f2 --- /dev/null +++ b/lessons/09_Data_Processing 2.ipynb @@ -0,0 +1,2368 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Data Processing\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020\n", + "By: Per B. Sederberg, PhD\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Lesson Objectives\n", + "\n", + "Upon completion of this lesson, students should have learned:\n", + "\n", + "1. Read data from slog files\n", + "2. The Series and DataFrame data structures in Pandas\n", + "3. Load slogs as a DataFrame\n", + "4. Some basic operations on the data\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Updating SMILE\n", + "\n", + "- First you can test whether there is a new version Kivy, which is the primary dependency of SMILE:\n", + "\n", + "```bash\n", + "conda install -c conda-forge kivy==1.11.1\n", + "```\n", + "\n", + "- Then you can update SMILE right from the GitHub repository (note the upgrade option at the end):\n", + "\n", + "```bash\n", + "pip install git+https://github.com/compmem/smile --upgrade\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Math Distract\n", + "\n", + "- Sometimes you want to have a delay period, e.g., between study and test\n", + "- Although it may be fine to have an empty delay, often you'd like to fill it with a task that prevents rehearsal of the studied items\n", + "- We provide a subroutine that generates math problems!" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "from smile.common import *\n", + "from smile.math_distract import MathDistract\n", + "\n", + "exp = Experiment(show_splash=False, resolution=(1024,768))\n", + "\n", + "Wait(1.0)\n", + "MathDistract(num_vars=3,\n", + " min_num=1,\n", + " max_num=9,\n", + " max_probs=50,\n", + " duration=20)\n", + " \n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Moving Dot stimuli\n", + "\n", + "- A class of stimuli at the core of many studies of perceptual decision-making\n", + "- We provide a custom state for it:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "from smile.common import *\n", + "from smile.moving_dots import MovingDots\n", + "\n", + "exp = Experiment(show_splash=False, resolution=(1024,768))\n", + "\n", + "# set up some config\n", + "dot_speed = 180\n", + "\n", + "# set initial values\n", + "exp.cr=0.2\n", + "exp.cl=0.2\n", + "motion_props = [{\"coherence\": exp.cr, \"direction\": 0, \"direction_variance\": 0},\n", + " {\"coherence\": exp.cl, \"direction\": 180, \"direction_variance\": 0}]\n", + "with Loop():\n", + " with Parallel():\n", + " dots = MovingDots(color='white', scale=3, num_dots=100, radius=200,\n", + " motion_props=motion_props, speed=dot_speed,\n", + " lifespan=0.5, lifespan_variance=1.5)\n", + " lr = Label(text='Right Coherence:\\n'+Ref(str,exp.cr), left=dots.right+40, font_size=28) \n", + " ll = Label(text='Left Coherence:\\n'+Ref(str,exp.cl), right=dots.left-40, font_size=28)\n", + "\n", + " with UntilDone():\n", + " kp = KeyPress(keys=['UP','DOWN','LEFT','RIGHT'])\n", + " with If(kp.pressed=='UP'):\n", + " exp.cr=exp.cr+0.05\n", + " exp.cl=exp.cl+0.05\n", + " with If(exp.cr+exp.cl>1.0):\n", + " exp.cr=exp.cr-0.05\n", + " exp.cl=exp.cl-0.05\n", + " with Elif((kp.pressed=='DOWN')):\n", + " exp.cr=exp.cr-0.05\n", + " exp.cl=exp.cl-0.05\n", + " with If(exp.cr<0.05):\n", + " exp.cr=0.0\n", + " with If(exp.cl<0.05):\n", + " exp.cl=0.0\n", + " with Elif(kp.pressed=='LEFT'):\n", + " exp.cl=exp.cl+0.05\n", + " with If(exp.cr+exp.cl>1.0):\n", + " exp.cl=exp.cl-0.05\n", + " with Elif(kp.pressed=='RIGHT'):\n", + " exp.cr=exp.cr+0.05\n", + " with If(exp.cr+exp.cl>1.0):\n", + " exp.cr=exp.cr-0.05\n", + " # update the motion props\n", + " dots.update(motion_props=[{\"coherence\": exp.cr, \"direction\": 0},\n", + " {\"coherence\": exp.cl, \"direction\": 180}])\n", + " lr.update(text='Right Coherence:\\n'+Ref(str,exp.cr))\n", + " ll.update(text='Left Coherence:\\n'+Ref(str,exp.cl))\n", + "with UntilDone():\n", + " KeyPress(keys=['ENTER'])\n", + "\n", + " \n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Reading in slog files\n", + "\n", + "- SMILE stores data in log files with the `.slog` file extension\n", + "- It is a custom format that are pickled and compressed dictionaries\n", + "- We can read them in with a SMILE function `log2dl` that converts the log to a list of dictionaries (i.e., a dict list):" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'rt': 2.941615221919392,\n", + " 'disappear_time_error': 0.0,\n", + " 'disappear_time_time': 617.657840390051,\n", + " 'run_num': 0,\n", + " 'refresh_rate': 26.731467774154293,\n", + " 'appear_time_error': 0.0,\n", + " 'appear_time_time': 614.7072808193332,\n", + " 'eeg_pulse_time': None,\n", + " 'right_coherence': 0.06,\n", + " 'left_coherence': 0.0,\n", + " 'press_time_error': 0.0005104763318968253,\n", + " 'press_time_time': 617.6488960412526,\n", + " 'incorrect_resp': '1',\n", + " 'pressed': '1',\n", + " 'log_time': 618.1488960412526,\n", + " 'correct_resp': '4',\n", + " 'correct': False,\n", + " 'fmri_tr_time': None,\n", + " 'log_num': 0},\n", + " {'rt': 1.5730700115865375,\n", + " 'disappear_time_error': 0.0,\n", + " 'disappear_time_time': 620.2500236883183,\n", + " 'run_num': 0,\n", + " 'refresh_rate': 26.59035375261749,\n", + " 'appear_time_error': 0.0,\n", + " 'appear_time_time': 618.6663967214259,\n", + " 'eeg_pulse_time': None,\n", + " 'right_coherence': 0.24,\n", + " 'left_coherence': 0.3,\n", + " 'press_time_error': 0.00047487459045214564,\n", + " 'press_time_time': 620.2394667330125,\n", + " 'incorrect_resp': '4',\n", + " 'pressed': '4',\n", + " 'log_time': 620.7394667330125,\n", + " 'correct_resp': '1',\n", + " 'correct': False,\n", + " 'fmri_tr_time': None,\n", + " 'log_num': 0}]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from smile.log import log2dl\n", + "dl = log2dl('log_MD_0.slog')\n", + "dl[:2]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Pandas\n", + "\n", + "- Library at the core of most data science with Python\n", + "- Provides two key data structures: `Series` and `DataFrame`\n", + "- The key feature of Pandas is that ***data alignment is intrinsic***. \n", + " - The link between labels and data will not be broken unless done so explicitly by you.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Series\n", + "\n", + "- A `Series` is a one-dimensional labeled array capable of holding any data type:\n", + " - integers, strings, floating point numbers, Python objects, etc...\n", + "- The axis labels are collectively referred to as the index. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 -0.023272\n", + "1 -0.921768\n", + "2 -2.679155\n", + "3 -0.823877\n", + "4 -2.134610\n", + "dtype: float64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "s = pd.Series(np.random.randn(5))\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 1.482528\n", + "b -0.377511\n", + "c 1.875236\n", + "d 1.089316\n", + "e -0.978238\n", + "dtype: float64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you can specify the index\n", + "s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Series are ndarray-like\n", + "\n", + "- You can slice a series and it will also slice your index\n", + "- And many of the same methods are available (e.g., mean, sum, etc...)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "c 1.875236\n", + "d 1.089316\n", + "e -0.978238\n", + "dtype: float64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[2:]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6182659372161396" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 1.482528\n", + "c 1.875236\n", + "d 1.089316\n", + "dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[s > s.mean()]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Series is also dict-like\n", + "\n", + "- A Series is like a fixed-size dict in that you can get and set values by index label" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0893155918148905" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s['d']" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'d' in s" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Series keeps array operations aligned\n", + "\n", + "- Series can also be passed into most NumPy methods expecting an ndarray.\n", + "- Alignment will be maintained" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 2.965055\n", + "b -0.755022\n", + "c 3.750472\n", + "d 2.178631\n", + "e -1.956477\n", + "dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s+s" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 4.404063\n", + "b 0.685566\n", + "c 6.522357\n", + "d 2.972239\n", + "e 0.375973\n", + "dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.exp(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a NaN\n", + "b -0.755022\n", + "c 3.750472\n", + "d 2.178631\n", + "e NaN\n", + "dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[1:] + s[:-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 -16\n", + "2 16\n", + "3 3\n", + "4 16\n", + "dtype: int64" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = pd.Series(np.random.randn(5)*10)\n", + "x.astype(np.int)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## DataFrame\n", + "\n", + "- `DataFrame` is a 2-dimensional labeled data structure with columns of potentially different types. \n", + "- You can think of it like a spreadsheet or SQL table, or a dict of `Series` objects.\n", + "- It's possible to create a DataFrame a lot of different ways." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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onetwo
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" + ], + "text/plain": [ + " one two\n", + "a 1.0 4.0\n", + "b 2.0 3.0\n", + "c 3.0 2.0\n", + "d 4.0 1.0" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# e.g., from a dictionary\n", + "d = {'one': [1., 2., 3., 4.],\n", + " 'two': [4., 3., 2., 1.]}\n", + "df = pd.DataFrame(d, index=['a', 'b', 'c', 'd'])\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Column selection, addition, deletion\n", + "\n", + "- You can treat a DataFrame like a dict of Series objects" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 1.0\n", + "b 2.0\n", + "c 3.0\n", + "d 4.0\n", + "Name: one, dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pick a column\n", + "df['one']" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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onetwothreethresh
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c3.02.05.0True
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" + ], + "text/plain": [ + " one two three thresh\n", + "a 1.0 4.0 5.0 False\n", + "b 2.0 3.0 5.0 False\n", + "c 3.0 2.0 5.0 True\n", + "d 4.0 1.0 5.0 True" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# make new columns\n", + "df['three'] = df['one'] + df['two']\n", + "df['thresh'] = df['one'] > 2.0\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "one float64\n", + "two float64\n", + "three float64\n", + "thresh bool\n", + "dtype: object" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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onethresh
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" + ], + "text/plain": [ + " one thresh\n", + "a 1.0 False\n", + "b 2.0 False\n", + "c 3.0 True\n", + "d 4.0 True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you can delete with del or pop\n", + "del df['two']\n", + "df.pop('three')\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
onethreshfoo
a1.0Falsebar
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c3.0Truebar
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" + ], + "text/plain": [ + " one thresh foo\n", + "a 1.0 False bar\n", + "b 2.0 False bar\n", + "c 3.0 True bar\n", + "d 4.0 True bar" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# new values will populate the entire column\n", + "df['foo'] = 'bar'\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Indexing and Selection\n", + "\n", + " - loc\n", + " - iloc\n", + " - boolean\n", + " - column\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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onetwothreethresh
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c3.02.05.0True
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\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
onethree
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" + ], + "text/plain": [ + " one three\n", + "a 1.0 5.0\n", + "b 2.0 5.0\n", + "c 3.0 5.0\n", + "d 4.0 5.0" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pick columns of interest\n", + "df[['one', 'three']]" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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onetwothreethresh
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" + ], + "text/plain": [ + " one two three thresh\n", + "b 2.0 3.0 5.0 False\n", + "c 3.0 2.0 5.0 True" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pick rows by index\n", + "df.loc[['b', 'c']]" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " one two three thresh\n", + "b 2.0 3.0 5.0 False\n", + "d 4.0 1.0 5.0 True" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pick rows by numeric index\n", + "df.iloc[[1, 3]]" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "# pick rows by boolean index\n", + "df2 = df[(df['two']>2) & (df['one']<=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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twothree
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" + ], + "text/plain": [ + " two three\n", + "a 4.0 5.0" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pick rows by boolean index\n", + "df3 = df.loc[(df['two']>2) & (df['one']<=1), ['two', 'three']]\n", + "df3" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## SMILE and Pandas\n", + "\n", + "- We can create a DataFrame from a dict list in SMILE:" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rtdisappear_time_errordisappear_time_timerun_numrefresh_rateappear_time_errorappear_time_timeeeg_pulse_timeright_coherenceleft_coherencepress_time_errorpress_time_timeincorrect_resppressedlog_timecorrect_respcorrectfmri_tr_timelog_num
02.9416150.0617.657840026.7314680.0614.707281None0.060.000.000510617.64889611618.1488964FalseNone0
11.5730700.0620.250024026.5903540.0618.666397None0.240.300.000475620.23946744620.7394671FalseNone0
22.8109890.0624.100785027.4897380.0621.275211None0.000.000.000544624.08620014624.5862004TrueNone0
30.8724960.0625.751111027.2550890.0624.850929None0.000.240.001483625.72342541626.2234251TrueNone0
41.8188310.0628.618369026.4893660.0626.784654None0.000.120.000755628.60348541629.1034851TrueNone0
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" + ], + "text/plain": [ + " rt disappear_time_error disappear_time_time run_num refresh_rate \\\n", + "0 2.941615 0.0 617.657840 0 26.731468 \n", + "1 1.573070 0.0 620.250024 0 26.590354 \n", + "2 2.810989 0.0 624.100785 0 27.489738 \n", + "3 0.872496 0.0 625.751111 0 27.255089 \n", + "4 1.818831 0.0 628.618369 0 26.489366 \n", + "\n", + " appear_time_error appear_time_time eeg_pulse_time right_coherence \\\n", + "0 0.0 614.707281 None 0.06 \n", + "1 0.0 618.666397 None 0.24 \n", + "2 0.0 621.275211 None 0.00 \n", + "3 0.0 624.850929 None 0.00 \n", + "4 0.0 626.784654 None 0.00 \n", + "\n", + " left_coherence press_time_error press_time_time incorrect_resp pressed \\\n", + "0 0.00 0.000510 617.648896 1 1 \n", + "1 0.30 0.000475 620.239467 4 4 \n", + "2 0.00 0.000544 624.086200 1 4 \n", + "3 0.24 0.001483 625.723425 4 1 \n", + "4 0.12 0.000755 628.603485 4 1 \n", + "\n", + " log_time correct_resp correct fmri_tr_time log_num \n", + "0 618.148896 4 False None 0 \n", + "1 620.739467 1 False None 0 \n", + "2 624.586200 4 True None 0 \n", + "3 626.223425 1 True None 0 \n", + "4 629.103485 1 True None 0 " + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dl = log2dl('log_MD_0.slog')\n", + "df = pd.DataFrame(dl)\n", + "df.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# A quick summary\n", + "\n", + "- You can use the `describe` method to get a quick summary of your data frame" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rtdisappear_time_errordisappear_time_timerun_numrefresh_rateappear_time_errorappear_time_timeright_coherenceleft_coherencepress_time_errorpress_time_timelog_timelog_num
count248.000000248.0248.000000248.000000248.000000248.0248.000000248.000000248.000000248.000000248.000000248.000000248.0
mean0.8045880.01481.1269651.50000026.6675930.01480.3072700.1500000.1500000.0012041481.1118581481.6118580.0
std0.3838540.0601.1879321.1202950.8542390.0601.2335620.1045520.1045520.005860601.187790601.1877900.0
min0.2527370.0617.6578400.00000021.0592050.0614.7072810.0000000.0000000.000430617.648896618.1488960.0
25%0.5588390.01008.3402820.75000026.3976690.01007.3213250.0600000.0600000.0004971008.3281161008.8281160.0
50%0.6785020.01532.0496361.50000026.7177800.01530.6282670.1500000.1500000.0005191532.0335801532.5335800.0
75%0.9268570.02005.1566512.25000027.1433140.02004.5586310.2400000.2400000.0005472005.1430652005.6430650.0
max2.9416150.02243.1418043.00000028.6090710.02241.9832370.3000000.3000000.0692832243.1306642243.6306640.0
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" + ], + "text/plain": [ + " rt disappear_time_error disappear_time_time run_num \\\n", + "count 248.000000 248.0 248.000000 248.000000 \n", + "mean 0.804588 0.0 1481.126965 1.500000 \n", + "std 0.383854 0.0 601.187932 1.120295 \n", + "min 0.252737 0.0 617.657840 0.000000 \n", + "25% 0.558839 0.0 1008.340282 0.750000 \n", + "50% 0.678502 0.0 1532.049636 1.500000 \n", + "75% 0.926857 0.0 2005.156651 2.250000 \n", + "max 2.941615 0.0 2243.141804 3.000000 \n", + "\n", + " refresh_rate appear_time_error appear_time_time right_coherence \\\n", + "count 248.000000 248.0 248.000000 248.000000 \n", + "mean 26.667593 0.0 1480.307270 0.150000 \n", + "std 0.854239 0.0 601.233562 0.104552 \n", + "min 21.059205 0.0 614.707281 0.000000 \n", + "25% 26.397669 0.0 1007.321325 0.060000 \n", + "50% 26.717780 0.0 1530.628267 0.150000 \n", + "75% 27.143314 0.0 2004.558631 0.240000 \n", + "max 28.609071 0.0 2241.983237 0.300000 \n", + "\n", + " left_coherence press_time_error press_time_time log_time log_num \n", + "count 248.000000 248.000000 248.000000 248.000000 248.0 \n", + "mean 0.150000 0.001204 1481.111858 1481.611858 0.0 \n", + "std 0.104552 0.005860 601.187790 601.187790 0.0 \n", + "min 0.000000 0.000430 617.648896 618.148896 0.0 \n", + "25% 0.060000 0.000497 1008.328116 1008.828116 0.0 \n", + "50% 0.150000 0.000519 1532.033580 1532.533580 0.0 \n", + "75% 0.240000 0.000547 2005.143065 2005.643065 0.0 \n", + "max 0.300000 0.069283 2243.130664 2243.630664 0.0 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Some data clean-up" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['rt', 'disappear_time_error', 'disappear_time_time', 'run_num',\n", + " 'refresh_rate', 'appear_time_error', 'appear_time_time',\n", + " 'eeg_pulse_time', 'right_coherence', 'left_coherence',\n", + " 'press_time_error', 'press_time_time', 'incorrect_resp', 'pressed',\n", + " 'log_time', 'correct_resp', 'correct', 'fmri_tr_time', 'log_num',\n", + " 'coh_diff', 'log_rt'],\n", + " dtype='object')" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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run_numtrial_numrtlog_rtcoh_diffright_coherenceleft_coherenceappear_time_timepress_time_errorpress_time_timeincorrect_resppressedcorrect_respcorrect
0002.9416151.0789590.060.060.00614.7072810.000510617.648896114False
1011.5730700.4530290.060.240.30618.6663970.000475620.239467441False
2022.8109891.0335370.000.000.00621.2752110.000544624.086200144True
3030.872496-0.1363970.240.000.24624.8509290.001483625.723425411True
4041.8188310.5981940.120.000.12626.7846540.000755628.603485411True
\n", + "
" + ], + "text/plain": [ + " run_num trial_num rt log_rt coh_diff right_coherence \\\n", + "0 0 0 2.941615 1.078959 0.06 0.06 \n", + "1 0 1 1.573070 0.453029 0.06 0.24 \n", + "2 0 2 2.810989 1.033537 0.00 0.00 \n", + "3 0 3 0.872496 -0.136397 0.24 0.00 \n", + "4 0 4 1.818831 0.598194 0.12 0.00 \n", + "\n", + " left_coherence appear_time_time press_time_error press_time_time \\\n", + "0 0.00 614.707281 0.000510 617.648896 \n", + "1 0.30 618.666397 0.000475 620.239467 \n", + "2 0.00 621.275211 0.000544 624.086200 \n", + "3 0.24 624.850929 0.001483 625.723425 \n", + "4 0.12 626.784654 0.000755 628.603485 \n", + "\n", + " incorrect_resp pressed correct_resp correct \n", + "0 1 1 4 False \n", + "1 4 4 1 False \n", + "2 1 4 4 True \n", + "3 4 1 1 True \n", + "4 4 1 1 True " + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's add a new column for the absolute value of the coherence difference\n", + "df['coh_diff'] = np.abs(df['right_coherence'] - df['left_coherence'])\n", + "\n", + "# and make a log rt\n", + "df['log_rt'] = np.log(df['rt'])\n", + "\n", + "df['trial_num'] = np.arange(len(df))\n", + "\n", + "df = df[['run_num', 'trial_num', 'rt', 'log_rt', 'coh_diff', \n", + " 'right_coherence', 'left_coherence', 'appear_time_time',\n", + " 'press_time_error', 'press_time_time', 'incorrect_resp', 'pressed',\n", + " 'correct_resp', 'correct']]\n", + "\n", + "# show it\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Grouping data\n", + "\n", + "- The `groupby` method allows you to create different groupings of your data" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['run_num', 'trial_num', 'rt', 'log_rt', 'coh_diff', 'right_coherence',\n", + " 'left_coherence', 'appear_time_time', 'press_time_error',\n", + " 'press_time_time', 'incorrect_resp', 'pressed', 'correct_resp',\n", + " 'correct'],\n", + " dtype='object')" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "coh_diff\n", + "0.00 48\n", + "0.06 40\n", + "0.12 64\n", + "0.18 48\n", + "0.24 32\n", + "0.30 16\n", + "Name: correct, dtype: int64" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('coh_diff')['correct'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0 0.6041666666666666\n", + "0.06 0.75\n", + "0.12 0.78125\n", + "0.18 0.9583333333333334\n", + "0.24 0.90625\n", + "0.3 1.0\n" + ] + } + ], + "source": [ + "ucd = df['coh_diff'].unique()\n", + "ucd.sort()\n", + "ucd\n", + "for cd in ucd:\n", + " print(cd, df.loc[df['coh_diff']==cd, 'correct'].mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# let's look at performance as a function of coh_diff\n", + "df.groupby('coh_diff')['correct'].mean().plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# how about rts\n", + "df.groupby('coh_diff')['rt'].mean().plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "correct coh_diff\n", + "False 0.00 0.829746\n", + " 0.06 1.036445\n", + " 0.12 0.783893\n", + " 0.18 0.884447\n", + " 0.24 0.834226\n", + "True 0.00 0.949310\n", + " 0.06 0.861958\n", + " 0.12 0.849955\n", + " 0.18 0.682256\n", + " 0.24 0.701371\n", + " 0.30 0.659511\n", + "Name: rt, dtype: float64" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you can group by more than one column\n", + "df.groupby(['correct', 'coh_diff'])['rt'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "correct\n", + "False 0.861994\n", + "True 0.790811\n", + "Name: rt, dtype: float64" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you can group by more than one column\n", + "df.groupby(['correct'])['rt'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "correct_resp\n", + "1 -0.323040\n", + "4 -0.277191\n", + "Name: log_rt, dtype: float64" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# is there a speed bias?\n", + "df.groupby(['correct_resp'])['log_rt'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "correct_resp\n", + "1 0.879032\n", + "4 0.733871\n", + "Name: correct, dtype: float64" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# is there an accuracy bias?\n", + "df.groupby(['correct_resp'])['correct'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment before next class\n", + "\n", + "- We will post experiments for you to run, along with instructions for how to upload the data\n", + "- This will be due by ***Wednesday*** next week, so that we can prepare the data for class\n", + "\n", + "### See you next week!!!" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lessons/09_Data_Processing-Copy1 2.ipynb b/lessons/09_Data_Processing-Copy1 2.ipynb new file mode 100644 index 0000000..e2105f9 --- /dev/null +++ b/lessons/09_Data_Processing-Copy1 2.ipynb @@ -0,0 +1,1910 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Data Processing\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020\n", + "By: Per B. Sederberg, PhD\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Lesson Objectives\n", + "\n", + "Upon completion of this lesson, students should have learned:\n", + "\n", + "1. Read data from slog files\n", + "2. The Series and DataFrame data structures in Pandas\n", + "3. Load slogs as a DataFrame\n", + "4. Some basic operations on the data\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Updating SMILE\n", + "\n", + "- First you can test whether there is a new version Kivy, which is the primary dependency of SMILE:\n", + "\n", + "```bash\n", + "conda install -c conda-forge kivy==1.11.1\n", + "```\n", + "\n", + "- Then you can update SMILE right from the GitHub repository (note the upgrade option at the end):\n", + "\n", + "```bash\n", + "pip install git+https://github.com/compmem/smile --upgrade\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Math Distract\n", + "\n", + "- Sometimes you want to have a delay period, e.g., between study and test\n", + "- Although it may be fine to have an empty delay, often you'd like to fill it with a task that prevents rehearsal of the studied items\n", + "- We provide a subroutine that generates math problems!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from smile.common import *\n", + "from smile.math_distract import MathDistract\n", + "\n", + "exp = Experiment(show_splash=False, resolution=(1024,768))\n", + "\n", + "Wait(1.0)\n", + "MathDistract(num_vars=2,\n", + " min_num=1,\n", + " max_num=9,\n", + " max_probs=50,\n", + " duration=20)\n", + " \n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Moving Dot stimuli\n", + "\n", + "- A class of stimuli at the core of many studies of perceptual decision-making\n", + "- We provide a custom state for it:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO ] [Logger ] Record log in /Users/uva/.kivy/logs/kivy_20-10-22_1.txt\n", + "[INFO ] [Kivy ] v1.11.1\n", + "[INFO ] [Kivy ] Installed at \"/Users/uva/opt/anaconda3/envs/compsy/lib/python3.7/site-packages/kivy/__init__.py\"\n", + "[INFO ] [Python ] v3.7.7 (default, May 6 2020, 04:59:01) \n", + "[Clang 4.0.1 (tags/RELEASE_401/final)]\n", + "[INFO ] [Python ] Interpreter at \"/Users/uva/opt/anaconda3/envs/compsy/bin/python\"\n", + "[INFO ] [Factory ] 184 symbols loaded\n", + "[INFO ] [Image ] Providers: img_tex, img_imageio, img_dds, img_sdl2, img_pil, img_gif (img_ffpyplayer ignored)\n", + "[INFO ] [Text ] Provider: sdl2\n", + "[INFO ] [Camera ] Provider: avfoundation\n", + "[INFO ] [VideoGstplayer] Using Gstreamer 1.14.5.0\n", + "[INFO ] [Video ] Provider: gstplayer\n", + "[WARNING] [SMILE ] Unable to import PYO!\n", + "[WARNING] [SMILE ] Durations will be maintained, unless none are specified\n", + "[INFO ] [Window ] Provider: sdl2\n", + "[INFO ] [GL ] Using the \"OpenGL ES 2\" graphics system\n", + "[INFO ] [GL ] Backend used \n", + "[INFO ] [GL ] OpenGL version \n", + "[INFO ] [GL ] OpenGL vendor \n", + "[INFO ] [GL ] OpenGL renderer \n", + "[INFO ] [GL ] OpenGL parsed version: 2, 1\n", + "[INFO ] [GL ] Shading version \n", + "[INFO ] [GL ] Texture max size <16384>\n", + "[INFO ] [GL ] Texture max units <16>\n", + "[INFO ] [Window ] auto add sdl2 input provider\n", + "[INFO ] [Window ] virtual keyboard not allowed, single mode, not docked\n", + "[INFO ] [Base ] Start application main loop\n", + "[INFO ] [GL ] NPOT texture support is available\n", + "[INFO ] [WindowSDL ] exiting mainloop and closing.\n", + "[INFO ] [Base ] Leaving application in progress...\n" + ] + } + ], + "source": [ + "from smile.common import *\n", + "from smile.moving_dots import MovingDots\n", + "\n", + "exp = Experiment(show_splash=False, resolution=(1024,768))\n", + "\n", + "# set up some config\n", + "dot_speed = 180\n", + "\n", + "# set initial values\n", + "exp.cr=0.2\n", + "exp.cl=0.2\n", + "motion_props = [{\"coherence\": exp.cr, \"direction\": 0, \"direction_variance\": 0},\n", + " {\"coherence\": exp.cl, \"direction\": 180, \"direction_variance\": 0}]\n", + "with Loop():\n", + " with Parallel():\n", + " dots = MovingDots(color='white', scale=3, num_dots=100, radius=200,\n", + " motion_props=motion_props, speed=dot_speed,\n", + " lifespan=0.5, lifespan_variance=1.5)\n", + " lr = Label(text='Right Coherence:\\n'+Ref(str,exp.cr), left=dots.right+40, font_size=28) \n", + " ll = Label(text='Left Coherence:\\n'+Ref(str,exp.cl), right=dots.left-40, font_size=28)\n", + "\n", + " with UntilDone():\n", + " kp = KeyPress(keys=['UP','DOWN','LEFT','RIGHT'])\n", + " with If(kp.pressed=='UP'):\n", + " exp.cr=exp.cr+0.05\n", + " exp.cl=exp.cl+0.05\n", + " with If(exp.cr+exp.cl>1.0):\n", + " exp.cr=exp.cr-0.05\n", + " exp.cl=exp.cl-0.05\n", + " with Elif((kp.pressed=='DOWN')):\n", + " exp.cr=exp.cr-0.05\n", + " exp.cl=exp.cl-0.05\n", + " with If(exp.cr<0.05):\n", + " exp.cr=0.0\n", + " with If(exp.cl<0.05):\n", + " exp.cl=0.0\n", + " with Elif(kp.pressed=='LEFT'):\n", + " exp.cl=exp.cl+0.05\n", + " with If(exp.cr+exp.cl>1.0):\n", + " exp.cl=exp.cl-0.05\n", + " with Elif(kp.pressed=='RIGHT'):\n", + " exp.cr=exp.cr+0.05\n", + " with If(exp.cr+exp.cl>1.0):\n", + " exp.cr=exp.cr-0.05\n", + " # update the motion props\n", + " dots.update(motion_props=[{\"coherence\": exp.cr, \"direction\": 0},\n", + " {\"coherence\": exp.cl, \"direction\": 180}])\n", + " lr.update(text='Right Coherence:\\n'+Ref(str,exp.cr))\n", + " ll.update(text='Left Coherence:\\n'+Ref(str,exp.cl))\n", + "with UntilDone():\n", + " KeyPress(keys=['ENTER'])\n", + "\n", + " \n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Reading in slog files\n", + "\n", + "- SMILE stores data in log files with the `.slog` file extension\n", + "- It is a custom format that are pickled and compressed dictionaries\n", + "- We can read them in with a SMILE function `log2dl` that converts the log to a list of dictionaries (i.e., a dict list):" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'rt': 2.941615221919392,\n", + " 'disappear_time_error': 0.0,\n", + " 'disappear_time_time': 617.657840390051,\n", + " 'run_num': 0,\n", + " 'refresh_rate': 26.731467774154293,\n", + " 'appear_time_error': 0.0,\n", + " 'appear_time_time': 614.7072808193332,\n", + " 'eeg_pulse_time': None,\n", + " 'right_coherence': 0.06,\n", + " 'left_coherence': 0.0,\n", + " 'press_time_error': 0.0005104763318968253,\n", + " 'press_time_time': 617.6488960412526,\n", + " 'incorrect_resp': '1',\n", + " 'pressed': '1',\n", + " 'log_time': 618.1488960412526,\n", + " 'correct_resp': '4',\n", + " 'correct': False,\n", + " 'fmri_tr_time': None,\n", + " 'log_num': 0},\n", + " {'rt': 1.5730700115865375,\n", + " 'disappear_time_error': 0.0,\n", + " 'disappear_time_time': 620.2500236883183,\n", + " 'run_num': 0,\n", + " 'refresh_rate': 26.59035375261749,\n", + " 'appear_time_error': 0.0,\n", + " 'appear_time_time': 618.6663967214259,\n", + " 'eeg_pulse_time': None,\n", + " 'right_coherence': 0.24,\n", + " 'left_coherence': 0.3,\n", + " 'press_time_error': 0.00047487459045214564,\n", + " 'press_time_time': 620.2394667330125,\n", + " 'incorrect_resp': '4',\n", + " 'pressed': '4',\n", + " 'log_time': 620.7394667330125,\n", + " 'correct_resp': '1',\n", + " 'correct': False,\n", + " 'fmri_tr_time': None,\n", + " 'log_num': 0}]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from smile.log import log2dl\n", + "dl = log2dl('log_MD_0.slog')\n", + "dl[:2]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Pandas\n", + "\n", + "- Library at the core of most data science with Python\n", + "- Provides two key data structures: `Series` and `DataFrame`\n", + "- The key feature of Pandas is that ***data alignment is intrinsic***. \n", + " - The link between labels and data will not be broken unless done so explicitly by you.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Series\n", + "\n", + "- A `Series` is a one-dimensional labeled array capable of holding any data type:\n", + " - integers, strings, floating point numbers, Python objects, etc...\n", + "- The axis labels are collectively referred to as the index. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 -0.023272\n", + "1 -0.921768\n", + "2 -2.679155\n", + "3 -0.823877\n", + "4 -2.134610\n", + "dtype: float64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "s = pd.Series(np.random.randn(5))\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 1.482528\n", + "b -0.377511\n", + "c 1.875236\n", + "d 1.089316\n", + "e -0.978238\n", + "dtype: float64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you can specify the index\n", + "s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Series are ndarray-like\n", + "\n", + "- You can slice a series and it will also slice your index\n", + "- And many of the same methods are available (e.g., mean, sum, etc...)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "c 1.875236\n", + "d 1.089316\n", + "e -0.978238\n", + "dtype: float64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[2:]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 1.482528\n", + "c 1.875236\n", + "d 1.089316\n", + "dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[s > s.mean()]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Series is also dict-like\n", + "\n", + "- A Series is like a fixed-size dict in that you can get and set values by index label" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0893155918148905" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s['d']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'d' in s" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Series keeps array operations aligned\n", + "\n", + "- Series can also be passed into most NumPy methods expecting an ndarray.\n", + "- Alignment will be maintained" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 2.965055\n", + "b -0.755022\n", + "c 3.750472\n", + "d 2.178631\n", + "e -1.956477\n", + "dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s+s" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 4.404063\n", + "b 0.685566\n", + "c 6.522357\n", + "d 2.972239\n", + "e 0.375973\n", + "dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.exp(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a NaN\n", + "b -0.755022\n", + "c 3.750472\n", + "d 2.178631\n", + "e NaN\n", + "dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[1:] + s[:-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## DataFrame\n", + "\n", + "- `DataFrame` is a 2-dimensional labeled data structure with columns of potentially different types. \n", + "- You can think of it like a spreadsheet or SQL table, or a dict of `Series` objects.\n", + "- It's possible to create a DataFrame a lot of different ways." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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onetwo
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" + ], + "text/plain": [ + " one two\n", + "a 1.0 4.0\n", + "b 2.0 3.0\n", + "c 3.0 2.0\n", + "d 4.0 1.0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# e.g., from a dictionary\n", + "d = {'one': [1., 2., 3., 4.],\n", + " 'two': [4., 3., 2., 1.]}\n", + "df = pd.DataFrame(d, index=['a', 'b', 'c', 'd'])\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Column selection, addition, deletion\n", + "\n", + "- You can treat a DataFrame like a dict of Series objects" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 1.0\n", + "b 2.0\n", + "c 3.0\n", + "d 4.0\n", + "Name: one, dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pick a column\n", + "df['one']" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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onetwothreethresh
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" + ], + "text/plain": [ + " one two three thresh\n", + "a 1.0 4.0 5.0 False\n", + "b 2.0 3.0 5.0 False\n", + "c 3.0 2.0 5.0 True\n", + "d 4.0 1.0 5.0 True" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# make new columns\n", + "df['three'] = df['one'] + df['two']\n", + "df['thresh'] = df['one'] > 2.0\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " one thresh\n", + "a 1.0 False\n", + "b 2.0 False\n", + "c 3.0 True\n", + "d 4.0 True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you can delete with del or pop\n", + "del df['two']\n", + "df.pop('three')\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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onethreshfoo
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" + ], + "text/plain": [ + " one thresh foo\n", + "a 1.0 False bar\n", + "b 2.0 False bar\n", + "c 3.0 True bar\n", + "d 4.0 True bar" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# new values will populate the entire column\n", + "df['foo'] = 'bar'\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Indexing and Selection\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " one thresh foo\n", + "c 3.0 True bar\n", + "d 4.0 True bar" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[df['one']>2]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## SMILE and Pandas\n", + "\n", + "- We can create a DataFrame from a dict list in SMILE:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " rt disappear_time_error disappear_time_time run_num refresh_rate \\\n", + "0 2.941615 0.0 617.657840 0 26.731468 \n", + "1 1.573070 0.0 620.250024 0 26.590354 \n", + "2 2.810989 0.0 624.100785 0 27.489738 \n", + "3 0.872496 0.0 625.751111 0 27.255089 \n", + "4 1.818831 0.0 628.618369 0 26.489366 \n", + "\n", + " appear_time_error appear_time_time eeg_pulse_time right_coherence \\\n", + "0 0.0 614.707281 None 0.06 \n", + "1 0.0 618.666397 None 0.24 \n", + "2 0.0 621.275211 None 0.00 \n", + "3 0.0 624.850929 None 0.00 \n", + "4 0.0 626.784654 None 0.00 \n", + "\n", + " left_coherence press_time_error press_time_time incorrect_resp pressed \\\n", + "0 0.00 0.000510 617.648896 1 1 \n", + "1 0.30 0.000475 620.239467 4 4 \n", + "2 0.00 0.000544 624.086200 1 4 \n", + "3 0.24 0.001483 625.723425 4 1 \n", + "4 0.12 0.000755 628.603485 4 1 \n", + "\n", + " log_time correct_resp correct fmri_tr_time log_num \n", + "0 618.148896 4 False None 0 \n", + "1 620.739467 1 False None 0 \n", + "2 624.586200 4 True None 0 \n", + "3 626.223425 1 True None 0 \n", + "4 629.103485 1 True None 0 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dl = log2dl('log_MD_0.slog')\n", + "df = pd.DataFrame(dl)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# A quick summary\n", + "\n", + "- You can use the `describe` method to get a quick summary of your data frame" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rtdisappear_time_errordisappear_time_timerun_numrefresh_rateappear_time_errorappear_time_timeright_coherenceleft_coherencepress_time_errorpress_time_timelog_timelog_num
count248.000000248.0248.000000248.000000248.000000248.0248.000000248.000000248.000000248.000000248.000000248.000000248.0
mean0.8045880.01481.1269651.50000026.6675930.01480.3072700.1500000.1500000.0012041481.1118581481.6118580.0
std0.3838540.0601.1879321.1202950.8542390.0601.2335620.1045520.1045520.005860601.187790601.1877900.0
min0.2527370.0617.6578400.00000021.0592050.0614.7072810.0000000.0000000.000430617.648896618.1488960.0
25%0.5588390.01008.3402820.75000026.3976690.01007.3213250.0600000.0600000.0004971008.3281161008.8281160.0
50%0.6785020.01532.0496361.50000026.7177800.01530.6282670.1500000.1500000.0005191532.0335801532.5335800.0
75%0.9268570.02005.1566512.25000027.1433140.02004.5586310.2400000.2400000.0005472005.1430652005.6430650.0
max2.9416150.02243.1418043.00000028.6090710.02241.9832370.3000000.3000000.0692832243.1306642243.6306640.0
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" + ], + "text/plain": [ + " rt disappear_time_error disappear_time_time run_num \\\n", + "count 248.000000 248.0 248.000000 248.000000 \n", + "mean 0.804588 0.0 1481.126965 1.500000 \n", + "std 0.383854 0.0 601.187932 1.120295 \n", + "min 0.252737 0.0 617.657840 0.000000 \n", + "25% 0.558839 0.0 1008.340282 0.750000 \n", + "50% 0.678502 0.0 1532.049636 1.500000 \n", + "75% 0.926857 0.0 2005.156651 2.250000 \n", + "max 2.941615 0.0 2243.141804 3.000000 \n", + "\n", + " refresh_rate appear_time_error appear_time_time right_coherence \\\n", + "count 248.000000 248.0 248.000000 248.000000 \n", + "mean 26.667593 0.0 1480.307270 0.150000 \n", + "std 0.854239 0.0 601.233562 0.104552 \n", + "min 21.059205 0.0 614.707281 0.000000 \n", + "25% 26.397669 0.0 1007.321325 0.060000 \n", + "50% 26.717780 0.0 1530.628267 0.150000 \n", + "75% 27.143314 0.0 2004.558631 0.240000 \n", + "max 28.609071 0.0 2241.983237 0.300000 \n", + "\n", + " left_coherence press_time_error press_time_time log_time log_num \n", + "count 248.000000 248.000000 248.000000 248.000000 248.0 \n", + "mean 0.150000 0.001204 1481.111858 1481.611858 0.0 \n", + "std 0.104552 0.005860 601.187790 601.187790 0.0 \n", + "min 0.000000 0.000430 617.648896 618.148896 0.0 \n", + "25% 0.060000 0.000497 1008.328116 1008.828116 0.0 \n", + "50% 0.150000 0.000519 1532.033580 1532.533580 0.0 \n", + "75% 0.240000 0.000547 2005.143065 2005.643065 0.0 \n", + "max 0.300000 0.069283 2243.130664 2243.630664 0.0 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Some data clean-up" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rtdisappear_time_errordisappear_time_timerun_numrefresh_rateappear_time_errorappear_time_timeeeg_pulse_timeright_coherenceleft_coherence...press_time_timeincorrect_resppressedlog_timecorrect_respcorrectfmri_tr_timelog_numcoh_difflog_rt
02.9416150.0617.657840026.7314680.0614.707281None0.060.00...617.64889611618.1488964FalseNone00.061.078959
11.5730700.0620.250024026.5903540.0618.666397None0.240.30...620.23946744620.7394671FalseNone00.060.453029
22.8109890.0624.100785027.4897380.0621.275211None0.000.00...624.08620014624.5862004TrueNone00.001.033537
30.8724960.0625.751111027.2550890.0624.850929None0.000.24...625.72342541626.2234251TrueNone00.24-0.136397
41.8188310.0628.618369026.4893660.0626.784654None0.000.12...628.60348541629.1034851TrueNone00.120.598194
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5 rows × 21 columns

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" + ], + "text/plain": [ + " rt disappear_time_error disappear_time_time run_num refresh_rate \\\n", + "0 2.941615 0.0 617.657840 0 26.731468 \n", + "1 1.573070 0.0 620.250024 0 26.590354 \n", + "2 2.810989 0.0 624.100785 0 27.489738 \n", + "3 0.872496 0.0 625.751111 0 27.255089 \n", + "4 1.818831 0.0 628.618369 0 26.489366 \n", + "\n", + " appear_time_error appear_time_time eeg_pulse_time right_coherence \\\n", + "0 0.0 614.707281 None 0.06 \n", + "1 0.0 618.666397 None 0.24 \n", + "2 0.0 621.275211 None 0.00 \n", + "3 0.0 624.850929 None 0.00 \n", + "4 0.0 626.784654 None 0.00 \n", + "\n", + " left_coherence ... press_time_time incorrect_resp pressed log_time \\\n", + "0 0.00 ... 617.648896 1 1 618.148896 \n", + "1 0.30 ... 620.239467 4 4 620.739467 \n", + "2 0.00 ... 624.086200 1 4 624.586200 \n", + "3 0.24 ... 625.723425 4 1 626.223425 \n", + "4 0.12 ... 628.603485 4 1 629.103485 \n", + "\n", + " correct_resp correct fmri_tr_time log_num coh_diff log_rt \n", + "0 4 False None 0 0.06 1.078959 \n", + "1 1 False None 0 0.06 0.453029 \n", + "2 4 True None 0 0.00 1.033537 \n", + "3 1 True None 0 0.24 -0.136397 \n", + "4 1 True None 0 0.12 0.598194 \n", + "\n", + "[5 rows x 21 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's add a new column\n", + "df['coh_diff'] = np.abs(df['right_coherence'] - df['left_coherence'])\n", + "\n", + "# and make a log rt\n", + "df['log_rt'] = np.log(df['rt'])\n", + "\n", + "# show it\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Grouping data\n", + "\n", + "- The `groupby` method allows you to create different groupings of your data" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['rt', 'disappear_time_error', 'disappear_time_time', 'run_num',\n", + " 'refresh_rate', 'appear_time_error', 'appear_time_time',\n", + " 'eeg_pulse_time', 'right_coherence', 'left_coherence',\n", + " 'press_time_error', 'press_time_time', 'incorrect_resp', 'pressed',\n", + " 'log_time', 'correct_resp', 'correct', 'fmri_tr_time', 'log_num',\n", + " 'coh_diff', 'log_rt'],\n", + " dtype='object')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "coh_diff\n", + "0.00 0.604167\n", + "0.06 0.750000\n", + "0.12 0.781250\n", + "0.18 0.958333\n", + "0.24 0.906250\n", + "0.30 1.000000\n", + "Name: correct, dtype: float64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's look at performance as a function of coh_diff\n", + "df.groupby('coh_diff')['correct'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "coh_diff\n", + "0.00 0.901983\n", + "0.06 0.905580\n", + "0.12 0.835504\n", + "0.18 0.690680\n", + "0.24 0.713827\n", + "0.30 0.659511\n", + "Name: rt, dtype: float64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# how about rts\n", + "df.groupby('coh_diff')['rt'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "correct coh_diff\n", + "False 0.00 0.829746\n", + " 0.06 1.036445\n", + " 0.12 0.783893\n", + " 0.18 0.884447\n", + " 0.24 0.834226\n", + "True 0.00 0.949310\n", + " 0.06 0.861958\n", + " 0.12 0.849955\n", + " 0.18 0.682256\n", + " 0.24 0.701371\n", + " 0.30 0.659511\n", + "Name: rt, dtype: float64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you can group by more than one column\n", + "df.groupby(['correct', 'coh_diff'])['rt'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "correct_resp\n", + "1 0.776073\n", + "4 0.833104\n", + "Name: rt, dtype: float64" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# is there a speed bias?\n", + "df.groupby(['correct_resp'])['rt'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pressed\n", + "1 0.767606\n", + "4 0.858491\n", + "Name: correct, dtype: float64" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# is there a accuracy bias?\n", + "df.groupby(['pressed'])['correct'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment before next class\n", + "\n", + "- We will post experiments for you to run, along with instructions for how to upload the data\n", + "- This will be due by ***Wednesday*** next week, so that we can prepare the data for class\n", + "\n", + "### See you next week!!!" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lessons/09_Data_Processing-Copy1.ipynb b/lessons/09_Data_Processing-Copy1.ipynb new file mode 100644 index 0000000..e2105f9 --- /dev/null +++ b/lessons/09_Data_Processing-Copy1.ipynb @@ -0,0 +1,1910 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Data Processing\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020\n", + "By: Per B. Sederberg, PhD\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Lesson Objectives\n", + "\n", + "Upon completion of this lesson, students should have learned:\n", + "\n", + "1. Read data from slog files\n", + "2. The Series and DataFrame data structures in Pandas\n", + "3. Load slogs as a DataFrame\n", + "4. Some basic operations on the data\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Updating SMILE\n", + "\n", + "- First you can test whether there is a new version Kivy, which is the primary dependency of SMILE:\n", + "\n", + "```bash\n", + "conda install -c conda-forge kivy==1.11.1\n", + "```\n", + "\n", + "- Then you can update SMILE right from the GitHub repository (note the upgrade option at the end):\n", + "\n", + "```bash\n", + "pip install git+https://github.com/compmem/smile --upgrade\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Math Distract\n", + "\n", + "- Sometimes you want to have a delay period, e.g., between study and test\n", + "- Although it may be fine to have an empty delay, often you'd like to fill it with a task that prevents rehearsal of the studied items\n", + "- We provide a subroutine that generates math problems!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from smile.common import *\n", + "from smile.math_distract import MathDistract\n", + "\n", + "exp = Experiment(show_splash=False, resolution=(1024,768))\n", + "\n", + "Wait(1.0)\n", + "MathDistract(num_vars=2,\n", + " min_num=1,\n", + " max_num=9,\n", + " max_probs=50,\n", + " duration=20)\n", + " \n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Moving Dot stimuli\n", + "\n", + "- A class of stimuli at the core of many studies of perceptual decision-making\n", + "- We provide a custom state for it:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO ] [Logger ] Record log in /Users/uva/.kivy/logs/kivy_20-10-22_1.txt\n", + "[INFO ] [Kivy ] v1.11.1\n", + "[INFO ] [Kivy ] Installed at \"/Users/uva/opt/anaconda3/envs/compsy/lib/python3.7/site-packages/kivy/__init__.py\"\n", + "[INFO ] [Python ] v3.7.7 (default, May 6 2020, 04:59:01) \n", + "[Clang 4.0.1 (tags/RELEASE_401/final)]\n", + "[INFO ] [Python ] Interpreter at \"/Users/uva/opt/anaconda3/envs/compsy/bin/python\"\n", + "[INFO ] [Factory ] 184 symbols loaded\n", + "[INFO ] [Image ] Providers: img_tex, img_imageio, img_dds, img_sdl2, img_pil, img_gif (img_ffpyplayer ignored)\n", + "[INFO ] [Text ] Provider: sdl2\n", + "[INFO ] [Camera ] Provider: avfoundation\n", + "[INFO ] [VideoGstplayer] Using Gstreamer 1.14.5.0\n", + "[INFO ] [Video ] Provider: gstplayer\n", + "[WARNING] [SMILE ] Unable to import PYO!\n", + "[WARNING] [SMILE ] Durations will be maintained, unless none are specified\n", + "[INFO ] [Window ] Provider: sdl2\n", + "[INFO ] [GL ] Using the \"OpenGL ES 2\" graphics system\n", + "[INFO ] [GL ] Backend used \n", + "[INFO ] [GL ] OpenGL version \n", + "[INFO ] [GL ] OpenGL vendor \n", + "[INFO ] [GL ] OpenGL renderer \n", + "[INFO ] [GL ] OpenGL parsed version: 2, 1\n", + "[INFO ] [GL ] Shading version \n", + "[INFO ] [GL ] Texture max size <16384>\n", + "[INFO ] [GL ] Texture max units <16>\n", + "[INFO ] [Window ] auto add sdl2 input provider\n", + "[INFO ] [Window ] virtual keyboard not allowed, single mode, not docked\n", + "[INFO ] [Base ] Start application main loop\n", + "[INFO ] [GL ] NPOT texture support is available\n", + "[INFO ] [WindowSDL ] exiting mainloop and closing.\n", + "[INFO ] [Base ] Leaving application in progress...\n" + ] + } + ], + "source": [ + "from smile.common import *\n", + "from smile.moving_dots import MovingDots\n", + "\n", + "exp = Experiment(show_splash=False, resolution=(1024,768))\n", + "\n", + "# set up some config\n", + "dot_speed = 180\n", + "\n", + "# set initial values\n", + "exp.cr=0.2\n", + "exp.cl=0.2\n", + "motion_props = [{\"coherence\": exp.cr, \"direction\": 0, \"direction_variance\": 0},\n", + " {\"coherence\": exp.cl, \"direction\": 180, \"direction_variance\": 0}]\n", + "with Loop():\n", + " with Parallel():\n", + " dots = MovingDots(color='white', scale=3, num_dots=100, radius=200,\n", + " motion_props=motion_props, speed=dot_speed,\n", + " lifespan=0.5, lifespan_variance=1.5)\n", + " lr = Label(text='Right Coherence:\\n'+Ref(str,exp.cr), left=dots.right+40, font_size=28) \n", + " ll = Label(text='Left Coherence:\\n'+Ref(str,exp.cl), right=dots.left-40, font_size=28)\n", + "\n", + " with UntilDone():\n", + " kp = KeyPress(keys=['UP','DOWN','LEFT','RIGHT'])\n", + " with If(kp.pressed=='UP'):\n", + " exp.cr=exp.cr+0.05\n", + " exp.cl=exp.cl+0.05\n", + " with If(exp.cr+exp.cl>1.0):\n", + " exp.cr=exp.cr-0.05\n", + " exp.cl=exp.cl-0.05\n", + " with Elif((kp.pressed=='DOWN')):\n", + " exp.cr=exp.cr-0.05\n", + " exp.cl=exp.cl-0.05\n", + " with If(exp.cr<0.05):\n", + " exp.cr=0.0\n", + " with If(exp.cl<0.05):\n", + " exp.cl=0.0\n", + " with Elif(kp.pressed=='LEFT'):\n", + " exp.cl=exp.cl+0.05\n", + " with If(exp.cr+exp.cl>1.0):\n", + " exp.cl=exp.cl-0.05\n", + " with Elif(kp.pressed=='RIGHT'):\n", + " exp.cr=exp.cr+0.05\n", + " with If(exp.cr+exp.cl>1.0):\n", + " exp.cr=exp.cr-0.05\n", + " # update the motion props\n", + " dots.update(motion_props=[{\"coherence\": exp.cr, \"direction\": 0},\n", + " {\"coherence\": exp.cl, \"direction\": 180}])\n", + " lr.update(text='Right Coherence:\\n'+Ref(str,exp.cr))\n", + " ll.update(text='Left Coherence:\\n'+Ref(str,exp.cl))\n", + "with UntilDone():\n", + " KeyPress(keys=['ENTER'])\n", + "\n", + " \n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Reading in slog files\n", + "\n", + "- SMILE stores data in log files with the `.slog` file extension\n", + "- It is a custom format that are pickled and compressed dictionaries\n", + "- We can read them in with a SMILE function `log2dl` that converts the log to a list of dictionaries (i.e., a dict list):" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'rt': 2.941615221919392,\n", + " 'disappear_time_error': 0.0,\n", + " 'disappear_time_time': 617.657840390051,\n", + " 'run_num': 0,\n", + " 'refresh_rate': 26.731467774154293,\n", + " 'appear_time_error': 0.0,\n", + " 'appear_time_time': 614.7072808193332,\n", + " 'eeg_pulse_time': None,\n", + " 'right_coherence': 0.06,\n", + " 'left_coherence': 0.0,\n", + " 'press_time_error': 0.0005104763318968253,\n", + " 'press_time_time': 617.6488960412526,\n", + " 'incorrect_resp': '1',\n", + " 'pressed': '1',\n", + " 'log_time': 618.1488960412526,\n", + " 'correct_resp': '4',\n", + " 'correct': False,\n", + " 'fmri_tr_time': None,\n", + " 'log_num': 0},\n", + " {'rt': 1.5730700115865375,\n", + " 'disappear_time_error': 0.0,\n", + " 'disappear_time_time': 620.2500236883183,\n", + " 'run_num': 0,\n", + " 'refresh_rate': 26.59035375261749,\n", + " 'appear_time_error': 0.0,\n", + " 'appear_time_time': 618.6663967214259,\n", + " 'eeg_pulse_time': None,\n", + " 'right_coherence': 0.24,\n", + " 'left_coherence': 0.3,\n", + " 'press_time_error': 0.00047487459045214564,\n", + " 'press_time_time': 620.2394667330125,\n", + " 'incorrect_resp': '4',\n", + " 'pressed': '4',\n", + " 'log_time': 620.7394667330125,\n", + " 'correct_resp': '1',\n", + " 'correct': False,\n", + " 'fmri_tr_time': None,\n", + " 'log_num': 0}]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from smile.log import log2dl\n", + "dl = log2dl('log_MD_0.slog')\n", + "dl[:2]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Pandas\n", + "\n", + "- Library at the core of most data science with Python\n", + "- Provides two key data structures: `Series` and `DataFrame`\n", + "- The key feature of Pandas is that ***data alignment is intrinsic***. \n", + " - The link between labels and data will not be broken unless done so explicitly by you.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Series\n", + "\n", + "- A `Series` is a one-dimensional labeled array capable of holding any data type:\n", + " - integers, strings, floating point numbers, Python objects, etc...\n", + "- The axis labels are collectively referred to as the index. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 -0.023272\n", + "1 -0.921768\n", + "2 -2.679155\n", + "3 -0.823877\n", + "4 -2.134610\n", + "dtype: float64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "s = pd.Series(np.random.randn(5))\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 1.482528\n", + "b -0.377511\n", + "c 1.875236\n", + "d 1.089316\n", + "e -0.978238\n", + "dtype: float64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you can specify the index\n", + "s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Series are ndarray-like\n", + "\n", + "- You can slice a series and it will also slice your index\n", + "- And many of the same methods are available (e.g., mean, sum, etc...)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "c 1.875236\n", + "d 1.089316\n", + "e -0.978238\n", + "dtype: float64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[2:]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 1.482528\n", + "c 1.875236\n", + "d 1.089316\n", + "dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[s > s.mean()]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Series is also dict-like\n", + "\n", + "- A Series is like a fixed-size dict in that you can get and set values by index label" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0893155918148905" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s['d']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'d' in s" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Series keeps array operations aligned\n", + "\n", + "- Series can also be passed into most NumPy methods expecting an ndarray.\n", + "- Alignment will be maintained" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 2.965055\n", + "b -0.755022\n", + "c 3.750472\n", + "d 2.178631\n", + "e -1.956477\n", + "dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s+s" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 4.404063\n", + "b 0.685566\n", + "c 6.522357\n", + "d 2.972239\n", + "e 0.375973\n", + "dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.exp(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a NaN\n", + "b -0.755022\n", + "c 3.750472\n", + "d 2.178631\n", + "e NaN\n", + "dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[1:] + s[:-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## DataFrame\n", + "\n", + "- `DataFrame` is a 2-dimensional labeled data structure with columns of potentially different types. \n", + "- You can think of it like a spreadsheet or SQL table, or a dict of `Series` objects.\n", + "- It's possible to create a DataFrame a lot of different ways." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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onetwo
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" + ], + "text/plain": [ + " one two\n", + "a 1.0 4.0\n", + "b 2.0 3.0\n", + "c 3.0 2.0\n", + "d 4.0 1.0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# e.g., from a dictionary\n", + "d = {'one': [1., 2., 3., 4.],\n", + " 'two': [4., 3., 2., 1.]}\n", + "df = pd.DataFrame(d, index=['a', 'b', 'c', 'd'])\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Column selection, addition, deletion\n", + "\n", + "- You can treat a DataFrame like a dict of Series objects" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 1.0\n", + "b 2.0\n", + "c 3.0\n", + "d 4.0\n", + "Name: one, dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pick a column\n", + "df['one']" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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onetwothreethresh
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" + ], + "text/plain": [ + " one two three thresh\n", + "a 1.0 4.0 5.0 False\n", + "b 2.0 3.0 5.0 False\n", + "c 3.0 2.0 5.0 True\n", + "d 4.0 1.0 5.0 True" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# make new columns\n", + "df['three'] = df['one'] + df['two']\n", + "df['thresh'] = df['one'] > 2.0\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " one thresh\n", + "a 1.0 False\n", + "b 2.0 False\n", + "c 3.0 True\n", + "d 4.0 True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you can delete with del or pop\n", + "del df['two']\n", + "df.pop('three')\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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onethreshfoo
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" + ], + "text/plain": [ + " one thresh foo\n", + "a 1.0 False bar\n", + "b 2.0 False bar\n", + "c 3.0 True bar\n", + "d 4.0 True bar" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# new values will populate the entire column\n", + "df['foo'] = 'bar'\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Indexing and Selection\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " one thresh foo\n", + "c 3.0 True bar\n", + "d 4.0 True bar" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[df['one']>2]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## SMILE and Pandas\n", + "\n", + "- We can create a DataFrame from a dict list in SMILE:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " rt disappear_time_error disappear_time_time run_num refresh_rate \\\n", + "0 2.941615 0.0 617.657840 0 26.731468 \n", + "1 1.573070 0.0 620.250024 0 26.590354 \n", + "2 2.810989 0.0 624.100785 0 27.489738 \n", + "3 0.872496 0.0 625.751111 0 27.255089 \n", + "4 1.818831 0.0 628.618369 0 26.489366 \n", + "\n", + " appear_time_error appear_time_time eeg_pulse_time right_coherence \\\n", + "0 0.0 614.707281 None 0.06 \n", + "1 0.0 618.666397 None 0.24 \n", + "2 0.0 621.275211 None 0.00 \n", + "3 0.0 624.850929 None 0.00 \n", + "4 0.0 626.784654 None 0.00 \n", + "\n", + " left_coherence press_time_error press_time_time incorrect_resp pressed \\\n", + "0 0.00 0.000510 617.648896 1 1 \n", + "1 0.30 0.000475 620.239467 4 4 \n", + "2 0.00 0.000544 624.086200 1 4 \n", + "3 0.24 0.001483 625.723425 4 1 \n", + "4 0.12 0.000755 628.603485 4 1 \n", + "\n", + " log_time correct_resp correct fmri_tr_time log_num \n", + "0 618.148896 4 False None 0 \n", + "1 620.739467 1 False None 0 \n", + "2 624.586200 4 True None 0 \n", + "3 626.223425 1 True None 0 \n", + "4 629.103485 1 True None 0 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dl = log2dl('log_MD_0.slog')\n", + "df = pd.DataFrame(dl)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# A quick summary\n", + "\n", + "- You can use the `describe` method to get a quick summary of your data frame" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rtdisappear_time_errordisappear_time_timerun_numrefresh_rateappear_time_errorappear_time_timeright_coherenceleft_coherencepress_time_errorpress_time_timelog_timelog_num
count248.000000248.0248.000000248.000000248.000000248.0248.000000248.000000248.000000248.000000248.000000248.000000248.0
mean0.8045880.01481.1269651.50000026.6675930.01480.3072700.1500000.1500000.0012041481.1118581481.6118580.0
std0.3838540.0601.1879321.1202950.8542390.0601.2335620.1045520.1045520.005860601.187790601.1877900.0
min0.2527370.0617.6578400.00000021.0592050.0614.7072810.0000000.0000000.000430617.648896618.1488960.0
25%0.5588390.01008.3402820.75000026.3976690.01007.3213250.0600000.0600000.0004971008.3281161008.8281160.0
50%0.6785020.01532.0496361.50000026.7177800.01530.6282670.1500000.1500000.0005191532.0335801532.5335800.0
75%0.9268570.02005.1566512.25000027.1433140.02004.5586310.2400000.2400000.0005472005.1430652005.6430650.0
max2.9416150.02243.1418043.00000028.6090710.02241.9832370.3000000.3000000.0692832243.1306642243.6306640.0
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" + ], + "text/plain": [ + " rt disappear_time_error disappear_time_time run_num \\\n", + "count 248.000000 248.0 248.000000 248.000000 \n", + "mean 0.804588 0.0 1481.126965 1.500000 \n", + "std 0.383854 0.0 601.187932 1.120295 \n", + "min 0.252737 0.0 617.657840 0.000000 \n", + "25% 0.558839 0.0 1008.340282 0.750000 \n", + "50% 0.678502 0.0 1532.049636 1.500000 \n", + "75% 0.926857 0.0 2005.156651 2.250000 \n", + "max 2.941615 0.0 2243.141804 3.000000 \n", + "\n", + " refresh_rate appear_time_error appear_time_time right_coherence \\\n", + "count 248.000000 248.0 248.000000 248.000000 \n", + "mean 26.667593 0.0 1480.307270 0.150000 \n", + "std 0.854239 0.0 601.233562 0.104552 \n", + "min 21.059205 0.0 614.707281 0.000000 \n", + "25% 26.397669 0.0 1007.321325 0.060000 \n", + "50% 26.717780 0.0 1530.628267 0.150000 \n", + "75% 27.143314 0.0 2004.558631 0.240000 \n", + "max 28.609071 0.0 2241.983237 0.300000 \n", + "\n", + " left_coherence press_time_error press_time_time log_time log_num \n", + "count 248.000000 248.000000 248.000000 248.000000 248.0 \n", + "mean 0.150000 0.001204 1481.111858 1481.611858 0.0 \n", + "std 0.104552 0.005860 601.187790 601.187790 0.0 \n", + "min 0.000000 0.000430 617.648896 618.148896 0.0 \n", + "25% 0.060000 0.000497 1008.328116 1008.828116 0.0 \n", + "50% 0.150000 0.000519 1532.033580 1532.533580 0.0 \n", + "75% 0.240000 0.000547 2005.143065 2005.643065 0.0 \n", + "max 0.300000 0.069283 2243.130664 2243.630664 0.0 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Some data clean-up" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rtdisappear_time_errordisappear_time_timerun_numrefresh_rateappear_time_errorappear_time_timeeeg_pulse_timeright_coherenceleft_coherence...press_time_timeincorrect_resppressedlog_timecorrect_respcorrectfmri_tr_timelog_numcoh_difflog_rt
02.9416150.0617.657840026.7314680.0614.707281None0.060.00...617.64889611618.1488964FalseNone00.061.078959
11.5730700.0620.250024026.5903540.0618.666397None0.240.30...620.23946744620.7394671FalseNone00.060.453029
22.8109890.0624.100785027.4897380.0621.275211None0.000.00...624.08620014624.5862004TrueNone00.001.033537
30.8724960.0625.751111027.2550890.0624.850929None0.000.24...625.72342541626.2234251TrueNone00.24-0.136397
41.8188310.0628.618369026.4893660.0626.784654None0.000.12...628.60348541629.1034851TrueNone00.120.598194
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5 rows × 21 columns

\n", + "
" + ], + "text/plain": [ + " rt disappear_time_error disappear_time_time run_num refresh_rate \\\n", + "0 2.941615 0.0 617.657840 0 26.731468 \n", + "1 1.573070 0.0 620.250024 0 26.590354 \n", + "2 2.810989 0.0 624.100785 0 27.489738 \n", + "3 0.872496 0.0 625.751111 0 27.255089 \n", + "4 1.818831 0.0 628.618369 0 26.489366 \n", + "\n", + " appear_time_error appear_time_time eeg_pulse_time right_coherence \\\n", + "0 0.0 614.707281 None 0.06 \n", + "1 0.0 618.666397 None 0.24 \n", + "2 0.0 621.275211 None 0.00 \n", + "3 0.0 624.850929 None 0.00 \n", + "4 0.0 626.784654 None 0.00 \n", + "\n", + " left_coherence ... press_time_time incorrect_resp pressed log_time \\\n", + "0 0.00 ... 617.648896 1 1 618.148896 \n", + "1 0.30 ... 620.239467 4 4 620.739467 \n", + "2 0.00 ... 624.086200 1 4 624.586200 \n", + "3 0.24 ... 625.723425 4 1 626.223425 \n", + "4 0.12 ... 628.603485 4 1 629.103485 \n", + "\n", + " correct_resp correct fmri_tr_time log_num coh_diff log_rt \n", + "0 4 False None 0 0.06 1.078959 \n", + "1 1 False None 0 0.06 0.453029 \n", + "2 4 True None 0 0.00 1.033537 \n", + "3 1 True None 0 0.24 -0.136397 \n", + "4 1 True None 0 0.12 0.598194 \n", + "\n", + "[5 rows x 21 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's add a new column\n", + "df['coh_diff'] = np.abs(df['right_coherence'] - df['left_coherence'])\n", + "\n", + "# and make a log rt\n", + "df['log_rt'] = np.log(df['rt'])\n", + "\n", + "# show it\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Grouping data\n", + "\n", + "- The `groupby` method allows you to create different groupings of your data" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['rt', 'disappear_time_error', 'disappear_time_time', 'run_num',\n", + " 'refresh_rate', 'appear_time_error', 'appear_time_time',\n", + " 'eeg_pulse_time', 'right_coherence', 'left_coherence',\n", + " 'press_time_error', 'press_time_time', 'incorrect_resp', 'pressed',\n", + " 'log_time', 'correct_resp', 'correct', 'fmri_tr_time', 'log_num',\n", + " 'coh_diff', 'log_rt'],\n", + " dtype='object')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "coh_diff\n", + "0.00 0.604167\n", + "0.06 0.750000\n", + "0.12 0.781250\n", + "0.18 0.958333\n", + "0.24 0.906250\n", + "0.30 1.000000\n", + "Name: correct, dtype: float64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's look at performance as a function of coh_diff\n", + "df.groupby('coh_diff')['correct'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "coh_diff\n", + "0.00 0.901983\n", + "0.06 0.905580\n", + "0.12 0.835504\n", + "0.18 0.690680\n", + "0.24 0.713827\n", + "0.30 0.659511\n", + "Name: rt, dtype: float64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# how about rts\n", + "df.groupby('coh_diff')['rt'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "correct coh_diff\n", + "False 0.00 0.829746\n", + " 0.06 1.036445\n", + " 0.12 0.783893\n", + " 0.18 0.884447\n", + " 0.24 0.834226\n", + "True 0.00 0.949310\n", + " 0.06 0.861958\n", + " 0.12 0.849955\n", + " 0.18 0.682256\n", + " 0.24 0.701371\n", + " 0.30 0.659511\n", + "Name: rt, dtype: float64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you can group by more than one column\n", + "df.groupby(['correct', 'coh_diff'])['rt'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "correct_resp\n", + "1 0.776073\n", + "4 0.833104\n", + "Name: rt, dtype: float64" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# is there a speed bias?\n", + "df.groupby(['correct_resp'])['rt'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pressed\n", + "1 0.767606\n", + "4 0.858491\n", + "Name: correct, dtype: float64" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# is there a accuracy bias?\n", + "df.groupby(['pressed'])['correct'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment before next class\n", + "\n", + "- We will post experiments for you to run, along with instructions for how to upload the data\n", + "- This will be due by ***Wednesday*** next week, so that we can prepare the data for class\n", + "\n", + "### See you next week!!!" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lessons/09_Data_Processing.ipynb b/lessons/09_Data_Processing.ipynb index bce3acf..69fb4f2 100644 --- a/lessons/09_Data_Processing.ipynb +++ b/lessons/09_Data_Processing.ipynb @@ -2357,7 +2357,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.7" }, "rise": { "scroll": true diff --git a/lessons/10_Initial_Analyses 2.ipynb b/lessons/10_Initial_Analyses 2.ipynb new file mode 100644 index 0000000..547d326 --- /dev/null +++ b/lessons/10_Initial_Analyses 2.ipynb @@ -0,0 +1,1177 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Initial Analyses\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020\n", + "By: Per B. Sederberg, PhD\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Lesson Objectives\n", + "\n", + "Upon completion of this lesson, students should have learned:\n", + "\n", + "1. Read in some real data\n", + "2. Perform some simple data clean-up\n", + "3. Some visualizations with Pandas\n", + "4. Simple statistics with SciPy and StatsModels\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Updating SMILE\n", + "\n", + "- First you can test whether there is a new version Kivy, which is the primary dependency of SMILE:\n", + "\n", + "```bash\n", + "conda install -c conda-forge kivy==1.11.1\n", + "```\n", + "\n", + "- Then you can update SMILE right from the GitHub repository (note the upgrade option at the end):\n", + "\n", + "```bash\n", + "pip install git+https://github.com/compmem/smile --upgrade\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Deep Dive Into Single Subj\n", + "\n", + "- Let's explore one subject's data and learn stuff along the way!\n", + "- Where are the data?" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0m\u001b[01;34ms001\u001b[0m/ \u001b[01;34ms002\u001b[0m/ \u001b[01;34ms003\u001b[0m/\r\n" + ] + } + ], + "source": [ + "ls data/Taskapalooza/" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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correctresprttrial_appear_timetrial_appear_errorpress_time_timepress_time_errorlog_timetextconditioncorrect_keysubjlog_num
0TrueJ4.1846392037.6527230.02041.8373620.0004662042.3373629-5-10=-6TrueJs0030
1TrueF1.5142992043.3411230.02044.8554220.0004262045.3554226+5+4=14FalseFs0030
2TrueF1.8086432046.3639930.02048.1726360.0005372048.6726365-3-1=0FalseFs0030
3TrueJ2.1578102049.6838410.02051.8416510.0004382052.3416517+8+9=24TrueJs0030
4TrueJ0.9232312053.3544540.02054.2776850.0005212054.77768510+1+3=14TrueJs0030
5TrueF1.4825982188.4834440.02189.9660420.0005582190.4660428+10+2=19FalseFs0030
6TrueF1.1294872191.4765710.02192.6060580.0004682193.10605810+1+8=29FalseFs0030
7TrueJ3.4116562194.1135610.02197.5252170.0004852198.0252173-9-2=-8TrueJs0030
8TrueF2.5386572199.0346440.02201.5733020.0004692202.0733026-7-1=8FalseFs0030
9TrueJ1.4698082203.0876780.02204.5574870.0004672205.05748710+6+7=23TrueJs0030
\n", + "
" + ], + "text/plain": [ + " correct resp rt trial_appear_time trial_appear_error \\\n", + "0 True J 4.184639 2037.652723 0.0 \n", + "1 True F 1.514299 2043.341123 0.0 \n", + "2 True F 1.808643 2046.363993 0.0 \n", + "3 True J 2.157810 2049.683841 0.0 \n", + "4 True J 0.923231 2053.354454 0.0 \n", + "5 True F 1.482598 2188.483444 0.0 \n", + "6 True F 1.129487 2191.476571 0.0 \n", + "7 True J 3.411656 2194.113561 0.0 \n", + "8 True F 2.538657 2199.034644 0.0 \n", + "9 True J 1.469808 2203.087678 0.0 \n", + "\n", + " press_time_time press_time_error log_time text condition \\\n", + "0 2041.837362 0.000466 2042.337362 9-5-10=-6 True \n", + "1 2044.855422 0.000426 2045.355422 6+5+4=14 False \n", + "2 2048.172636 0.000537 2048.672636 5-3-1=0 False \n", + "3 2051.841651 0.000438 2052.341651 7+8+9=24 True \n", + "4 2054.277685 0.000521 2054.777685 10+1+3=14 True \n", + "5 2189.966042 0.000558 2190.466042 8+10+2=19 False \n", + "6 2192.606058 0.000468 2193.106058 10+1+8=29 False \n", + "7 2197.525217 0.000485 2198.025217 3-9-2=-8 True \n", + "8 2201.573302 0.000469 2202.073302 6-7-1=8 False \n", + "9 2204.557487 0.000467 2205.057487 10+6+7=23 True \n", + "\n", + " correct_key subj log_num \n", + "0 J s003 0 \n", + "1 F s003 0 \n", + "2 F s003 0 \n", + "3 J s003 0 \n", + "4 J s003 0 \n", + "5 F s003 0 \n", + "6 F s003 0 \n", + "7 J s003 0 \n", + "8 F s003 0 \n", + "9 J s003 0 " + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from smile.log import log2dl\n", + "import pandas as pd\n", + "\n", + "# note use of kwargs to add subject info to the dataframe\n", + "df_i = pd.DataFrame(log2dl('data/Taskapalooza/s003/20201027_110222/log_image_test_0.slog', subj='s003'))\n", + "df_w = pd.DataFrame(log2dl('data/Taskapalooza/s003/20201027_110222/log_word_test_0.slog', subj='s003'))\n", + "df_f = pd.DataFrame(log2dl('data/Taskapalooza/s003/20201027_110222/log_flanker_0.slog', subj='s003'))\n", + "df_m = pd.DataFrame(log2dl('data/Taskapalooza/s003/20201027_110222/log_math_distract', subj='s003'))\n", + "\n", + "df_m.head(10)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Some data clean-up" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "# it turns out the cond is easier to visualize as pure and mixed\n", + "def fix_conds(df, type_col):\n", + " ublocks = df.block_num.unique()\n", + " for b in ublocks:\n", + " dfb = df.loc[df.block_num==b]\n", + " uval = dfb[type_col].unique()\n", + " if len(uval) > 1:\n", + " # it's mixed\n", + " df.loc[df.block_num==b, 'cond'] = 'mixed'\n", + " else:\n", + " # it's the pure\n", + " df.loc[df.block_num==b, 'cond'] = 'pure'\n", + "\n", + "# fix the conds in the recog experiments (updated in place)\n", + "fix_conds(df_i, type_col='in_out')\n", + "fix_conds(df_w, type_col='valence')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Statistics in Python\n", + "\n", + "- Once your data are in a nice tabular form, you are all set to start asking questions\n", + "- In this section we'll introduce:\n", + " - Grouping and visualization with Pandas\n", + " - Statistics with SciPy\n", + " - Simple Statistics with StatsModels" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Stats in SciPy\n", + "\n", + "- Many useful statistics are available from [SciPy](https://docs.scipy.org/doc/scipy/reference/tutorial/stats.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy import stats" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Checking for math performance\n", + "\n", + "- One test to make sure participants are trying during the task is to check performance on the math task\n", + "- ***Question: Did they perform above chance on the math problems?***" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9775280898876404" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we can calculate mean performance, but is it significant?\n", + "df_m['correct'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The binomial test\n", + "\n", + "- We need to take into account the number of problems\n", + "- and whether they did significantly above what could be expected by chance\n", + "- The binomal tests this for specific probabilities with *two* outcomes\n", + " - Like flipping a coin and determining whether it is fair or biased" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 1 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 1]\n" + ] + }, + { + "data": { + "text/plain": [ + "0.04138946533203125" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# generate some random data\n", + "dat = np.random.choice([0, 1], size=20, p=[0.3, 0.7])\n", + "print(dat)\n", + "\n", + "# calculate whether it deviates from chance\n", + "stats.binom_test(dat.sum(), len(dat), p=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prop correct: 0.98 (p=0.0000)\n" + ] + } + ], + "source": [ + "# determine number correct and total number of problems\n", + "num_correct = df_m['correct'].sum()\n", + "num_tries = len(df_m)\n", + "\n", + "# perform the statistic\n", + "p_val = stats.binom_test(num_correct, n=num_tries, \n", + " p=0.5, alternative='greater')\n", + "prop_correct = num_correct/num_tries\n", + "\n", + "# report the results (with some string formatting)\n", + "print('Prop correct: {:0.2f} (p={:0.4f})'.format(prop_correct, p_val))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What about performance on the easy conditions?\n", + "\n", + "- Another way to test for task compliance is to check the performance on the easiest task conditions.\n", + "- ***Question: Did the participant perform above chance on the congruent flanker trials?***" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prop correct: 1.00 (p=0.0000)\n" + ] + } + ], + "source": [ + "# grab a boolean index for the congruent trials\n", + "ind = df_f['condition']=='congruent'\n", + "num_correct = df_f[ind]['correct'].sum()\n", + "num_tries = ind.sum()\n", + "\n", + "# perform the statistic\n", + "p_val = stats.binom_test(num_correct, n=num_tries, \n", + " p=0.5, alternative='greater')\n", + "prop_correct = num_correct/num_tries\n", + "\n", + "# report the results (with some string formatting)\n", + "print('Prop correct: {:0.2f} (p={:0.4f})'.format(prop_correct, p_val))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Deeper Dive into Flanker\n", + "\n", + "- The typical congruency effect is that participants show lower accuracy and slower reaction times in the incongruent relative to the congruent conditions\n", + "- Let's check that for our participant!" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "condition\n", + "congruent 1.000000\n", + "incongruent 0.989583\n", + "neutral 1.000000\n", + "Name: correct, dtype: float64" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# look at mean performance by condition\n", + "df_f.groupby(['condition'])['correct'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "condition\n", + "congruent 0.682562\n", + "incongruent 0.893463\n", + "neutral 0.592118\n", + "Name: rt, dtype: float64" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# how about RT by condition?\n", + "df_f.groupby(['condition'])['rt'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## *t*-test to compare distributions\n", + "\n", + "- The *t*-test assesses whether two normal distributions are the same (or whether one distribution is different from a fixed value)\n", + "- Assumes your data are independent and normally distributed\n", + "- There are both paired (1-sample) and non-paired (independent) versions of the t-test available in scipy" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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X2z4l6SelkT1XqvSktnOFR+kBIKlRmEIBgE2JAAeApAhwAEiKAAeApAhwAEiKAAeApAhwAEjq/wFyaR+iGzSVFQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# how do our RTs look?\n", + "df_f['rt'].hist(bins='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# how about if we take the log?\n", + "np.log(df_f['rt']).hist(bins='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [], + "source": [ + "# let's just add in that column\n", + "df_f['log_rt'] = np.log(df_f['rt'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Boxplots get you closer to your data\n", + "\n", + "- While it's hard to look at individual data points, it can still be very useful to visualize your data\n", + "- A box plot is a non-parametric visualization of your data with the:\n", + " - Minimum (excluding outliers) as the upper whisker\n", + " - Maximum (excluding outliers) as the lower whisker\n", + " - Median (50% quantile) line inside the box\n", + " - 25% and 75% quantile of your data as the upper and lower box sides\n", + " - Outliers as dots" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# visualization of the comparison\n", + "df_f.boxplot(column=['log_rt'], by=['condition'])" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Ttest_indResult(statistic=-7.040795742871274, pvalue=3.3949886939953275e-11)" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we can check for statistical signficance of RTs\n", + "# with an unpaired independed samples t-test\n", + "stats.ttest_ind(df_f[df_f['condition']=='congruent']['log_rt'],\n", + " df_f[df_f['condition']=='incongruent']['log_rt'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Ttest_indResult(statistic=5.321568309157725, pvalue=2.879595834046887e-07)" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# are the neutral even better than congruent?\n", + "stats.ttest_ind(df_f[df_f['condition']=='congruent']['log_rt'],\n", + " df_f[df_f['condition']=='neutral']['log_rt'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Non-parametric statistics\n", + "\n", + "- There is an entire sub-field of statistics dedicated to situations where you can not assume your data come from normal distributions.\n", + "- Here, the approach is to turn the values into ranks and test whether then mean *ranks* are different.\n", + "- There are both paired (signed-rank test) and un-paired versions (Mann-Whitney U test) available in scipy." + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MannwhitneyuResult(statistic=1854.0, pvalue=4.2769954727175223e-13)" + ] + }, + "execution_count": 174, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# perform the non-paired non-parametric test on RTs\n", + "stats.mannwhitneyu(df_f[df_f['condition']=='congruent']['rt'],\n", + " df_f[df_f['condition']=='incongruent']['rt'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Regression and beyond!\n", + "\n", + "- More complicated questions require more complicated models\n", + "- The most standard approach in statistics is regression:\n", + "\n", + "![](https://scipy-lectures.org/_images/math/8c27948834377cd91a6907f91d1f87acb32f1817.png)\n", + "\n", + "- Here `y` is the dependent variable and `x` is the independent variable.\n", + "- This is also often called *endogenous* and *exogenous*." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Statsmodels allows us to specify regressions as formulas\n", + "\n", + "- There are many ways to fit regression models that often depend on your dependent data\n", + " - e.g., ordinary least squares vs. logistic regression" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.formula.api as smf" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "***Question: Are there differences in accuracy between conditions?***" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.368516\n", + " Iterations 6\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: correct_int No. Observations: 288
Model: Logit Df Residuals: 285
Method: MLE Df Model: 2
Date: Thu, 29 Oct 2020 Pseudo R-squ.: 0.003898
Time: 13:28:08 Log-Likelihood: -106.13
converged: True LL-Null: -106.55
Covariance Type: nonrobust LLR p-value: 0.6601
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coef std err z P>|z| [0.025 0.975]
Intercept 2.1518 0.334 6.440 0.000 1.497 2.807
condition[T.incongruent] -0.3841 0.442 -0.869 0.385 -1.250 0.482
condition[T.neutral] -0.1070 0.463 -0.231 0.817 -1.014 0.800
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: correct_int No. Observations: 288\n", + "Model: Logit Df Residuals: 285\n", + "Method: MLE Df Model: 2\n", + "Date: Thu, 29 Oct 2020 Pseudo R-squ.: 0.003898\n", + "Time: 13:28:08 Log-Likelihood: -106.13\n", + "converged: True LL-Null: -106.55\n", + "Covariance Type: nonrobust LLR p-value: 0.6601\n", + "============================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "--------------------------------------------------------------------------------------------\n", + "Intercept 2.1518 0.334 6.440 0.000 1.497 2.807\n", + "condition[T.incongruent] -0.3841 0.442 -0.869 0.385 -1.250 0.482\n", + "condition[T.neutral] -0.1070 0.463 -0.231 0.817 -1.014 0.800\n", + "============================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 190, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# dependent variables can't be boolean\n", + "df_f['correct_int'] = df['correct'].astype(int)\n", + "\n", + "# build a logistic regression\n", + "model = smf.logit(\"correct_int ~ condition\", df_f).fit()\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "***Question: Can we test for differences in RTs between conditions with regression?***" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: log_rt R-squared: 0.350
Model: OLS Adj. R-squared: 0.346
Method: Least Squares F-statistic: 76.78
Date: Thu, 29 Oct 2020 Prob (F-statistic): 2.12e-27
Time: 13:30:39 Log-Likelihood: 35.506
No. Observations: 288 AIC: -65.01
Df Residuals: 285 BIC: -54.02
Df Model: 2
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept -0.4018 0.022 -18.309 0.000 -0.445 -0.359
condition[T.incongruent] 0.2458 0.031 7.921 0.000 0.185 0.307
condition[T.neutral] -0.1332 0.031 -4.293 0.000 -0.194 -0.072
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Omnibus: 68.720 Durbin-Watson: 1.671
Prob(Omnibus): 0.000 Jarque-Bera (JB): 269.077
Skew: 0.946 Prob(JB): 3.72e-59
Kurtosis: 7.341 Cond. No. 3.73


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: log_rt R-squared: 0.350\n", + "Model: OLS Adj. R-squared: 0.346\n", + "Method: Least Squares F-statistic: 76.78\n", + "Date: Thu, 29 Oct 2020 Prob (F-statistic): 2.12e-27\n", + "Time: 13:30:39 Log-Likelihood: 35.506\n", + "No. Observations: 288 AIC: -65.01\n", + "Df Residuals: 285 BIC: -54.02\n", + "Df Model: 2 \n", + "Covariance Type: nonrobust \n", + "============================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------------------\n", + "Intercept -0.4018 0.022 -18.309 0.000 -0.445 -0.359\n", + "condition[T.incongruent] 0.2458 0.031 7.921 0.000 0.185 0.307\n", + "condition[T.neutral] -0.1332 0.031 -4.293 0.000 -0.194 -0.072\n", + "==============================================================================\n", + "Omnibus: 68.720 Durbin-Watson: 1.671\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 269.077\n", + "Skew: 0.946 Prob(JB): 3.72e-59\n", + "Kurtosis: 7.341 Cond. No. 3.73\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# build a linear regression\n", + "model = smf.ols(\"log_rt ~ condition\", df_f).fit()\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment before next class\n", + "\n", + "- We will post a small set of analyses to run on some of the other data based on the examples in this class\n", + "- This will be due on ***Thursday*** next week\n", + "\n", + "### See you next week!!!" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lessons/11_Across_Subject_Analyses 2.ipynb b/lessons/11_Across_Subject_Analyses 2.ipynb new file mode 100644 index 0000000..6d69e78 --- /dev/null +++ b/lessons/11_Across_Subject_Analyses 2.ipynb @@ -0,0 +1,2751 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Across-Subject Analyses\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020\n", + "By: Per B. Sederberg, PhD\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Lesson Objectives\n", + "\n", + "Upon completion of this lesson, students should have learned:\n", + "\n", + "1. How to read in all the data\n", + "2. Perform some simple data clean-up\n", + "3. Some more Pandas analysis tricks\n", + "4. Regression across subjects\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Updating SMILE\n", + "\n", + "- First you can test whether there is a new version Kivy, which is the primary dependency of SMILE:\n", + "\n", + "```bash\n", + "conda install -c conda-forge kivy==1.11.1\n", + "```\n", + "\n", + "- Then you can update SMILE right from the GitHub repository (note the upgrade option at the end):\n", + "\n", + "```bash\n", + "pip install git+https://github.com/compmem/smile --upgrade\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Loading in all the data\n", + "\n", + "- Let's explore one subject's data and learn stuff along the way!\n", + "- Where are the data?" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log_flanker_0.slog state_If_0.slog\r\n", + "log_image_study_0.slog state_Image_0.slog\r\n", + "log_image_test_0.slog state_KeyPress_0.slog\r\n", + "log_math_distract_0.slog state_Label_0.slog\r\n", + "log_math_distract_1.slog state_Loop_0.slog\r\n", + "log_word_study_0.slog state_Parallel_0.slog\r\n", + "log_word_test_0.slog state_ParentSet_0.slog\r\n", + "state_Beep_0.slog state_Rectangle_0.slog\r\n", + "state_Button_0.slog state_Serial_0.slog\r\n", + "state_ButtonPress_0.slog state_SubroutineState_0.slog\r\n", + "state_Elif_0.slog state_Wait_0.slog\r\n", + "state_Func_0.slog sysinfo.slog\r\n" + ] + } + ], + "source": [ + "ls data/Taskapalooza/s001" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from smile.log import log2dl\n", + "import numpy as np\n", + "from scipy import stats\n", + "import pandas as pd\n", + "from glob import glob\n", + "import os\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Glob?\n", + "\n", + "- Allows for pattern matching for files\n", + "- Here we want to return a list of all the subject directories matching a specific pattern" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['data/Taskapalooza/s018',\n", + " 'data/Taskapalooza/s006',\n", + " 'data/Taskapalooza/s013',\n", + " 'data/Taskapalooza/s015',\n", + " 'data/Taskapalooza/s014',\n", + " 'data/Taskapalooza/s001',\n", + " 'data/Taskapalooza/s003',\n", + " 'data/Taskapalooza/s008',\n", + " 'data/Taskapalooza/s005',\n", + " 'data/Taskapalooza/s016',\n", + " 'data/Taskapalooza/s002',\n", + " 'data/Taskapalooza/s021',\n", + " 'data/Taskapalooza/s017',\n", + " 'data/Taskapalooza/s011',\n", + " 'data/Taskapalooza/s022',\n", + " 'data/Taskapalooza/s007',\n", + " 'data/Taskapalooza/s010',\n", + " 'data/Taskapalooza/s019',\n", + " 'data/Taskapalooza/s020',\n", + " 'data/Taskapalooza/s023',\n", + " 'data/Taskapalooza/s009',\n", + " 'data/Taskapalooza/s012',\n", + " 'data/Taskapalooza/s004']" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glob('data/Taskapalooza/s???')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Manipulating paths with `os.path`\n", + "\n", + "- Different operation systems have different ways of dealing with directories\n", + " - Windows separate directories with `\\`\n", + " - Most others use `/`\n", + "- Python can handle processing paths for you with the `os.path` module" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('data/Taskapalooza', 's018')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# split up the path\n", + "os.path.split('data/Taskapalooza/s018')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Custom SLOG loading function" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# custom function to load slogs\n", + "def load_all_subj_logs(task_dir, log_file):\n", + " # load in a list of all the subj\n", + " subjs = [os.path.split(subj_dir)[-1] \n", + " for subj_dir in glob(os.path.join(task_dir, 's*'))]\n", + " subjs.sort()\n", + "\n", + " # loop over subj and their data\n", + " all_dat = []\n", + " for subj in subjs:\n", + " # set the file\n", + " log_path = os.path.join(task_dir, subj, log_file)\n", + " #print(log_path)\n", + "\n", + " # load the data\n", + " all_dat.extend(log2dl(log_path, subj=subj))\n", + "\n", + " df = pd.DataFrame(all_dat)\n", + " \n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Load in all the data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0FJ001829.9830960.0F1830.6146060.0002060.631509True1124.413938852.3848841831.313423neutralleft===<===s0010
1FJ011831.3202580.0J1831.7083340.0001810.388076True1488.898432948.5999641832.235087neutralright===>===s0010
2FJ021832.2441980.0F1832.8182320.0001970.574034True1853.572485748.9010921833.391370neutralleft===<===s0010
3FJ031833.4045860.0F1833.9905490.0002030.585963True1413.608109996.7044221834.859940neutralleft===<===s0010
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" + ], + "text/plain": [ + " resp_map_left resp_map_right block_num trial_num stim_on_time \\\n", + "0 F J 0 0 1829.983096 \n", + "1 F J 0 1 1831.320258 \n", + "2 F J 0 2 1832.244198 \n", + "3 F J 0 3 1833.404586 \n", + "4 F J 0 4 1834.876087 \n", + "\n", + " stim_on_error resp resp_time_time resp_time_error rt correct \\\n", + "0 0.0 F 1830.614606 0.000206 0.631509 True \n", + "1 0.0 J 1831.708334 0.000181 0.388076 True \n", + "2 0.0 F 1832.818232 0.000197 0.574034 True \n", + "3 0.0 F 1833.990549 0.000203 0.585963 True \n", + "4 0.0 F 1835.262948 0.000186 0.386861 True \n", + "\n", + " location_0 location_1 log_time condition direction stimulus subj \\\n", + "0 1124.413938 852.384884 1831.313423 neutral left ===<=== s001 \n", + "1 1488.898432 948.599964 1832.235087 neutral right ===>=== s001 \n", + "2 1853.572485 748.901092 1833.391370 neutral left ===<=== s001 \n", + "3 1413.608109 996.704422 1834.859940 neutral left ===<=== s001 \n", + "4 1557.565574 686.474531 1836.017047 neutral left ===<=== s001 \n", + "\n", + " log_num \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data from each task\n", + "task_dir = os.path.join('data', 'Taskapalooza')\n", + "\n", + "df_f = load_all_subj_logs(task_dir, 'log_flanker')\n", + "df_m = load_all_subj_logs(task_dir, 'log_math_distract')\n", + "df_i = load_all_subj_logs(task_dir, 'log_image_test')\n", + "df_w = load_all_subj_logs(task_dir, 'log_word_test')\n", + "df_f.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Some data clean-up" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [], + "source": [ + "# it turns out the cond is easier to visualize as pure and mixed\n", + "def fix_conds(df, type_col):\n", + " # loop over the unique subjects\n", + " usubj = df.subj.unique()\n", + " for s in usubj:\n", + " # loop over their blocks\n", + " ublocks = df.loc[df['subj']==s, 'block_num'].unique()\n", + " for b in ublocks:\n", + " # grab the data for that subj and block\n", + " dfb = df.loc[(df['subj']==s)&(df['block_num']==b)]\n", + " \n", + " # get the unique types in that block\n", + " uval = dfb[type_col].unique()\n", + " if len(uval) > 1:\n", + " # it's mixed\n", + " df.loc[(df['subj']==s)&(df.block_num==b), 'cond'] = 'mixed'\n", + " else:\n", + " # it's the pure\n", + " df.loc[(df['subj']==s)&(df.block_num==b), 'cond'] = 'pure'\n", + "\n", + "# fix the conds in the recog experiments (updated in place)\n", + "fix_conds(df_i, type_col='in_out')\n", + "fix_conds(df_w, type_col='valence')\n", + "\n", + "\n", + "# add in log_rt columns\n", + "df_i['log_rt'] = np.log(df_i['rt'])\n", + "df_w['log_rt'] = np.log(df_w['rt'])\n", + "df_f['log_rt'] = np.log(df_f['rt'])\n", + "df_m['log_rt'] = np.log(df_m['rt'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Check Math Performance\n", + "\n", + "- We want to calculate mean performance and run a binomial test on each participant\n", + "- How can we do this efficiently with Pandas?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Start with a `groupby`\n", + "\n", + "- We know we can group rows of data by subject and apply a function to specific columns" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([ 922, 923, 924, 925, 926, 927, 928, 929, 930, 931,\n", + " ...\n", + " 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031],\n", + " dtype='int64', length=110)" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_m.groupby(['subj']).groups['s011']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "subj\n", + "s001 0.797753\n", + "s002 0.767677\n", + "s003 0.977528\n", + "s004 0.875000\n", + "s005 0.862745\n", + "s006 0.934783\n", + "s007 0.913043\n", + "s008 0.768421\n", + "s009 0.892857\n", + "s010 0.793478\n", + "s011 0.881818\n", + "s012 0.942857\n", + "s013 0.858696\n", + "s014 0.804124\n", + "s015 0.723404\n", + "s016 0.881720\n", + "s017 0.814433\n", + "s018 0.989247\n", + "s019 0.858974\n", + "s020 0.955056\n", + "s021 0.967391\n", + "s022 0.847619\n", + "s023 0.700000\n", + "Name: correct, dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_m.groupby(['subj'])['correct'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The `agg` method can help\n", + "\n", + "- `agg` allows us to run more than one function on each group\n", + "- Can provide either a string or the actual function\n", + "- We need the `sum` and the `count` for the binomial test" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sum count mean\n", + "subj \n", + "s001 71 89 0.797753\n", + "s002 76 99 0.767677\n", + "s003 87 89 0.977528\n", + "s004 77 88 0.875000\n", + "s005 88 102 0.862745" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mperf = df_m.groupby(['subj'])['correct'].agg(['sum', 'count', 'mean'])\n", + "mperf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Use `apply` to run a function on each row\n", + "\n", + "- The `apply` method of a `DataFrame` allows you to \n", + "- Here we need to specify a custom function that uses the info from each row to call `stats.binom_test`\n", + " - We could have defined a separate function\n", + " - But here I'll use `lambda` to define a function inline, since it's only one line of code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def run_binom_test(x):\n", + " return stats.binom_test(x['sum'], n=x['count'], \n", + " p=0.5, alternative='greater')," + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sumcountmeanbinom_pvalgood
subj
s00171890.7977536.484375e-09True
s00276990.7676774.267164e-08True
s00387890.9775286.472042e-24True
s00477880.8750001.200777e-13True
s005881020.8627451.387694e-14True
s00686920.9347831.545247e-19True
s00784920.9130432.072124e-17True
s00873950.7684217.314912e-08True
s00975840.8928572.154292e-14True
s01073920.7934786.170194e-09True
s011971100.8818182.350471e-17True
s012991050.9428574.217640e-23True
s01379920.8586965.366874e-13True
s01478970.8041245.767114e-10True
s01568940.7234048.658456e-06True
s01682930.8817207.079936e-15True
s01779970.8144331.360272e-10True
s01892930.9892479.491574e-27True
s01967780.8589743.059155e-11True
s02085890.9550564.134604e-21True
s02189920.9673912.622482e-23True
s022891050.8476199.356564e-14True
s023841200.7000006.948506e-06True
\n", + "
" + ], + "text/plain": [ + " sum count mean binom_pval good\n", + "subj \n", + "s001 71 89 0.797753 6.484375e-09 True\n", + "s002 76 99 0.767677 4.267164e-08 True\n", + "s003 87 89 0.977528 6.472042e-24 True\n", + "s004 77 88 0.875000 1.200777e-13 True\n", + "s005 88 102 0.862745 1.387694e-14 True\n", + "s006 86 92 0.934783 1.545247e-19 True\n", + "s007 84 92 0.913043 2.072124e-17 True\n", + "s008 73 95 0.768421 7.314912e-08 True\n", + "s009 75 84 0.892857 2.154292e-14 True\n", + "s010 73 92 0.793478 6.170194e-09 True\n", + "s011 97 110 0.881818 2.350471e-17 True\n", + "s012 99 105 0.942857 4.217640e-23 True\n", + "s013 79 92 0.858696 5.366874e-13 True\n", + "s014 78 97 0.804124 5.767114e-10 True\n", + "s015 68 94 0.723404 8.658456e-06 True\n", + "s016 82 93 0.881720 7.079936e-15 True\n", + "s017 79 97 0.814433 1.360272e-10 True\n", + "s018 92 93 0.989247 9.491574e-27 True\n", + "s019 67 78 0.858974 3.059155e-11 True\n", + "s020 85 89 0.955056 4.134604e-21 True\n", + "s021 89 92 0.967391 2.622482e-23 True\n", + "s022 89 105 0.847619 9.356564e-14 True\n", + "s023 84 120 0.700000 6.948506e-06 True" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# add the binom_test p value as a new column (axis=1 tells it to go by row)\n", + "mperf['binom_pval'] = mperf.apply(lambda x: stats.binom_test(x['sum'], n=x['count'], \n", + " p=0.5, alternative='greater'),\n", + " axis=1)\n", + "\n", + "# they are good if the mean is greater than 0.5 and the pval is less that .05\n", + "mperf['good'] = (mperf['mean']>0.5) & (mperf['binom_pval'] <= 0.05)\n", + "mperf" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# quick plot of performance\n", + "ax = mperf['mean'].hist(bins='auto')\n", + "ax.axvline(0.5, color='k', lw=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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sumcountmeanbinom_pval
subjcondition
s001congruent94960.9791671.175592e-25
incongruent80960.8333332.074411e-11
neutral95960.9895832.448624e-27
s002congruent94960.9791671.175592e-25
incongruent91960.9479171.630571e-21
..................
s022incongruent89960.9270833.259153e-19
neutral95960.9895832.448624e-27
s023congruent92960.9583338.758242e-23
incongruent36960.3750001.843335e-02
neutral90960.9375002.503256e-20
\n", + "

69 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " sum count mean binom_pval\n", + "subj condition \n", + "s001 congruent 94 96 0.979167 1.175592e-25\n", + " incongruent 80 96 0.833333 2.074411e-11\n", + " neutral 95 96 0.989583 2.448624e-27\n", + "s002 congruent 94 96 0.979167 1.175592e-25\n", + " incongruent 91 96 0.947917 1.630571e-21\n", + "... ... ... ... ...\n", + "s022 incongruent 89 96 0.927083 3.259153e-19\n", + " neutral 95 96 0.989583 2.448624e-27\n", + "s023 congruent 92 96 0.958333 8.758242e-23\n", + " incongruent 36 96 0.375000 1.843335e-02\n", + " neutral 90 96 0.937500 2.503256e-20\n", + "\n", + "[69 rows x 4 columns]" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fperf = df_f.groupby(['subj', 'condition'])['correct'].agg(['sum', 'count', 'mean'])\n", + "fperf['binom_pval'] = fperf.apply(lambda x: stats.binom_test(x['sum'], n=x['count'], \n", + " p=0.5, alternative='two-sided'),\n", + " axis=1)\n", + "fperf" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Congruency effect\n", + "\n", + "- The typical congruency effect is that participants show lower accuracy and slower reaction times in the incongruent relative to the congruent conditions\n", + "- Let's check that for all our participants!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Performance by condition" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Condition')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Let's make a boxplot of the performance values\n", + "ax = fperf.boxplot(column='mean', by=['condition'])\n", + "ax.set_title('')\n", + "ax.set_ylabel('Performance')\n", + "ax.set_xlabel('Condition')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### RT by condition" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Condition')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fres = df_f.groupby(['subj', 'condition'])['log_rt'].mean().reset_index()\n", + "ax = fres.boxplot(column='log_rt', by=['condition'])\n", + "ax.set_title('')\n", + "ax.set_ylabel('Log(RT)')\n", + "ax.set_xlabel('Condition')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Error bars for repeated measures designs\n", + "\n", + "- The box plots above do not actually show us the relationships between the conditions that indicate significance\n", + "- We have a repeated-measures design, with different conditions *within* subjects\n", + "- Thus, the real comparisons of interest are *within* subjects, not *between* subjects\n", + " - In the box plots, the between-subject variability is masking the within-subject effects!\n", + "- To help us visualize the differences more-accurately, we can calculate within-subject corrected error bars\n", + " - All this entails is removing the within-subject mean across conditions before calculating error" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# some folks wrote a useful script for calculating these for us!\n", + "from ci_within import ci_within" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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meanstdsemcilen
conditioncorrect
congruentFalse-0.6495450.2951230.0615370.12762123.0
True-0.5931830.2577110.0055130.0108122185.0
incongruentFalse-0.5813880.4272610.0299140.058983204.0
True-0.2115660.3297540.0073660.0144462004.0
neutralFalse-0.5914970.5188510.0864750.17555436.0
True-0.6146230.2259980.0048490.0095102172.0
\n", + "
" + ], + "text/plain": [ + " mean std sem ci len\n", + "condition correct \n", + "congruent False -0.649545 0.295123 0.061537 0.127621 23.0\n", + " True -0.593183 0.257711 0.005513 0.010812 2185.0\n", + "incongruent False -0.581388 0.427261 0.029914 0.058983 204.0\n", + " True -0.211566 0.329754 0.007366 0.014446 2004.0\n", + "neutral False -0.591497 0.518851 0.086475 0.175554 36.0\n", + " True -0.614623 0.225998 0.004849 0.009510 2172.0" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get the error corrected by condition and whether they got it correct\n", + "res = ci_within(df_f, indexvar='subj', \n", + " withinvars=['condition', 'correct'], \n", + " measvar='log_rt')\n", + "res" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Unstacking and Resetting Index\n", + "\n", + "- After a group-by, we often need to pivot the data so that it has the right indices for plotting\n", + "- The `unstack` command takes a multi-level index and moves one of the row indices to a column\n", + "- Here we'll move the `correct` row indicator to be a column indicator\n", + "- Calling `reset_index` will fill in the values in all the index columns and add in an overall index." + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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conditionmeanstdsemcilen
correctFalseTrueFalseTrueFalseTrueFalseTrueFalseTrue
0congruent-0.649545-0.5931830.2951230.2577110.0615370.0055130.1276210.01081223.02185.0
1incongruent-0.581388-0.2115660.4272610.3297540.0299140.0073660.0589830.014446204.02004.0
2neutral-0.591497-0.6146230.5188510.2259980.0864750.0048490.1755540.00951036.02172.0
\n", + "
" + ], + "text/plain": [ + " condition mean std sem \\\n", + "correct False True False True False \n", + "0 congruent -0.649545 -0.593183 0.295123 0.257711 0.061537 \n", + "1 incongruent -0.581388 -0.211566 0.427261 0.329754 0.029914 \n", + "2 neutral -0.591497 -0.614623 0.518851 0.225998 0.086475 \n", + "\n", + " ci len \n", + "correct True False True False True \n", + "0 0.005513 0.127621 0.010812 23.0 2185.0 \n", + "1 0.007366 0.058983 0.014446 204.0 2004.0 \n", + "2 0.004849 0.175554 0.009510 36.0 2172.0 " + ] + }, + "execution_count": 158, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# must unstack and reset index to plot properly\n", + "res.unstack().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Log(RT)')" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the results\n", + "ax = res.unstack().reset_index().plot(x='condition', y='mean', yerr='ci', kind=\"bar\")\n", + "#ax.get_legend().remove()\n", + "ax.set_xlabel('Condition')\n", + "ax.set_ylabel('Log(RT)')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Regression and beyond!\n", + "\n", + "- This sure looks significant, but we need to build a regression to test all these factors\n", + "\n", + "![](https://scipy-lectures.org/_images/math/8c27948834377cd91a6907f91d1f87acb32f1817.png)\n", + "\n", + "- Here `y` is the dependent variable and `x` is the independent variable(s).\n", + "- This is also often called *endogenous* and *exogenous*." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Statsmodels allows us to specify regressions as formulas\n", + "\n", + "- There are many ways to fit regression models that often depend on your dependent data\n", + " - e.g., ordinary least squares vs. logistic regression" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.formula.api as smf" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Question: Are there differences in accuracy between conditions?" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjconditioncorrect
0s001congruent0.979167
1s001incongruent0.833333
2s001neutral0.989583
3s002congruent0.979167
4s002incongruent0.947917
............
64s022incongruent0.927083
65s022neutral0.989583
66s023congruent0.958333
67s023incongruent0.375000
68s023neutral0.937500
\n", + "

69 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " subj condition correct\n", + "0 s001 congruent 0.979167\n", + "1 s001 incongruent 0.833333\n", + "2 s001 neutral 0.989583\n", + "3 s002 congruent 0.979167\n", + "4 s002 incongruent 0.947917\n", + ".. ... ... ...\n", + "64 s022 incongruent 0.927083\n", + "65 s022 neutral 0.989583\n", + "66 s023 congruent 0.958333\n", + "67 s023 incongruent 0.375000\n", + "68 s023 neutral 0.937500\n", + "\n", + "[69 rows x 3 columns]" + ] + }, + "execution_count": 161, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# first we need to get summary values for each subj\n", + "sum_df = df_f.groupby(['subj', 'condition'])['correct'].mean().reset_index()\n", + "sum_df" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: correct R-squared: 0.192
Model: OLS Adj. R-squared: 0.168
Method: Least Squares F-statistic: 7.862
Date: Thu, 05 Nov 2020 Prob (F-statistic): 0.000866
Time: 16:02:23 Log-Likelihood: 79.466
No. Observations: 69 AIC: -152.9
Df Residuals: 66 BIC: -146.2
Df Model: 2
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept 0.9896 0.016 60.684 0.000 0.957 1.022
condition[T.incongruent] -0.0820 0.023 -3.555 0.001 -0.128 -0.036
condition[T.neutral] -0.0059 0.023 -0.255 0.799 -0.052 0.040
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Omnibus: 108.905 Durbin-Watson: 1.709
Prob(Omnibus): 0.000 Jarque-Bera (JB): 3118.136
Skew: -4.889 Prob(JB): 0.00
Kurtosis: 34.448 Cond. No. 3.73


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: correct R-squared: 0.192\n", + "Model: OLS Adj. R-squared: 0.168\n", + "Method: Least Squares F-statistic: 7.862\n", + "Date: Thu, 05 Nov 2020 Prob (F-statistic): 0.000866\n", + "Time: 16:02:23 Log-Likelihood: 79.466\n", + "No. Observations: 69 AIC: -152.9\n", + "Df Residuals: 66 BIC: -146.2\n", + "Df Model: 2 \n", + "Covariance Type: nonrobust \n", + "============================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------------------\n", + "Intercept 0.9896 0.016 60.684 0.000 0.957 1.022\n", + "condition[T.incongruent] -0.0820 0.023 -3.555 0.001 -0.128 -0.036\n", + "condition[T.neutral] -0.0059 0.023 -0.255 0.799 -0.052 0.040\n", + "==============================================================================\n", + "Omnibus: 108.905 Durbin-Watson: 1.709\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 3118.136\n", + "Skew: -4.889 Prob(JB): 0.00\n", + "Kurtosis: 34.448 Cond. No. 3.73\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# build a least squares regression\n", + "model = smf.ols(\"correct ~ condition\", sum_df).fit()\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Question: Are there differences in RTs between conditions?" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjconditioncorrectlog_rt
0s001congruentFalse-0.722098
1s001congruentTrue-0.730229
2s001incongruentFalse-0.832261
3s001incongruentTrue-0.146624
4s001neutralFalse-0.690644
...............
116s023congruentTrue-0.793806
117s023incongruentFalse-0.760241
118s023incongruentTrue-0.188005
119s023neutralFalse-0.911746
120s023neutralTrue-0.768993
\n", + "

121 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " subj condition correct log_rt\n", + "0 s001 congruent False -0.722098\n", + "1 s001 congruent True -0.730229\n", + "2 s001 incongruent False -0.832261\n", + "3 s001 incongruent True -0.146624\n", + "4 s001 neutral False -0.690644\n", + ".. ... ... ... ...\n", + "116 s023 congruent True -0.793806\n", + "117 s023 incongruent False -0.760241\n", + "118 s023 incongruent True -0.188005\n", + "119 s023 neutral False -0.911746\n", + "120 s023 neutral True -0.768993\n", + "\n", + "[121 rows x 4 columns]" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# first we need to get summary values for each subj\n", + "sum_df = df_f.groupby(['subj', 'condition', 'correct'])['log_rt'].mean().reset_index()\n", + "sum_df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "#### Full model with interaction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "log_rt ~ condition + correct + condition*correct + intercept + noise" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: log_rt R-squared: 0.123
Model: OLS Adj. R-squared: 0.085
Method: Least Squares F-statistic: 3.225
Date: Thu, 05 Nov 2020 Prob (F-statistic): 0.00922
Time: 13:48:43 Log-Likelihood: -59.297
No. Observations: 121 AIC: 130.6
Df Residuals: 115 BIC: 147.4
Df Model: 5
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept -0.5988 0.108 -5.530 0.000 -0.813 -0.384
condition[T.incongruent] 0.1093 0.140 0.782 0.436 -0.168 0.386
condition[T.neutral] 0.0816 0.146 0.558 0.578 -0.208 0.371
correct[T.True] 0.0058 0.137 0.042 0.967 -0.266 0.278
condition[T.incongruent]:correct[T.True] 0.2791 0.184 1.517 0.132 -0.085 0.643
condition[T.neutral]:correct[T.True] -0.1029 0.189 -0.545 0.587 -0.477 0.271
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Omnibus: 103.979 Durbin-Watson: 1.018
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1057.580
Skew: 2.934 Prob(JB): 2.24e-230
Kurtosis: 16.242 Cond. No. 11.3


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: log_rt R-squared: 0.123\n", + "Model: OLS Adj. R-squared: 0.085\n", + "Method: Least Squares F-statistic: 3.225\n", + "Date: Thu, 05 Nov 2020 Prob (F-statistic): 0.00922\n", + "Time: 13:48:43 Log-Likelihood: -59.297\n", + "No. Observations: 121 AIC: 130.6\n", + "Df Residuals: 115 BIC: 147.4\n", + "Df Model: 5 \n", + "Covariance Type: nonrobust \n", + "============================================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------------------------\n", + "Intercept -0.5988 0.108 -5.530 0.000 -0.813 -0.384\n", + "condition[T.incongruent] 0.1093 0.140 0.782 0.436 -0.168 0.386\n", + "condition[T.neutral] 0.0816 0.146 0.558 0.578 -0.208 0.371\n", + "correct[T.True] 0.0058 0.137 0.042 0.967 -0.266 0.278\n", + "condition[T.incongruent]:correct[T.True] 0.2791 0.184 1.517 0.132 -0.085 0.643\n", + "condition[T.neutral]:correct[T.True] -0.1029 0.189 -0.545 0.587 -0.477 0.271\n", + "==============================================================================\n", + "Omnibus: 103.979 Durbin-Watson: 1.018\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1057.580\n", + "Skew: 2.934 Prob(JB): 2.24e-230\n", + "Kurtosis: 16.242 Cond. No. 11.3\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# build a linear regression of the full model\n", + "m0 = smf.ols(\"log_rt ~ condition * correct\", sum_df).fit()\n", + "m0.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "#### Model with only interaction" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: log_rt R-squared: 0.123
Model: OLS Adj. R-squared: 0.085
Method: Least Squares F-statistic: 3.225
Date: Thu, 05 Nov 2020 Prob (F-statistic): 0.00922
Time: 13:48:46 Log-Likelihood: -59.297
No. Observations: 121 AIC: 130.6
Df Residuals: 115 BIC: 147.4
Df Model: 5
Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [0.025 0.975]
Intercept -0.5988 0.108 -5.530 0.000 -0.813 -0.384
correct[T.True] 0.0058 0.137 0.042 0.967 -0.266 0.278
condition[T.incongruent]:correct[False] 0.1093 0.140 0.782 0.436 -0.168 0.386
condition[T.neutral]:correct[False] 0.0816 0.146 0.558 0.578 -0.208 0.371
condition[T.incongruent]:correct[True] 0.3884 0.119 3.251 0.002 0.152 0.625
condition[T.neutral]:correct[True] -0.0213 0.119 -0.178 0.859 -0.258 0.215
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Omnibus: 103.979 Durbin-Watson: 1.018
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1057.580
Skew: 2.934 Prob(JB): 2.24e-230
Kurtosis: 16.242 Cond. No. 7.80


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: log_rt R-squared: 0.123\n", + "Model: OLS Adj. R-squared: 0.085\n", + "Method: Least Squares F-statistic: 3.225\n", + "Date: Thu, 05 Nov 2020 Prob (F-statistic): 0.00922\n", + "Time: 13:48:46 Log-Likelihood: -59.297\n", + "No. Observations: 121 AIC: 130.6\n", + "Df Residuals: 115 BIC: 147.4\n", + "Df Model: 5 \n", + "Covariance Type: nonrobust \n", + "===========================================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "-----------------------------------------------------------------------------------------------------------\n", + "Intercept -0.5988 0.108 -5.530 0.000 -0.813 -0.384\n", + "correct[T.True] 0.0058 0.137 0.042 0.967 -0.266 0.278\n", + "condition[T.incongruent]:correct[False] 0.1093 0.140 0.782 0.436 -0.168 0.386\n", + "condition[T.neutral]:correct[False] 0.0816 0.146 0.558 0.578 -0.208 0.371\n", + "condition[T.incongruent]:correct[True] 0.3884 0.119 3.251 0.002 0.152 0.625\n", + "condition[T.neutral]:correct[True] -0.0213 0.119 -0.178 0.859 -0.258 0.215\n", + "==============================================================================\n", + "Omnibus: 103.979 Durbin-Watson: 1.018\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1057.580\n", + "Skew: 2.934 Prob(JB): 2.24e-230\n", + "Kurtosis: 16.242 Cond. No. 7.80\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m1 = smf.ols(\"log_rt ~ condition : correct\", sum_df).fit()\n", + "m1.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "#### Model with only correct items" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjconditionlog_rt
0s001congruent-0.730229
1s001incongruent-0.146624
2s001neutral-0.722494
3s002congruent-0.633025
4s002incongruent-0.454405
............
64s022incongruent-0.356837
65s022neutral-0.789345
66s023congruent-0.793806
67s023incongruent-0.188005
68s023neutral-0.768993
\n", + "

69 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " subj condition log_rt\n", + "0 s001 congruent -0.730229\n", + "1 s001 incongruent -0.146624\n", + "2 s001 neutral -0.722494\n", + "3 s002 congruent -0.633025\n", + "4 s002 incongruent -0.454405\n", + ".. ... ... ...\n", + "64 s022 incongruent -0.356837\n", + "65 s022 neutral -0.789345\n", + "66 s023 congruent -0.793806\n", + "67 s023 incongruent -0.188005\n", + "68 s023 neutral -0.768993\n", + "\n", + "[69 rows x 3 columns]" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum_df = df_f.loc[df_f['correct']].groupby(['subj', 'condition'])['log_rt'].mean().reset_index()\n", + "sum_df" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: log_rt R-squared: 0.401
Model: OLS Adj. R-squared: 0.382
Method: Least Squares F-statistic: 22.06
Date: Thu, 05 Nov 2020 Prob (F-statistic): 4.61e-08
Time: 16:20:45 Log-Likelihood: 3.4100
No. Observations: 69 AIC: -0.8201
Df Residuals: 66 BIC: 5.882
Df Model: 2
Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [0.025 0.975]
Intercept -0.5930 0.049 -12.078 0.000 -0.691 -0.495
condition[T.incongruent] 0.3884 0.069 5.593 0.000 0.250 0.527
condition[T.neutral] -0.0213 0.069 -0.306 0.760 -0.160 0.117
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Omnibus: 48.632 Durbin-Watson: 0.942
Prob(Omnibus): 0.000 Jarque-Bera (JB): 170.063
Skew: 2.192 Prob(JB): 1.18e-37
Kurtosis: 9.320 Cond. No. 3.73


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: log_rt R-squared: 0.401\n", + "Model: OLS Adj. R-squared: 0.382\n", + "Method: Least Squares F-statistic: 22.06\n", + "Date: Thu, 05 Nov 2020 Prob (F-statistic): 4.61e-08\n", + "Time: 16:20:45 Log-Likelihood: 3.4100\n", + "No. Observations: 69 AIC: -0.8201\n", + "Df Residuals: 66 BIC: 5.882\n", + "Df Model: 2 \n", + "Covariance Type: nonrobust \n", + "============================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------------------\n", + "Intercept -0.5930 0.049 -12.078 0.000 -0.691 -0.495\n", + "condition[T.incongruent] 0.3884 0.069 5.593 0.000 0.250 0.527\n", + "condition[T.neutral] -0.0213 0.069 -0.306 0.760 -0.160 0.117\n", + "==============================================================================\n", + "Omnibus: 48.632 Durbin-Watson: 0.942\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 170.063\n", + "Skew: 2.192 Prob(JB): 1.18e-37\n", + "Kurtosis: 9.320 Cond. No. 3.73\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m2 = smf.ols(\"log_rt ~ condition\", sum_df).fit()\n", + "m2.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Conclusion\n", + "\n", + "- We see a robust congruency effect whereby:\n", + " - Incongruent trials have lower performance than congruent\n", + " - *Correct* incongruent trials are much slower than *incorrect*\n", + " - i.e., participants make \"fast\" errors" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment before next class\n", + "\n", + "- We will post a small set of analyses to run on the memory data based on the examples in this class\n", + "- This will be due on ***Thursday*** next week\n", + "\n", + "### See you next week!!!" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lessons/12_Recognition_Memory-Copy1.ipynb b/lessons/12_Recognition_Memory-Copy1.ipynb new file mode 100644 index 0000000..9852c28 --- /dev/null +++ b/lessons/12_Recognition_Memory-Copy1.ipynb @@ -0,0 +1,2489 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Recognition Memory\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020\n", + "By: Per B. Sederberg, PhD\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Lesson Objectives\n", + "\n", + "Upon completion of this lesson, students should have learned:\n", + "\n", + "1. Some basic probability\n", + "2. Strength theories of memory\n", + "3. its relation to Signal Detection Theory\n", + "4. How to calculate sensitivity (d', or d-prime) and bias (c)\n", + "5. Plotting and statistics with d-prime and bias\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# New library to install\n", + "\n", + "You're going to need a new plotting library, so run this line at your Anaconda Prompt/Terminal:\n", + "\n", + "`conda install -c conda-forge plotnine` " + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "# import some useful libraries\n", + "import numpy as np # numerical analysis linear algebra\n", + "import pandas as pd # efficient tables\n", + "import matplotlib.pyplot as plt # plotting\n", + "import plotnine as pn\n", + "import scipy.stats.distributions as dists # probability distributions\n", + "from scipy import stats\n", + "from glob import glob\n", + "import os\n", + "\n", + "from smile.log import log2dl\n", + "\n", + "from ci_within import ci_within" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Custom SLOG loading function" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "# custom function to load slogs\n", + "def load_all_subj_logs(task_dir, log_file):\n", + " # load in a list of all the subj\n", + " subjs = [os.path.split(subj_dir)[-1] \n", + " for subj_dir in glob(os.path.join(task_dir, 's*'))]\n", + " subjs.sort()\n", + "\n", + " # loop over subj and their data\n", + " all_dat = []\n", + " for subj in subjs:\n", + " # set the file\n", + " log_path = os.path.join(task_dir, subj, log_file)\n", + " #print(log_path)\n", + "\n", + " # load the data\n", + " all_dat.extend(log2dl(log_path, subj=subj))\n", + "\n", + " df = pd.DataFrame(all_dat)\n", + " \n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Load in all the data" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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resp_map_lureresp_map_targetblock_numtrial_numstim_on_timestim_on_errorrespresp_time_timeresp_time_errorrtcorrectlog_timefilenamein_outnoveltycondsubjlog_num
0FJ002361.4701670.0F2362.5022650.0003911.032098True2363.385215out2646.jpgoutdoorlureoutdoors0010
1FJ012363.3920590.0J2363.9930730.0010330.601014True2364.559602out0031_new.jpgoutdoortargetoutdoors0010
2FJ022364.5728680.0F2365.3636710.0001970.790803True2365.870152out1227.jpgoutdoorlureoutdoors0010
3FJ032365.8744930.0F2366.7145440.0001910.840051True2367.588254out0134_new.jpgoutdoorlureoutdoors0010
4FJ042367.5925580.0F2368.4632090.0002480.870651True2369.152451out2086.jpgoutdoorlureoutdoors0010
\n", + "
" + ], + "text/plain": [ + " resp_map_lure resp_map_target block_num trial_num stim_on_time \\\n", + "0 F J 0 0 2361.470167 \n", + "1 F J 0 1 2363.392059 \n", + "2 F J 0 2 2364.572868 \n", + "3 F J 0 3 2365.874493 \n", + "4 F J 0 4 2367.592558 \n", + "\n", + " stim_on_error resp resp_time_time resp_time_error rt correct \\\n", + "0 0.0 F 2362.502265 0.000391 1.032098 True \n", + "1 0.0 J 2363.993073 0.001033 0.601014 True \n", + "2 0.0 F 2365.363671 0.000197 0.790803 True \n", + "3 0.0 F 2366.714544 0.000191 0.840051 True \n", + "4 0.0 F 2368.463209 0.000248 0.870651 True \n", + "\n", + " log_time filename in_out novelty cond subj log_num \n", + "0 2363.385215 out2646.jpg outdoor lure outdoor s001 0 \n", + "1 2364.559602 out0031_new.jpg outdoor target outdoor s001 0 \n", + "2 2365.870152 out1227.jpg outdoor lure outdoor s001 0 \n", + "3 2367.588254 out0134_new.jpg outdoor lure outdoor s001 0 \n", + "4 2369.152451 out2086.jpg outdoor lure outdoor s001 0 " + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data from each task\n", + "task_dir = os.path.join('data2', 'Taskapalooza')\n", + "\n", + "df_i = load_all_subj_logs(task_dir, 'log_image_test')\n", + "df_w = load_all_subj_logs(task_dir, 'log_word_test')\n", + "df_i.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Some data clean-up" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "# it turns out the cond is easier to visualize as pure and mixed\n", + "def fix_conds(df, type_col):\n", + " # loop over the unique subjects\n", + " usubj = df.subj.unique()\n", + " for s in usubj:\n", + " # loop over their blocks\n", + " ublocks = df.loc[df['subj']==s, 'block_num'].unique()\n", + " for b in ublocks:\n", + " # grab the data for that subj and block\n", + " dfb = df.loc[(df['subj']==s)&(df['block_num']==b)]\n", + " \n", + " # get the unique types in that block\n", + " uval = dfb[type_col].unique()\n", + " if len(uval) > 1:\n", + " # it's mixed\n", + " df.loc[(df['subj']==s)&(df.block_num==b), 'cond'] = 'mixed'\n", + " else:\n", + " # it's the pure\n", + " df.loc[(df['subj']==s)&(df.block_num==b), 'cond'] = 'pure'\n", + "\n", + "# fix the conds in the recog experiments (updated in place)\n", + "fix_conds(df_i, type_col='in_out')\n", + "fix_conds(df_w, type_col='valence')\n", + "\n", + "\n", + "# add in log_rt columns\n", + "df_i['log_rt'] = np.log(df_i['rt'])\n", + "df_w['log_rt'] = np.log(df_w['rt'])\n", + "\n", + "# must make correct an int\n", + "df_i['correct'] = df_i['correct'].astype(np.int)\n", + "df_w['correct'] = df_w['correct'].astype(np.int)\n", + "\n", + "# add in a column for whether they made an 'old' response\n", + "df_i['old_resp'] = (df_i['resp_map_target'] == df_i['resp']).astype(np.int)\n", + "df_w['old_resp'] = (df_w['resp_map_target'] == df_w['resp']).astype(np.int)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Image Recognition Figure\n", + "\n", + "- Recognition memory performance is about more than simply whether you got a response correct\n", + "- There are two different ways we are probing memory: \n", + " - With old (target) items and with new (lure) items\n", + "- We can explore that by plotting the probability of making an `old` response, split out by novelty of the test item" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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meanstdsemcilen
condnoveltyin_out
mixedlureindoor0.1388890.3716300.0129150.025350828.0
outdoor0.1135270.3398900.0118120.023185828.0
targetindoor0.7862320.4265410.0148230.029096828.0
outdoor0.7149760.4684790.0162810.031956828.0
purelureindoor0.1388890.3684120.0090530.0177571656.0
outdoor0.1413040.3688590.0090640.0177791656.0
targetindoor0.7910630.4257970.0104630.0205231656.0
outdoor0.6914250.4829060.0118670.0232751656.0
\n", + "
" + ], + "text/plain": [ + " mean std sem ci len\n", + "cond novelty in_out \n", + "mixed lure indoor 0.138889 0.371630 0.012915 0.025350 828.0\n", + " outdoor 0.113527 0.339890 0.011812 0.023185 828.0\n", + " target indoor 0.786232 0.426541 0.014823 0.029096 828.0\n", + " outdoor 0.714976 0.468479 0.016281 0.031956 828.0\n", + "pure lure indoor 0.138889 0.368412 0.009053 0.017757 1656.0\n", + " outdoor 0.141304 0.368859 0.009064 0.017779 1656.0\n", + " target indoor 0.791063 0.425797 0.010463 0.020523 1656.0\n", + " outdoor 0.691425 0.482906 0.011867 0.023275 1656.0" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get the error corrected by condition and whether they said old\n", + "res = ci_within(df_i, \n", + " indexvar='subj', # column that identifies a subject\n", + " withinvars=['cond', 'novelty', 'in_out'], # list of columns for grouping within subject\n", + " measvar='old_resp') # dependent variable averaging over\n", + "res" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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condnoveltymeanstdsemcilen
in_outindooroutdoorindooroutdoorindooroutdoorindooroutdoorindooroutdoor
0mixedlure0.1388890.1135270.3716300.3398900.0129150.0118120.0253500.023185828.0828.0
1mixedtarget0.7862320.7149760.4265410.4684790.0148230.0162810.0290960.031956828.0828.0
2purelure0.1388890.1413040.3684120.3688590.0090530.0090640.0177570.0177791656.01656.0
3puretarget0.7910630.6914250.4257970.4829060.0104630.0118670.0205230.0232751656.01656.0
\n", + "
" + ], + "text/plain": [ + " cond novelty mean std sem \\\n", + "in_out indoor outdoor indoor outdoor indoor \n", + "0 mixed lure 0.138889 0.113527 0.371630 0.339890 0.012915 \n", + "1 mixed target 0.786232 0.714976 0.426541 0.468479 0.014823 \n", + "2 pure lure 0.138889 0.141304 0.368412 0.368859 0.009053 \n", + "3 pure target 0.791063 0.691425 0.425797 0.482906 0.010463 \n", + "\n", + " ci len \n", + "in_out outdoor indoor outdoor indoor outdoor \n", + "0 0.011812 0.025350 0.023185 828.0 828.0 \n", + "1 0.016281 0.029096 0.031956 828.0 828.0 \n", + "2 0.009064 0.017757 0.017779 1656.0 1656.0 \n", + "3 0.011867 0.020523 0.023275 1656.0 1656.0 " + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# must unstack and reset index to plot properly\n", + "res.unstack().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Performance')" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the results\n", + "ax = res.unstack().reset_index().plot(x='cond', y='mean', yerr='ci', kind=\"bar\")\n", + "#ax.get_legend().remove()\n", + "ax.set_xlabel('Condition')\n", + "ax.set_ylabel('Performance')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Can we improve that plot?\n", + "\n", + "- It looks like we've run up against the max level of complexity Pandas can easily handle.\n", + "- Let's explore the `plotnine` package, which provides a \"Grammar of Graphics\" (port of `ggplot` from R to Python)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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condnoveltyin_outmeanstdsemcilen
0mixedlureindoor0.1388890.3716300.0129150.025350828.0
1mixedlureoutdoor0.1135270.3398900.0118120.023185828.0
2mixedtargetindoor0.7862320.4265410.0148230.029096828.0
3mixedtargetoutdoor0.7149760.4684790.0162810.031956828.0
4purelureindoor0.1388890.3684120.0090530.0177571656.0
5purelureoutdoor0.1413040.3688590.0090640.0177791656.0
6puretargetindoor0.7910630.4257970.0104630.0205231656.0
7puretargetoutdoor0.6914250.4829060.0118670.0232751656.0
\n", + "
" + ], + "text/plain": [ + " cond novelty in_out mean std sem ci len\n", + "0 mixed lure indoor 0.138889 0.371630 0.012915 0.025350 828.0\n", + "1 mixed lure outdoor 0.113527 0.339890 0.011812 0.023185 828.0\n", + "2 mixed target indoor 0.786232 0.426541 0.014823 0.029096 828.0\n", + "3 mixed target outdoor 0.714976 0.468479 0.016281 0.031956 828.0\n", + "4 pure lure indoor 0.138889 0.368412 0.009053 0.017757 1656.0\n", + "5 pure lure outdoor 0.141304 0.368859 0.009064 0.017779 1656.0\n", + "6 pure target indoor 0.791063 0.425797 0.010463 0.020523 1656.0\n", + "7 pure target outdoor 0.691425 0.482906 0.011867 0.023275 1656.0" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we won't need to unstack, but we will still need to reset the index\n", + "res = res.reset_index()\n", + "res" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Plotnine / ggplot\n", + "\n", + "- In plotnine, you start with initializing a figure with the data you're going to plot, specifying the general `aesthetics` of what the plot will look like (i.e., what will go on each axis, what colors should they have, how to split out groups, etc...\n", + "- Then you add in `geoms` that specify how to map your data to specific visual properties of your figure\n", + "- You can also specify rules for how to split out a figure into sub plots\n", + "- Finally, you can customize the labels as needed\n", + "\n", + "Let's build this plot, step by step!" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# start with a basic bar plot (specifying to dodge the position, so they are not stacked)\n", + "p = (pn.ggplot(res, pn.aes('cond', 'mean', fill='in_out'))\n", + " + pn.geom_bar(stat='identity', position=pn.position_dodge(.9))\n", + ")\n", + "p" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# add in separate plots for novelty because they were on top of each other\n", + "p += pn.facet_wrap('~novelty')\n", + "p" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# and now the error bars\n", + "p += pn.geom_errorbar(pn.aes(ymin='mean-ci', ymax='mean+ci', width=0.2), \n", + " position=pn.position_dodge(.9)) # needs to use the same dodge as the bars\n", + "p" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AANXD7af7i4qKFBAQ4LAuKChIhYWFpdpmZWXp5MmTuvfee7VhwwalpqZq/vz5ioqKcvhogMVikcVisS/7+fkpKirKdU/CQ5jNZoef8ByMXcXxGtU8jKnnYuxwO9weUv39/UsF0oKCglLBVboWNr28vDRq1Cj5+PiodevW6tatm/7+9787hNSkpCQlJibal+fMmaPFixe77kl4mNDQUHeXgEpi7G4tPDz8tvvIqYI6UHWcGVPGzliq4nhE7eX2kNq4cWNJUlpampo0aSJJSklJUUxMTKm2zZo1q1CfEydO1NChQ+3Lfn5+ysvLu/1iPZzZbFZoaKjy8/NltVrdXQ6cwNg5Ku8fPo71mocx9Vy3O3aE3NrN7SHV399f3bp105YtWzRt2jRlZmYqOTlZM2fOLNW2ffv2atSokXbu3KlHHnlEqampOnjwoBISEhzaRUZGKjIy0r6cnZ3NP+w3sFqtvB4eirG7NV6fmocx9VyMHW6H2y+ckq7NfEpSfHy8EhMTNXbsWHXs2FGSFBsbq++++07StdmkuXPn6tixYxo1apSWLl2q8ePHq3379m6rHQAAAFXP7TOpkhQcHKxZs2aVuW3Hjh0Oy9HR0XrppZeqoywAAAC4iSFmUgEAAIAbEVIBAABgOIRUAAAAGA4hFQAAAIZDSAUAAIDhEFIBAABgOIRUAAAAGA4hFQAAAIZDSAUAAIDhEFIBAABgOIRUAAAAGA4hFQAAAIZDSAUAAIDhEFIBAABgOIRUAAAAGA4hFQAAAIZDSAUAAIDhEFIBAABgOIRUAAAAGA4hFQAAAIZDSAUAAIDhEFIBAABgOIRUAAAAGA4hFQAAAIZDSAUAAIDhEFIBAABgOIRUAAAAGA4hFQAAAIZDSAUAAIDhEFIBAABgOIRUAAAAGA4hFQAAAIZDSAUAAIDhEFIBAABgOIRUAAAAGA4hFQAAAIbj7e4CqoOvr6/8/PzcXYbbmUwmSVJQUJBsNpubq4EzGLuKCwoKkpfX7b3/Plc1paCKhISEVLjtOdeVgUpwZuyAn6oVIbW4uFjFxcXuLsPtzGazfH19VVBQIKvV6u5y4ATGzlF5bzoLCgqqsRJUhwsXLri7BFTS7Y4dE0y1G6f7AQAAYDiEVAAAABgOIRUAAACGQ0gFAACA4RBSAQAAYDiEVAAAgP9v4cKFCg4OdncZOnfunBYuXKjvv/++1DaTyaRXXnnFDVVVr1pxCyoAAABPcu7cOSUmJqp9+/a68847HbYdPnxYMTExbqqs+hBSAQAAPEjXrl3dXUK14HQ/AABABX377bd64IEHFBwcrNDQUD300EM6ceKEQ5urV69qxYoVuuOOO+Tn56dGjRpp5MiROn/+vCTphx9+0KhRo9SkSRMFBgbqzjvv1PLly3X16lVJUmpqqpo3by5JGjlypEwmk0wmk1JTUyWVfbp/3bp19sdr2rSp5s6dqytXrti3b9iwQSaTSUePHtXAgQMVFBSk1q1ba9OmTa56qW4bIRUAAKAC0tLS1KNHD2VmZmrjxo364x//qOPHj6tHjx7Kysqyt5s6dapmzpypIUOG6KOPPtLq1asVEhKiixcvSpLS09PVtm1brVmzRrt379ZTTz2lF154QYsWLZIkRUZG6r333pMkLVmyRIcPH9bhw4cVGRlZZl1vvPGGJk6cqN69e+vDDz/UpEmTtHTpUk2cOLFU27i4OPXv318ffPCBOnbsqPj4+DI/92oEnO4HAACogFdffVXFxcX67LPPVL9+fUlSly5d1Lp1a61evVoLFy7U8ePH9Yc//EGLFy/W7Nmz7fsOHz7c/nufPn3Up08fSZLNZlP37t116dIlrVq1SvPnz5efn5/uvvtuSVLr1q3LPb1vtVr1wgsvaOTIkVq9erUkacCAATKZTEpISFBCQoJatGhhbz9lyhRNnjxZ0rWPDezatUvvvfdeqc+9GgEzqQAAABVw4MAB9e7d2x5QJSkmJkb33XefDhw4IEnat2+fbDabxo8ff9N+ioqKtGDBArVq1Up+fn7y8fFRQkKCLBaLfba1on744QdlZ2frkUcecVg/evRo2Ww2HTx40GF9//797b+HhISoSZMmOn36tFOPWV0IqQAAABWQl5enRo0alVrfqFEj5ebmSpJycnLk7e2tBg0a3LSf3/3ud1q2bJkmTJig3bt36+uvv9bcuXMlXQuwztZ0vYaf1iTJXtd1derUcVj29fV1+jGrC6f7AQAAKiAiIkKZmZml1mdkZCgiIkKSVLduXV25ckVnz569aVDduXOnJk6cqN/97nf2dbt27ap0TZJK1ZWRkeGw3RMxkwoAAFAB3bt3V3JysnJycuzr0tLSdOjQIfXo0UOS1Lt3b5lMJq1fv/6m/RQWFsrX19e+bLVatX37doc217ffapazbdu2ql+/vnbs2OGw/u2335bJZFL37t0r9uQMiJlUAACAG1itVr3zzjul1j/zzDNav369+vfvr4SEBFmtVi1YsEARERF6+umnJUlt2rTRpEmTNHfuXOXm5qpPnz66dOmSdu3apYULF6px48bq16+f3nzzTd15552qX7++Vq9ercuXLzs8VqNGjVSnTh1t27ZNzZs3l5+fnzp06OAQbiXJbDZr/vz5mjp1qurXr68HH3xQR48e1YIFC/T444/bb2XliW4rpNpsNv3zn/9URkaGCgsLVbduXbVp08ajp5YBAEDtVlRUpJEjR5Zav379eu3fv1/Tp0/XuHHj5OXlpV69emn58uUOF1OtWrVKzZs315tvvqlXX31VdevW1a9+9SuFhIRIunbLqEmTJmnq1KkKDAxUfHy8hg0bpgkTJtj78PLy0ltvvaWEhAT16dNHly9fVkpKipo1a1aqrilTpsjHx0evvvqqkpKS1LBhQ82YMUMLFy6s8temOplsNpvNmR2sVqs+/vhjbdy4Ufv27dOFCxd0Yxcmk0l33HGHRo4cqfj4eEN8bVd2dra7SzAEs9ms8PBw5eXlyWq1urscOIGxc1SvXr2bbquK4/3c9Cm33YcRzJ2+yN0lVIlVjepUuC1jZyzOjF1ZyjvWUfM59ZnUbdu2qW3btho7dqzMZrMWLlyo5ORkHTt2TMePH9dXX32lbdu2aeDAgdq5c6dat26tCRMm6MyZM66qHwAAADWQU6f7ExMTNWfOHI0aNUqBgYFltuncubNiY2O1bNkyHTt2TCtXrtSmTZs0a9asKikYAAAANZ9TIfWf//ynTCZThdt36NBBb731lpz8RAEAAABqOadCqjMBtSr2AwAAqA4n4ke5pN9WG7bfuhHK5FRI3b9/v1Od33///U61BwAAACQnQ2rPnj1lMpnsp+9vnCG12WylZky5ChkAAACV4VRI/frrr+2/nz17Vk899ZTuv/9+jRgxQg0bNlRmZqZ27typAwcOaN26dVVeLAAAAGoHp0Jqp06d7L+PGDFCo0aN0rJlyxzaDBs2TNOnT9e6des0cODAqqkSAAAAtYpT90m90Z49e9S/f/8ytw0YMEB79+6tdFEAAAA1Wbt27SqVldauXauePXtWfUEGVOmQGhwcrOTk5DK3ff755woODq50UQAAADXZd999p759+7q7DENz6nT/jZ5++mnNnz9fmZmZevjhh9WgQQOdPXtW77//vjZv3qzExMSqrBMAAAAuUlJSIh8fH3eX4aDSM6lz587V66+/rs8//1zDhg1T9+7dNWzYMH3++edauXKl5s6dW5V1AgAA1BjNmjXTp59+qoULF2r48OGaMGGCwsLC1KpVK4ePAZw6dUp9+vRRSEiIunXrppMnTzr0c+TIEXXt2lVhYWHq0KGDdu/ebd+Wn5+vJ554Qg0bNlR0dLSmT5+u4uJiSdJf//pXNWrUSCtWrFBUVJSGDh1aPU/cCZUOqZI0ZcoUnTp1SqmpqTp06JBSU1OVlpamqVOnVlV9AAAANdrHH3+sIUOGKDc3V08//bSeeOIJ+7YxY8aobdu2ysrK0uuvv64//elP9m15eXl64IEHNH78eOXk5OjFF1/UiBEjdOLECUnStGnTdObMGf3rX//SkSNHtG/fPr344ov2/bOzs3Xy5En95z//0XvvvVd9T7iCbiukSpKXl5eaNm2qLl26qGnTpvLyuu0uAQAAao1f/vKXeuihh2Q2m/XYY48pLS1N2dnZOnXqlA4dOqQXX3xR/v7+6tSpk8aOHWvfb9euXYqJidGECRPk7e2twYMHq3///tq+fbuuXr2qbdu26eWXX1adOnUUFRWl+fPna/Pmzfb9bTabve+AgAB3PPVyOfWZ1BUrVlS4rclk0nPPPed0QQAAALVJo0aN7L8HBgZKki5evKiMjAyFhYUpLCzMvj0mJkbffPONJCk9PV3NmjVz6KtZs2ZKT09XVlaWiouLHbZf33Zd3bp17Y9nRE6F1OnTp1e4LSEVAACg8qKionT+/Hnl5+crNDRU0rXPqF7XuHHjUp9RTU1N1T333KN69erJ19dXJ0+eVIcOHezbGjdubG9r9LPfTlV39erVCv/nzFeiXrx4US+//LIeeeQRxcfHO3zo92aSk5M1dOhQffLJJ848BQAAAI/QtGlTde3aVXPmzNHly5f1zTffaMuWLfbtgwYNUmpqqjZu3KgrV67ok08+0WeffabY2FiZzWaNGjVKCQkJOn/+vCwWixYtWqS4uDg3PiPnGCJCJyUlyWq1av369Zo3b562bNmiY8eO3bR9fn6+3nnnHcXExFRjlQAAANVr69at+u6771SvXj1NmTLF4aKqiIgI7dq1S6tXr1bdunU1c+ZMvf3222rTpo0k6fXXX1fdunXVpk0bderUSffff79mz57trqfitErfJ1WSiouLtX37dh04cEC5ubmKiIjQ/fffr0ceeUS+vr4V6qOoqEgHDx7UypUrFRgYqJYtW6p3797au3evfXr6p9566y0NGzZMf/3rX2+nfAAAALdITU2VJD3wwAMO6/39/WWz2ezLzZo101/+8peb9vPLX/5SR44cKXNbWFiYNmzYUOa2nj17KiMjw7miq1mlZ1LPnj2rTp06KT4+Xnv37tWZM2e0d+9ePfbYY7rnnnt09uzZCvVz/QO8TZs2ta9r0aJFqc9YXPePf/xDZ86cUb9+/SpbOgAAAAyu0jOp06dPV05Ojg4dOqSuXbva13/11VcaPny4ZsyYoY0bN96yn6KiolK3PQgKClJhYWGptiUlJVq7dq2ef/55mUymm/ZpsVhksVjsy35+foqKiqrI06rRzGazw094Dsau4niNah7G1HMxdrgdlQ6pu3fv1quvvuoQUCWpS5cuWrJkiZ5//vkK9ePv718qkBYUFJR5v653331Xd911l1q2bFlun0lJSQ5fyzpnzhwtXry4QvXUBtevEITnYexuLTw8/Lb7yKmCOlB1nBlTxs5YquJ4RO1V6ZB66dIl1a1bt8xtdevW1aVLlyrUz/VbIaSlpalJkyaSpJSUlDIvijp27JhSUlLsn0W9dOmSTpw4oR9++MHhdlcTJ050+HovPz8/5eXlVaiemsxsNis0NFT5+flO3X0B7sfYOSrvHz6O9ZqHMfVctzt2hNzardIhtVOnTnrttdc0YMAAh+l8q9Wq1157TZ06dapQP/7+/urWrZu2bNmiadOmKTMzU8nJyZo5c2aptr/73e9UUlJiX3755ZfVpUsX9e/f36FdZGSkIiMj7cvZ2dn8w34Dq9XK6+GhGLtb4/WpeRhTz8XY4XZUOqQuWbJE/fr1U4sWLfTwww+rUaNGyszM1AcffKDMzEx9/vnnFe5r4sSJWrVqleLj4xUYGKixY8eqY8eOkqTY2FgtWLBA7dq1c/jGBUny8fFRUFAQp0ABAABqmEqH1B49eujgwYNavHixtm3bpry8PEVERKh79+5KSEjQL37xiwr3FRwcrFmzZpW5bceOHTfdb8mSJU7XDQAA8FOtNmx3dwn4idu6T2qnTp303nvvVVUtAAAAgCSDfOMUAAAAcCOnZlJHjBihhIQE3X333RVqX1hYqHXr1ikoKEhPPvlkpQoEAABwtVHfnnBJv9vbt3JJv7WBUyG1WbNm6tatm372s59pxIgR6tatm37+858rIiJC0rWvSU1JSdHf//53ffLJJ/rwww/Vpk0brV271iXFAwAAoGZy6nT/K6+8oh9//FFDhgzRm2++qV69eql+/fry8fFRQECAAgICdOeddyo+Pl75+fnasmWLvv766wrfjgoAAACQKnHhVOPGjfXCCy/ohRde0IkTJ/S3v/1NFotFRUVFioiIUNu2bXXvvfcqMDDQFfUCAACgFnA6pH733XdKSkpSSkqKGjdurOHDh2vUqFGuqA0AAAC1lFMh9csvv1SfPn105coV1atXT7m5uXrzzTe1evVqTZo0yVU1AgAAoIJmzZqljIwMbdiwwd2l3BanPpO6cOFC3XnnnUpNTVVmZqZycnL08MMPa+7cua6qDwAAAP9fz549a80F6U6F1GPHjmnevHlq0qSJJCk0NFTLly9Xbm6u0tLSXFIgAAAA3K+kpKRaH8+pkJqdna3o6GiHddcDa3Z2dtVVBQAAUIMdP35cffv2VXh4uNq2bWs/NR8fH+/wVfE//PCDTCaTJCkhIUEHDhzQs88+q+DgYI0bN07StUnEzp07KyQkRIMHD1ZeXp7DY+3evVsdOnRQWFiYunbtqiNHjti3WSwW/frXv1a9evXUvHlzvfzyy7LZbJKkDRs2qGvXrpoxY4bq16+vadOmufIlKcXpb5y6/kIBAADAeSUlJRoyZIjuu+8+ZWZmauPGjXr22Wf1xRdflLvf4sWL1aNHD61cuVIXL17U5s2bVVJSooceekjDhg1Tbm6unnnmGW3evNm+z48//qgRI0boxRdfVE5OjsaPH6+BAwfag+yYMWNUp04dnTp1Sp9++qnWrFmjTZs22ff/29/+pgYNGujMmTNasWKFa16Qm3A6pPbq1UuhoaH2/8LDwyVJPXr0cFgfFhZW5cUCAAB4uq+++kq5ublasGCBfH191bVrV8XHxzuEy4o6fPiwCgoKNGvWLPn4+Kh///4aMGCAffvbb7+tAQMGaPDgwfL29taECRPUpEkT7dq1S6dPn9YXX3yh5cuXKzAwUG3bttVzzz3nUEeDBg00ffp0+z3xq5NTV/cvWLDAVXUAAADUCunp6WrSpInMZrN9XbNmzbRnzx41bNjQqb7OnDmjxo0by8vr/+YdY2JidO7cOftjNWvWzGGfZs2aKT09Xenp6QoLC7NPON647bro6Gi3nUUnpAIAAFSjxo0b6/Tp07JarfagmpqaqsaNG8vf31+XLl2yt83IyHDY96eBMSoqSunp6bp69ao9qJ46dUqhoaH2xzp69KjDPqmpqRoxYoQaN26s8+fP6/z58/Yz4NfruO7G8Fvd3PfIAAAAtVCXLl1Up04dvfjiiyouLtaRI0e0ceNGjR07Vnfffbd2796trKws5ebm6uWXX3bYt2HDhvr3v/9tX/7lL3+pwMBALV26VCUlJdq7d68+/fRT+/bY2Fjt2bNHe/bs0ZUrV/TWW2/p1KlTGjRokKKjo3X//fdr5syZKiws1PHjx/Xaa68pLi6u2l6L8hBSAQAAqpGPj48++ugj7du3Tw0aNFBcXJxeeeUV9erVS3FxcerSpYtatWqlbt266de//rXDvs8884w+/PBDhYeH67HHHpOPj48++OADvfPOOwoPD9err75qv+pfktq0aaPt27dr+vTpqlu3rtauXatdu3YpIiJCkrR161ZlZmYqOjpa/fv314QJE/Too49W6+txMybb9fsM1GDcHusas9ms8PBw5eXlyWq1urscOIGxc1SvXr2bbquK4/3c9Cm33YcRzJ2+yN0lVIlVjepUuC1jZyzOjF1ZyjvWq9qob0+4pN/t7Vu5pN/agJlUAAAAGA4hFQAAAIZDSAUAAIDhEFIBAABgOIRUAAAAGA4hFQAAAIbj1DdOAQAA1ETcKsp4mEkFAACA4RBSAQAAYDiEVAAAABgOIRUAAACGQ0gFAACA4RBSAQAAYDiEVAAAABgOIRUAAACGQ0gFAACA4RBSAQAAYDiEVAAAABgOIRUAAACG4+3uAqqDr6+v/Pz83F2G25lMJklSUFCQbDabm6uBMxi7igsKCpKX1+29/z5XNaWgioSEhFS47TnXlYFKcGbsgJ+qFSG1uLhYxcXF7i7D7cxms3x9fVVQUCCr1erucuAExs5ReW86CwoKqrESVIcLFy64uwRU0u2OHRNMtRun+wEAAGA4hFQAAAAYDiEVAAAAhkNIBQAAgOEQUgEAAGA4hFQAAAAYDiEVAAAAhkNIBQAAgOEQUgEAAGA4hFQAAAAYDiEVAAAAhkNIBQAAgOEQUgEAAGA4hFQAAAAYDiEVAAAAhkNIBQAAgOEQUgEAAGA4hFQAAAAYDiEVAAAAhkNIBQAAgOEQUgEAAGA4hFQAAAAYDiEVAAAAhkNIBQAAgOEQUgEAAGA4hFQAAAAYDiEVAAAAhkNIBQAAgOEQUgEAAGA4hFQAAAAYDiEVAAAAhkNIBQAAgOEQUgEAAGA4hFQAAAAYjre7C5CkixcvavXq1Tp69KgCAgIUGxurQYMGlWr3ww8/aNu2bTpx4oQkqW3btnryyScVFRVV3SUDAADAhQwxk5qUlCSr1ar169dr3rx52rJli44dO1aqXUFBgfr27at169Zpw4YNatq0qRYtWuSGigEAAOBKbg+pRUVFOnjwoOLi4hQYGKiWLVuqd+/e2rt3b6m2nTp1Uo8ePRQUFCQfHx89/PDDOn36tPLz891QOQAAAFzF7SE1PT1dktS0aVP7uhYtWujkyZO33Pfbb79VeHi4QkNDXVYfAAAAqp/bP5NaVFSkgIAAh3VBQUEqLCwsd7+MjAwlJSVp4sSJpbZZLBZZLBb7sp+fH59blWQ2mx1+wnMwdhXHa1TzMKaei7HD7XB7SPX39y8VSAsKCkoF1xtlZ2dr/vz5GjFihLp3715qe1JSkhITE+3Lc+bM0eLFi6uuaA/HzLPnYuxuLTw8/Lb7yKmCOlB1nBlTxs5YquJ4RO3l9pDauHFjSVJaWpqaNGkiSUpJSVFMTEyZ7XNycpSQkKD+/fvroYceKrPNxIkTNXToUPuyn5+f8vLyqrhyz2M2mxUaGqr8/HxZrVZ3lwMnMHaOyvuHj2O95mFMPdftjh0ht3Zze0j19/dXt27dtGXLFk2bNk2ZmZlKTk7WzJkzS7XNycnRnDlz1LNnT40YMeKmfUZGRioyMtK+nJ2dzT/sN7BarbweHoqxuzVen5qHMfVcjB1uh9svnJJk/1xpfHy8EhMTNXbsWHXs2FGSFBsbq++++06S9Nlnn8lisej9999XbGys/b+srCy31Q4AAICq5/aZVEkKDg7WrFmzyty2Y8cO+++jR4/W6NGjq6ssAAAAuIkhZlIBAACAGxFSAQAAYDiEVAAAABgOIRUAAACGQ0gFAACA4RBSAQAAYDiEVAAAABgOIRUAAACGQ0gFAACA4RjiG6dqm/z8fOXn51e4fWhoqEJDQ11YEQAAgLEQUt1g7dq1WrZsWYXbz5gxQzNnznRhRQAAAMZCSHWDSZMmacyYMQ7rLBaLBg0apN27dysyMtJhG7OocAVnZvSZzQcAVDdCqhuU9w9+ZGSkoqOjq7kiOKOmhDtnZvSZzQcAVDdCKuCkmhLufjqjz2w+AMBICKmAk2pKuLvZLC+z+QAAIyCkAk4i3AEA4HrcJxUAAACGQ0gFAACA4RBSAQAAYDh8JhXwEKO+PeHS/guyrt1Wa15WvoK8z7nscVY1quOyvgEANQchtYLOTZ/i0v7zLxZc+7lons4FB7nsccI3bK+yvjzl611rythp+iLX9Q0AgMEQUlFpfL0rAABwFUIqKo2vdwUAAK5CSEWl8fWuAADAVbi6HwAAAIbDTCpQSxVfuKCSixfsy4VnMx1+3sgnOES+ISHVVhsAAIRUwEkXiot1objEvpx5qdDh541CfH0U4utbbbU54/jm9fruD2+UWp8cF1tqXbvfTFX7ydOqoywAACQRUgGnrf/+uN743+9KrY/9JLnUuqkd22naXe2royyntRn3uJoPG1Ghtj7BzKICAKoXIRVw0uN3ttGIVs0r1DbE18fF1VSebwin8AEAxkVIdYOfni6WPPeUcW0U4uvLeAAA4GKEVDe42eliyfWnjPlqTQAA4AkIqW7gzOliydinjAEAAFyBkOoGnC4GAAAoHzfzBwAAgOEQUgEAAGA4hFQAAAAYTq34TKqvr6/8/Pxuq49zVVMKqkiIE/f3POe6MlAJzoxdZQQFBcnL6/bef5+rmlJQRTjePZerj3fUbLUipBYXF6u4uNjdZaAKXbhw4daNYEhVMXblveksKCi47f5hLBzvnut2x+52J5jg2TjdDwAAAMOpFTOpcI3iCxdUctHxXXLh2UyHnzfyCeZrOAEAQMUQUlFpxzev13d/eKPMbclxsaXWtfvNVLWfPM3VZQEAgBqAkIpKazPucTUfNqLC7X2CmUUFAAAVQ0hFpfmGcPoeAAC4BhdOAQAAwHAIqQAAADAcQioAAAAMh5AKAAAAwyGkAgAAwHAIqQAAADAcQioAAAAMh5AKAAAAwyGkAgAAwHAIqQAAADAcQioAAAAMh5AKAAAAwyGkAgAAwHAIqQAAADAcQioAAAAMh5AKAAAAwyGkAgAAwHAIqQAAADAcQioAAAAMh5AKAAAAwyGkAgAAwHAIqQAAADAcQioAAAAMh5AKAAAAwyGkAgAAwHAIqQAAADAcQioAAAAMh5AKAAAAwyGkAgAAwHC83V2Asy5evKjVq1fr6NGjCggIUGxsrAYNGuTusgAAAFCFPC6kJiUlyWq1av369bJYLJo/f76io6PVoUMHd5cGAACAKuJRp/uLiop08OBBxcXFKTAwUC1btlTv3r21d+9ed5cGAACAKuRRITU9PV2S1LRpU/u6Fi1a6OTJk+4qCQAAAC7gUaf7i4qKFBAQ4LAuKChIhYWFDussFossFot92c/PT1FRUdVSI6qH2Wx2dwmoJFePHX8bNQ9j6rkYO9wOjwqp/v7+pQJpQUFBqeCalJSkxMRE+/KcOXO0ePHi23rs8A3bb2t/o6gZz8I5jF3tEh4efvt98DfjsRg7oObwqJDauHFjSVJaWpqaNGkiSUpJSVFMTIxDu4kTJ2ro0KH2ZT8/P+Xl5VVfoQZlNpsVGhqq/Px8Wa1Wd5cDJzB2jsoLohzr1/A347kYu/9TFW864bk8KqT6+/urW7du2rJli6ZNm6bMzEwlJydr5syZDu0iIyMVGRlpX87Ozq71B/qNrFYrr4eHYuxujdfHEX8znouxQ23nUSFVujZLumrVKsXHxyswMFBjx45Vx44d3V0WAAAAqpDHhdTg4GDNmjXL3WUAAADAhTzqFlQAAACoHQipAAAAMBxCKgAAAAyHkAoAAADDIaQCAADAcAipAAAAMBxCKgAAAAyHkAoAAADDIaQCAADAcAipAAAAMBxCKgAAAAyHkAoAAADDMdlsNpu7i0D1sFgsSkpK0sSJExUZGenucuAExg7O4m/GczF2wDXMpNYiFotFiYmJslgs7i4FTmLs4Cz+ZjwXYwdcQ0gFAACA4RBSAQAAYDiE1FokMjJSCxYs4DNOHoixg7P4m/FcjB1wDRdOAQAAwHCYSQUAAIDhEFJroIULF+qzzz6r8n6XLVumrVu3Vnm/ACqP4x1ATeXt7gJQ9RYuXOjuEgBUE453ADUVM6mAB7ty5Yq7SwBQTTjeUdswk+ohnnzySQ0aNEj79+9Xenq6OnbsqGeffVYbNmzQl19+qfDwcD333HNq06aN5syZox49emjgwIFat26d0tPTtXDhQplMJr3//vvat2+fVqxYIW9vb33wwQfas2eP8vPz1bZtWz399NOqV6+eJOnYsWNKSkpSdna2unbtqpKSEje/CjXHk08+qQEDBmj//v3KyspSx44dNXXqVKWkpGjZsmXatGmTve306dM1cOBA9enTR8nJyfrkk0/Url07JScn67777tNvfvObcscRnofjvWbheAcqh5lUD/Lll19q3rx52rBhgzIyMjRjxgx16dJFW7ZsUffu3ZWUlFRqn/j4eOXm5urjjz9WSkqKduzYoenTp8vHx0e7du3S/v37lZiYqE2bNqlly5ZaunSpJOnChQtavHixRowYoa1bt6pDhw46cuRIdT/lGm3fvn1KSEjQW2+9pZKSEr355psV2u/EiRMKCwvThg0bNH78+HLHEZ6L471m4XgHnEdI9SCDBw9W3bp1FRQUpE6dOikiIkKdO3eW2WxWjx49lJKSoqtXrzrs4+vrq9/+9rfaunWrXnrpJY0ePVoxMTGSpE8++URxcXFq2LChvL29NXr0aJ04cUJZWVn6+uuvFRUVpV69eslsNqtPnz72/VA1Bg8erEaNGikwMFDjxo3TgQMHSo1fWerUqaNhw4bJ29tbfn5+5Y4jPBfHe83C8Q44j9P9HqROnTr23/38/EotX7lypczPLDVr1kwtW7bUiRMnNGDAAPv6zMxMLV26VF5e//dexcvLS9nZ2crNzVX9+vUd+mnQoEHVPRk4nJ6rX7++rly5ovz8/FvuV7duXZlMJvtyeeP40zGE5+B4r1k43gHnEVJrgX379uns2bNq3bq1Nm3apAkTJki69j/KyZMn6+c//3mpfSwWS6l35llZWWrevHm11FwbZGdn23/PysqSt7e3GjRooMuXLzu0O3funMPyjf9gSeWPI2ofjndj4ngHnMfp/houIyNDf/rTn/T888/r2Wef1RdffKFvvvlGkjRw4EBt3rxZFotFknTx4kV9+eWXkqR77rlHZ86c0RdffCGr1aq//OUvOnnypNueR020e/duZWRk6NKlS/bPGTZp0kRXr17VoUOHZLVatWvXLuXk5JTbT3njiNqF4924ON4B5zGTWoNZrVatWLFCDz74oH72s59JkiZPnqzXXntNr7/+uoYMGSKTyaTf//73ysnJUVBQkO666y51795doaGhmj17tt58802tWbNGXbt2VefOnd38jGqWXr16afHixcrKylKHDh00YcIEBQYGavLkyVq3bp1Wr16tgQMHqmXLluX2U944ovbgeDc2jnfAeSabzWZzdxFAbfPkk0/qN7/5jTp16uTuUgC4GMc7UDmc7gcAAIDhEFIBAABgOJzuBwAAgOEwkwoAAADDIaQCAADAcAipAAAAMBxCKgAAAAyHkAoAAADDIaQCKNfu3bv1wAMPqG7duvL19VVMTIwmT56sf//739Xy+O+8845MJpNSU1Pt60wmk1555RX78oYNG7R169ZS+8bHx6t9+/bVUSYAoIrxtagAbmru3LlavHixhg0bpqSkJDVo0ECpqanauHGj+vbtq5SUFLfUdfjwYcXExNiXN2zYoODgYI0ZM8ah3bx581RQUFDd5QEAqgAhFUCZPv30Uy1evFizZ8/WkiVL7Ovvv/9+Pfroo/roo4/cVlvXrl0r1O5W34MOADAuTvcDKNMrr7yihg0bKjExscztDz74oCTp6tWrWrJkiZo3by4/Pz+1bt1aK1eudGi7cOFCBQcH69ixY+revbsCAwPVvn177dmzx6FdSUmJnn32WUVERCgsLEzjx48vcyb0xtP9PXv21BdffKFdu3bJZDLJZDJp4cKFkso+3f/tt9/qgQceUHBwsEJDQ/XQQw/pxIkTpfpfunSpFixYoIYNG6pevXp6/PHHmZUFgGpESAVQypUrV3Tw4EH17dtXPj4+5badMWOG5s2bp7i4OH300Ud6+OGH9dxzz+n3v/+9Q7uSkhLFxcUpPj5e77//vurVq6fhw4crJyfH3mb27Nlas2aNZsyYoR07dujKlStKSEgo9/HXrFmju+++W926ddPhw4d1+PBhPfnkk2W2TUtLU48ePZSZmamNGzfqj3/8o44fP64ePXooKyvLoe2qVat04sQJbdy4UfPmzdPWrVtLPScAgAvZAOAnMjIybJJss2bNKrddVlaWzcfHxzZjxgyH9U899ZQtKCjIduHCBZvNZrMtWLDAJsm2a9cue5sff/zRJsm2efNmm81ms+Xk5NgCAgJs8+bNc+jrvvvus0mypaSk2NdJsi1btsy+/Ktf/co2ePDgUvU99thjtnbt2tmXn3vuOVtgYKDt7Nmz9nWpqak2Hx8f24IFCxz679y5s0NfY8eOtbVs2bLc1wMAUHWYSQVQis1mk3TttHd5vvrqK5WUlOiRRx5xWD969GgVFBTom2++sa/z8vJS37597cutWrWSr6+vTp8+LUn6xz/+ocLCQg0bNsyhr+HDh9/Wc7nRgQMH1Lt3b9WvX9++LiYmRvfdd58OHDjg0LZ///4Oy3feeae9VgCA6xFSAZRSr149+fv769SpU+W2y8vLkyQ1atTIYf315dzcXPu6gIAA+fr6OrTz8fFRUVGRJMlisUiSGjRo4NCmYcOGlXgGN6/3p7Ver/fGWiWpTp06Dsu+vr66fPlyldUCACgfIRVAKd7e3urevbv27t2rkpKSm7aLiIiQJGVmZjqsz8jIcNheEZGRkZKks2fPOqz/ad+3IyIiosz+MjIynKoVAOB6hFQAZfrtb3+rzMxMvfDCC2Vu//jjj3XvvffKx8dHO3bscNj29ttvKygoSL/4xS8q/Hg///nPFRAQoPfff99h/bvvvnvLfX19fe0zsuXp3r27kpOTHS7WSktL06FDh9SjR48K1woAcD3ukwqgTA888IASEhK0aNEi/fOf/9To0aPVoEEDnTx5Ups3b9bx48eVkpKiadOm6ZVXXpGfn5+6deum5ORkJSUlKTExUUFBQRV+vIiICE2aNEkvvfSSAgIC9Itf/EJbt27VyZMnb7nvHXfcoY0bN+qjjz5SZGSkoqKiFBUVVardc889p/Xr16t///5KSEiQ1WrVggULFBERoaefftqp1wcA4FrMpAK4qUWLFunjjz/WhQsXNGHCBPXu3VsJCQlq0qSJdu3aJUlaunSpEhMTtXHjRg0ZMkTvvvuuli9frnnz5jn9eC+99JImTZqkpUuXKjY2ViaTSYsWLbrlfjNnzlS3bt306KOPqnPnzlq3bl2Z7Zo0aaL9+/erXr16GjdunJ544gm1atVKBw4ccLiYCgDgfibb9ct4AQAAAINgJhUAAACGQ0gFAACA4RBSAQAAYDiEVAAAABgOIRUAAACGQ0gFAACA4RBSAQAAYDiEVAAAABgOIRUAAACGQ0gFAACA4RBSAQAAYDiEVAAAABjO/wOTz0uXA5XYCQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# finally fix up the labels\n", + "p += pn.labs(x=\"Condition\", y = \"P(old)\", fill='Location')\n", + "p" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What if we wanted points instead of bars?" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = (pn.ggplot(res, pn.aes('cond', 'mean', fill='in_out'))\n", + " + pn.geom_errorbar(pn.aes(ymin='mean-ci', ymax='mean+ci', width=0.2), \n", + " position=pn.position_dodge(.9))\n", + " + pn.geom_point(position=pn.position_dodge(.9), size=4)\n", + " + pn.facet_wrap('~novelty')\n", + " + pn.labs(x=\"Condition\", y = \"P(old)\", fill='Location')\n", + " )\n", + "p" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Probability Theory\n", + "\n", + "At the core of probability theory are the mathematical functions determining the probability of the potential outcomes of an experiment. \n", + "\n", + "In statistics, these distributions represent the models that attempt to describe the observed data. The equations take in parameters that determine the shape of the probability distributions.\n", + "\n", + "***Note: The area under a probability distribution must equal 1.0!***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Uniform distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# x values to evaluate\n", + "x = np.linspace(-5, 5, 100)\n", + "\n", + "# uniform distribution between 0 and 1\n", + "d = dists.uniform(loc=0.0, scale=1.0)\n", + "plt.plot(x, d.pdf(x), lw=3)\n", + "\n", + "# uniform distribution between -1 and 1\n", + "d = dists.uniform(loc=-1, scale=2.0)\n", + "plt.plot(x, d.pdf(x), lw=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The Normal / Gaussian distribution\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "d = dists.norm(loc=0.0, scale=1.0)\n", + "x = np.linspace(-5, 5, 100)\n", + "plt.plot(x, d.pdf(x), lw=3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### What's the probability of observing a value greater than 0?" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "d = dists.norm(loc=0.0, scale=1.0)\n", + "x = np.linspace(-5, 5, 100)\n", + "plt.plot(x, d.pdf(x), lw=3)\n", + "plt.fill_between(x, d.pdf(x), where=x>0.0, alpha=.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Strength theory of memory\n", + "\n", + "- Assumes that we have a baseline familiarity with each item\n", + " - CAT would be strong and have a higher value\n", + " - ALABASTER would be weak and have a lower value\n", + " - VACUUM would have a value somewhere in the middle\n", + "- Combining all stimuli gives you a normal distribution of baseline familiarity (i.e., memory strength)\n", + " - This has two parameters, the mean and standard deviation of the normal distribution" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The effect of studying and item\n", + "\n", + "- In strength theory, the process of studying an item adds some amount to that baseline familiarity for some item\n", + "- Thus, studying a set of items shifts their strength distribution to the right!" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mu = 0.0\n", + "sigma = 1.0\n", + "alpha = 2.0\n", + "\n", + "# xrange to plot\n", + "x = np.linspace(-5, 7.5, 100)\n", + "\n", + "# distribution of new items\n", + "y1 = dists.norm(loc=mu, scale=sigma).pdf(x)\n", + "plt.plot(x, y1, lw=3)\n", + "\n", + "# distribution of old (studied) items\n", + "y2 = dists.norm(loc=mu+alpha, scale=sigma).pdf(x)\n", + "plt.plot(x, y2, lw=3)\n", + "\n", + "plt.legend(['New', 'Old'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Signal Detection Theory\n", + "\n", + "- Developed by radar operators in the 1940s to help make decisions under uncertainty\n", + "- Casts the decision as detecting signal versus noise\n", + "- Adds one more parameter, the decision criterion (C)\n", + "\n", + "![SDT](figs/signal_detection.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Bias: How do we pick our decision criterion?\n", + "\n", + "- You don't have control over the signal and noise distributions, but you do have control over the criterion\n", + "- Questions:\n", + " - Where should you put your criterion to make the fewest errors?\n", + " - Where should you put your criterion if I said you would get $10 for every hit, and take away nothing for false alarms?\n", + " - How about if you had to pay me every time you made a false alarm?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculating sensitivity\n", + "\n", + "- Under assumptions of equal variance for both the signal and noise distributions, the d' (d-prime) is the measure of sensitivity\n", + "\n", + "$$d' = ((\\mu + \\alpha) - \\mu) / \\sigma$$\n", + "$$d' = \\alpha / \\sigma$$\n", + "\n", + "- Thus, $d'$ is the difference between the two distributions in units of the standard deviation\n", + "- Note, this is independent of the criterion\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "def calc_dprime(n_hits, n_targets, n_false_alarms, n_lures):\n", + " # calculate corrected hit rate and false alarm rate (to avoid zeros)\n", + " hr_trans = (n_hits+.5)/(n_targets+1)\n", + " far_trans = (n_false_alarms+.5)/(n_lures+1)\n", + " \n", + " # calculate dprime\n", + " Z = dists.norm.ppf\n", + " dprime = Z(hr_trans) - Z(far_trans)\n", + " return dprime\n", + "\n", + "def calc_c(n_hits, n_targets, n_false_alarms, n_lures):\n", + " # calculate corrected hit rate and false alarm rate (to avoid zeros)\n", + " hr_trans = (n_hits+.5)/(n_targets+1)\n", + " far_trans = (n_false_alarms+.5)/(n_lures+1)\n", + " \n", + " # calculate bias\n", + " Z = dists.norm.ppf\n", + " c = -(Z(hr_trans) + Z(far_trans)) / 2\n", + " return c\n" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sumcountmean
subjcondin_outnovelty
s001mixedindoorlure0360.000000
target29360.805556
outdoorlure2360.055556
target28360.777778
pureindoorlure2720.027778
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" + ], + "text/plain": [ + " sum count mean\n", + "subj cond in_out novelty \n", + "s001 mixed indoor lure 0 36 0.000000\n", + " target 29 36 0.805556\n", + " outdoor lure 2 36 0.055556\n", + " target 28 36 0.777778\n", + " pure indoor lure 2 72 0.027778" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iperf = df_i.groupby(['subj', 'cond', 'in_out', 'novelty'])['old_resp'].agg(['sum', 'count', 'mean'])\n", + "iperf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjcondin_outsum_luresum_targetcount_lurecount_targetmean_luremean_target
0s001mixedindoor02936360.0000000.805556
1s001mixedoutdoor22836360.0555560.777778
2s001pureindoor26172720.0277780.847222
3s001pureoutdoor66272720.0833330.861111
4s002mixedindoor52536360.1388890.694444
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" + ], + "text/plain": [ + " subj cond in_out sum_lure sum_target count_lure count_target \\\n", + "0 s001 mixed indoor 0 29 36 36 \n", + "1 s001 mixed outdoor 2 28 36 36 \n", + "2 s001 pure indoor 2 61 72 72 \n", + "3 s001 pure outdoor 6 62 72 72 \n", + "4 s002 mixed indoor 5 25 36 36 \n", + "\n", + " mean_lure mean_target \n", + "0 0.000000 0.805556 \n", + "1 0.055556 0.777778 \n", + "2 0.027778 0.847222 \n", + "3 0.083333 0.861111 \n", + "4 0.138889 0.694444 " + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# use the agg method to get the counts\n", + "\n", + "iperf = iperf.unstack().reset_index()\n", + "\n", + "# collapse the multi-index\n", + "iperf.columns = ['_'.join(col).strip() if len(col[1]) > 0 else col[0] \n", + " for col in iperf.columns.values]\n", + "iperf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Use `apply` to run the functions on each row" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjcondin_outsum_luresum_targetcount_lurecount_targetmean_luremean_targetdprimec
0s001mixedindoor02936360.0000000.8055563.0431340.689560
1s001mixedoutdoor22836360.0555560.7777782.2338920.377209
2s001pureindoor26172720.0277780.8472222.8263920.408552
3s001pureoutdoor66272720.0833330.8611112.4099280.141720
4s002mixedindoor52536360.1388890.6944441.5358000.274347
....................................
87s022pureoutdoor115272720.1527780.7222221.5850450.212121
88s023mixedindoor32436360.0833330.6666671.7313530.447305
89s023mixedoutdoor22036360.0555560.5555561.6300660.679122
90s023pureindoor95572720.1250000.7638891.8329280.209280
91s023pureoutdoor94572720.1250000.6250001.4398710.405808
\n", + "

92 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " subj cond in_out sum_lure sum_target count_lure count_target \\\n", + "0 s001 mixed indoor 0 29 36 36 \n", + "1 s001 mixed outdoor 2 28 36 36 \n", + "2 s001 pure indoor 2 61 72 72 \n", + "3 s001 pure outdoor 6 62 72 72 \n", + "4 s002 mixed indoor 5 25 36 36 \n", + ".. ... ... ... ... ... ... ... \n", + "87 s022 pure outdoor 11 52 72 72 \n", + "88 s023 mixed indoor 3 24 36 36 \n", + "89 s023 mixed outdoor 2 20 36 36 \n", + "90 s023 pure indoor 9 55 72 72 \n", + "91 s023 pure outdoor 9 45 72 72 \n", + "\n", + " mean_lure mean_target dprime c \n", + "0 0.000000 0.805556 3.043134 0.689560 \n", + "1 0.055556 0.777778 2.233892 0.377209 \n", + "2 0.027778 0.847222 2.826392 0.408552 \n", + "3 0.083333 0.861111 2.409928 0.141720 \n", + "4 0.138889 0.694444 1.535800 0.274347 \n", + ".. ... ... ... ... \n", + "87 0.152778 0.722222 1.585045 0.212121 \n", + "88 0.083333 0.666667 1.731353 0.447305 \n", + "89 0.055556 0.555556 1.630066 0.679122 \n", + "90 0.125000 0.763889 1.832928 0.209280 \n", + "91 0.125000 0.625000 1.439871 0.405808 \n", + "\n", + "[92 rows x 11 columns]" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# add the dprime as a new column (axis=1 tells it to go by row)\n", + "iperf['dprime'] = iperf.apply(lambda x: calc_dprime(x['sum_target'], x['count_target'],\n", + " x['sum_lure'], x['count_lure']),\n", + " axis=1)\n", + "# add bias (c) as a new column\n", + "iperf['c'] = iperf.apply(lambda x: calc_c(x['sum_target'], x['count_target'],\n", + " x['sum_lure'], x['count_lure']),\n", + " axis=1)\n", + "\n", + "iperf" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Plotting d prime\n", + "\n", + "Now that we have our sensitivity, let's see if there is any consistent result between conditions" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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condin_outmeanstdsemcilen
0mixedindoor2.1143750.4260060.0888280.18421923.0
1mixedoutdoor1.9549910.4304250.0897500.18613023.0
2pureindoor2.0552460.4121010.0859290.17820623.0
3pureoutdoor1.6881430.4649780.0969550.20107223.0
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" + ], + "text/plain": [ + " cond in_out mean std sem ci len\n", + "0 mixed indoor 2.114375 0.426006 0.088828 0.184219 23.0\n", + "1 mixed outdoor 1.954991 0.430425 0.089750 0.186130 23.0\n", + "2 pure indoor 2.055246 0.412101 0.085929 0.178206 23.0\n", + "3 pure outdoor 1.688143 0.464978 0.096955 0.201072 23.0" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res = ci_within(iperf, indexvar='subj', \n", + " withinvars=['cond', 'in_out'], \n", + " measvar='dprime').reset_index()\n", + "res" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = (pn.ggplot(res, pn.aes('cond', 'mean', fill='in_out'))\n", + " + pn.geom_errorbar(pn.aes(ymin='mean-ci', ymax='mean+ci', width=0.2), \n", + " position=pn.position_dodge(.9))\n", + " + pn.geom_point(position=pn.position_dodge(.9), size=4)\n", + " + pn.labs(x=\"Condition\", y = \"d'\", fill='Location')\n", + " )\n", + "p" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Let's run a linear model!" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.formula.api as smf" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: dprime R-squared: 0.034
Model: OLS Adj. R-squared: 0.002
Method: Least Squares F-statistic: 1.047
Date: Thu, 19 Nov 2020 Prob (F-statistic): 0.376
Time: 14:52:59 Log-Likelihood: -117.12
No. Observations: 92 AIC: 242.2
Df Residuals: 88 BIC: 252.3
Df Model: 3
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept 2.1144 0.184 11.476 0.000 1.748 2.481
cond[T.pure] -0.0591 0.261 -0.227 0.821 -0.577 0.459
in_out[T.outdoor] -0.1594 0.261 -0.612 0.542 -0.677 0.358
cond[T.pure]:in_out[T.outdoor] -0.2077 0.369 -0.564 0.574 -0.940 0.525
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Omnibus: 3.796 Durbin-Watson: 0.886
Prob(Omnibus): 0.150 Jarque-Bera (JB): 3.081
Skew: 0.400 Prob(JB): 0.214
Kurtosis: 3.405 Cond. No. 6.85


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: dprime R-squared: 0.034\n", + "Model: OLS Adj. R-squared: 0.002\n", + "Method: Least Squares F-statistic: 1.047\n", + "Date: Thu, 19 Nov 2020 Prob (F-statistic): 0.376\n", + "Time: 14:52:59 Log-Likelihood: -117.12\n", + "No. Observations: 92 AIC: 242.2\n", + "Df Residuals: 88 BIC: 252.3\n", + "Df Model: 3 \n", + "Covariance Type: nonrobust \n", + "==================================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------------------------\n", + "Intercept 2.1144 0.184 11.476 0.000 1.748 2.481\n", + "cond[T.pure] -0.0591 0.261 -0.227 0.821 -0.577 0.459\n", + "in_out[T.outdoor] -0.1594 0.261 -0.612 0.542 -0.677 0.358\n", + "cond[T.pure]:in_out[T.outdoor] -0.2077 0.369 -0.564 0.574 -0.940 0.525\n", + "==============================================================================\n", + "Omnibus: 3.796 Durbin-Watson: 0.886\n", + "Prob(Omnibus): 0.150 Jarque-Bera (JB): 3.081\n", + "Skew: 0.400 Prob(JB): 0.214\n", + "Kurtosis: 3.405 Cond. No. 6.85\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# build a least squares regression\n", + "model = smf.ols(\"dprime ~ cond * in_out\", iperf).fit()\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What about bias?\n", + "\n", + "It could be that there is a systematic bias in the responses due to the image types." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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condin_outmeanstdsemcilen
0mixedindoor0.1253180.2022340.0421690.08745223.0
1mixedoutdoor0.3243650.1612030.0336130.06971023.0
2pureindoor0.1338940.1568360.0327030.06782123.0
3pureoutdoor0.3013680.1585050.0330510.06854323.0
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" + ], + "text/plain": [ + " cond in_out mean std sem ci len\n", + "0 mixed indoor 0.125318 0.202234 0.042169 0.087452 23.0\n", + "1 mixed outdoor 0.324365 0.161203 0.033613 0.069710 23.0\n", + "2 pure indoor 0.133894 0.156836 0.032703 0.067821 23.0\n", + "3 pure outdoor 0.301368 0.158505 0.033051 0.068543 23.0" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res = ci_within(iperf, indexvar='subj', \n", + " withinvars=['cond', 'in_out'], \n", + " measvar='c').reset_index()\n", + "res" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OLS Regression Results
Dep. Variable: c R-squared: 0.093
Model: OLS Adj. R-squared: 0.062
Method: Least Squares F-statistic: 3.013
Date: Thu, 19 Nov 2020 Prob (F-statistic): 0.0343
Time: 14:53:01 Log-Likelihood: -15.756
No. Observations: 92 AIC: 39.51
Df Residuals: 88 BIC: 49.60
Df Model: 3
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept 0.1253 0.061 2.047 0.044 0.004 0.247
cond[T.pure] 0.0086 0.087 0.099 0.921 -0.163 0.181
in_out[T.outdoor] 0.1990 0.087 2.299 0.024 0.027 0.371
cond[T.pure]:in_out[T.outdoor] -0.0316 0.122 -0.258 0.797 -0.275 0.212
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Omnibus: 0.249 Durbin-Watson: 1.134
Prob(Omnibus): 0.883 Jarque-Bera (JB): 0.432
Skew: -0.005 Prob(JB): 0.806
Kurtosis: 2.664 Cond. No. 6.85


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: c R-squared: 0.093\n", + "Model: OLS Adj. R-squared: 0.062\n", + "Method: Least Squares F-statistic: 3.013\n", + "Date: Thu, 19 Nov 2020 Prob (F-statistic): 0.0343\n", + "Time: 14:53:01 Log-Likelihood: -15.756\n", + "No. Observations: 92 AIC: 39.51\n", + "Df Residuals: 88 BIC: 49.60\n", + "Df Model: 3 \n", + "Covariance Type: nonrobust \n", + "==================================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------------------------\n", + "Intercept 0.1253 0.061 2.047 0.044 0.004 0.247\n", + "cond[T.pure] 0.0086 0.087 0.099 0.921 -0.163 0.181\n", + "in_out[T.outdoor] 0.1990 0.087 2.299 0.024 0.027 0.371\n", + "cond[T.pure]:in_out[T.outdoor] -0.0316 0.122 -0.258 0.797 -0.275 0.212\n", + "==============================================================================\n", + "Omnibus: 0.249 Durbin-Watson: 1.134\n", + "Prob(Omnibus): 0.883 Jarque-Bera (JB): 0.432\n", + "Skew: -0.005 Prob(JB): 0.806\n", + "Kurtosis: 2.664 Cond. No. 6.85\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# build a linear regression of the full model\n", + "m0 = smf.ols(\"c ~ cond * in_out\", iperf).fit()\n", + "m0.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Conclusion\n", + "\n", + "- We see a robust bias whereby participants are less likely to respond 'old' for outdoor items.\n", + "- This is regardless of whether the list is mixed or pure." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment before next class\n", + "\n", + "- We will post a small set of analyses to run on the word memory data based on the examples in this class\n", + "- This will be due on ***Thursday*** next week\n", + "\n", + "### See you next week!!!" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lessons/13_Bayesian_LMER.ipynb b/lessons/13_Bayesian_LMER.ipynb index a5cda86..67f52f3 100644 --- a/lessons/13_Bayesian_LMER.ipynb +++ b/lessons/13_Bayesian_LMER.ipynb @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -133,8 +133,8 @@ "from scipy import stats\n", "from glob import glob\n", "import os\n", - "import arviz as az\n", - "import bambi as bmb\n", + "#import arviz as az\n", + "#import bambi as bmb\n", "import statsmodels.formula.api as smf\n", "import statsmodels.api as sm\n", "\n", @@ -156,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -195,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -372,14 +372,14 @@ "4 2369.152451 out2086.jpg outdoor lure outdoor s001 0 " ] }, - "execution_count": 3, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# load the data from each task\n", - "task_dir = os.path.join('data', 'Taskapalooza')\n", + "task_dir = os.path.join('data2', 'Taskapalooza')\n", "\n", "df_f = load_all_subj_logs(task_dir, 'log_flanker')\n", "df_i = load_all_subj_logs(task_dir, 'log_image_test')\n", @@ -400,18 +400,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 15, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", - "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" - ] - } - ], + "outputs": [], "source": [ "# it turns out the cond is easier to visualize as pure and mixed\n", "def fix_conds(df, type_col):\n", @@ -474,7 +465,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -661,7 +652,7 @@ "11 pure target pos 0.773551 0.420622 0.012659 0.024839 1104.0" ] }, - "execution_count": 5, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -678,20 +669,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 17, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/per/anaconda3/lib/python3.7/site-packages/plotnine/utils.py:1246: FutureWarning: is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\n", - " if pdtypes.is_categorical(arr):\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -702,10 +685,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -736,7 +719,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -878,7 +861,7 @@ "[276 rows x 5 columns]" ] }, - "execution_count": 7, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -892,8 +875,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 19, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -910,10 +895,10 @@ " Method: Least Squares F-statistic: 4.738\n", "\n", "\n", - " Date: Thu, 19 Nov 2020 Prob (F-statistic): 1.23e-06\n", + " Date: Wed, 02 Dec 2020 Prob (F-statistic): 1.23e-06\n", "\n", "\n", - " Time: 13:23:48 Log-Likelihood: 183.23\n", + " Time: 23:16:11 Log-Likelihood: 183.23\n", "\n", "\n", " No. Observations: 276 AIC: -342.5\n", @@ -992,8 +977,8 @@ "Dep. Variable: correct R-squared: 0.165\n", "Model: OLS Adj. R-squared: 0.130\n", "Method: Least Squares F-statistic: 4.738\n", - "Date: Thu, 19 Nov 2020 Prob (F-statistic): 1.23e-06\n", - "Time: 13:23:48 Log-Likelihood: 183.23\n", + "Date: Wed, 02 Dec 2020 Prob (F-statistic): 1.23e-06\n", + "Time: 23:16:11 Log-Likelihood: 183.23\n", "No. Observations: 276 AIC: -342.5\n", "Df Residuals: 264 BIC: -299.0\n", "Df Model: 11 \n", @@ -1025,7 +1010,7 @@ "\"\"\"" ] }, - "execution_count": 8, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1054,7 +1039,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1157,7 +1142,7 @@ "Residual 4.283420 264.0 NaN NaN" ] }, - "execution_count": 9, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } diff --git a/lessons/3subj_data 2.zip b/lessons/3subj_data 2.zip new file mode 100644 index 0000000..2f4939f Binary files /dev/null and b/lessons/3subj_data 2.zip differ diff --git a/lessons/A04_ListGen_Solution.ipynb b/lessons/A04_ListGen_Solution.ipynb new file mode 100644 index 0000000..c88b612 --- /dev/null +++ b/lessons/A04_ListGen_Solution.ipynb @@ -0,0 +1,1039 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment 4: List Generation for Experiments\n", + "## Computational Methods in Psychology (and Neuroscience)\n", + "### Psychology 4500/7559 --- Fall 2020" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Objectives\n", + "\n", + "Upon completion of this assignment, the student will have:\n", + "\n", + "1. Read in a stimulus pool from a file.\n", + "\n", + "2. Randomly generated lists to use in a experiment.\n", + "\n", + "3. Written the lists out to files for future use.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment\n", + "\n", + "* Write code in a Jupyter notebook (after making a copy and renaming it to have your userid in the title --- e.g., A04_ListGen_mst3k).\n", + "\n", + "## Design\n", + "\n", + "Your assignment is to write a script that reads in a pool of stimuli\n", + "and creates lists of dictionaries that you will later present to\n", + "participants as part of an experiment. \n", + "\n", + "The script should be configurable such that you can specify different\n", + "numbers of lists and trials, along with other details specific to the\n", + "experiment you decide to do.\n", + "\n", + "Each dictionary represents a trial and should contain all the\n", + "information necessary to identify the stimulus to be presented,\n", + "details about that stimulus, and the condition in which to present it.\n", + "This information will be experiment-specific, as outlined below.\n", + "\n", + "You have three options for your experiment. Please select **one** of\n", + "the following experiments, keeping in mind that your next assignment\n", + "will be to code the experiment presentation and response collection\n", + "for the lists you generate from this assignment.\n", + "\n", + " \n", + "* ***When you are done, save this notebook as HTML (`File -> Download as -> HTML`) and upload it to the matching assignment on UVACollab.*** " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generic Study/Test Block Function" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "from csv import DictReader\n", + "import copy\n", + "\n", + "# function to make a study/test block from the pools past in\n", + "def gen_block(pools, cond, num_items):\n", + " # fill the study list\n", + " study_list = []\n", + " \n", + " # loop over pools\n", + " for pool in pools:\n", + " # loop over items to add from that pool\n", + " # this will be num_items/num_types for mixed lists\n", + " for i in range(num_items):\n", + " study_item = pool.pop()\n", + " study_item.update({'novelty': 'target', \n", + " 'cond': cond})\n", + " study_list.append(study_item)\n", + "\n", + " # shuffle the study_list\n", + " random.shuffle(study_list)\n", + " \n", + " # copy the study list to be the start of the test list\n", + " test_list = copy.deepcopy(study_list)\n", + " \n", + " # loop over pools\n", + " for pool in pools:\n", + " # loop over items to add from that pool\n", + " # this will be num_items/num_types for mixed lists\n", + " for i in range(num_items):\n", + " test_item = pool.pop()\n", + " test_item.update({'novelty': 'lure', \n", + " 'cond': cond})\n", + " test_list.append(test_item)\n", + " \n", + " # shuffle the test list\n", + " random.shuffle(test_list)\n", + " \n", + " return {'study': study_list, 'test': test_list}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Verification function" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conds: ['neg' 'pos' 'neg' 'neu' 'pos' 'mixed' 'mixed' 'neu' 'pos' 'mixed' 'neu'\n", + " 'neg']\n", + "Cond Counts: [3 3 3 3]\n", + "Num Study: [9 9 9 9 9 9 9 9 9 9 9 9]\n", + "Num Test: [18 18 18 18 18 18 18 18 18 18 18 18]\n", + "It passed all the tests!!!\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# verification function\n", + "def verify_blocks(blocks, study_key='study', test_key='test', \n", + " cond_key='cond', mixed_cond='mixed',\n", + " novelty_key='novelty', type_key='in_out'):\n", + " # pull out the unique conditions from the first item in each study list\n", + " conds = np.array([b[study_key][0][cond_key] for b in blocks])\n", + " uconds = np.unique(conds)\n", + " num_conds = len(uconds)\n", + " print('Conds:', conds)\n", + " \n", + " # verify number of blocks is multiple of num_conds\n", + " assert len(blocks) % num_conds == 0\n", + " \n", + " # verify each cond is same number of times\n", + " cond_counts = np.array([(conds==cond).sum() for cond in uconds])\n", + " print('Cond Counts:', cond_counts)\n", + " assert np.all((cond_counts - cond_counts[0])==0)\n", + "\n", + " # verify number of study items is always the same\n", + " num_study = np.array([len(b[study_key]) for b in blocks])\n", + " print('Num Study:', num_study)\n", + " assert np.all((num_study - num_study[0])==0)\n", + "\n", + " # verify number of test items is always the same\n", + " num_test = np.array([len(b[test_key]) for b in blocks])\n", + " print('Num Test:', num_test)\n", + " assert np.all((num_test - num_test[0])==0)\n", + " \n", + " # verify study block is half length of test block\n", + " assert np.all((num_study*2 - num_test)==0)\n", + " \n", + " # do some checks on each block\n", + " for b in blocks:\n", + " if b[study_key][0][cond_key] == mixed_cond:\n", + " # verify mixed lists\n", + " # must have same number of each type\n", + " types = np.array([item[type_key] for item in b[study_key]])\n", + " utypes = np.unique(types)\n", + " type_counts = np.array([(types == ut).sum() \n", + " for ut in utypes])\n", + " assert np.all((type_counts - type_counts[0]) == 0)\n", + " \n", + " print('It passed all the tests!!!')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Option 1: Valence Study\n", + "\n", + "The main question of this study is whether recognition memory for\n", + "words depends on the emotional or affective valence of those words.\n", + "Participants will study lists of positive (+), negative (-), and\n", + "neutral (~) words and then, after a short delay, they will be given a\n", + "recognition test over all the studied target words plus a matched set\n", + "of non-studied lures. The stimuli are contained in three separate CSV\n", + "files:\n", + "\n", + "- [Positive Pool](./pos_pool.csv)\n", + "- [Negative Pool](./neg_pool.csv)\n", + "- [Neutral Pool](./neu_pool.csv)\n", + "\n", + "You will need to read these files in as lists of dictionaries (hint,\n", + "use the ``DictReader`` from the ``csv`` module that was covered in\n", + "class.) Use these pools to create lists of trials for two\n", + "experimental conditions: pure or mixed. In the *pure* condition,\n", + "all of the trials should be words from the same valence (be sure to\n", + "have the same number of positive, negative, and neutral pure lists.)\n", + "In the *mixed* condition, each list should contain an equal number of\n", + "positive, negative, and neutral words in *random* order (hint, use the\n", + "``shuffle`` function provided by the ``random`` module.) \n", + "\n", + "You will need to generate a matching test list for each study list\n", + "that includes all the studied items, plus a set of lures that match\n", + "the valence of the studied words.\n", + "\n", + "Be sure to add in information to each trial dictionary that identifies\n", + "the word, its valence, the condition of the list, and whether it is a\n", + "target or a lure. Feel free to add in more information if you would\n", + "like.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "# config variables\n", + "pos_file = 'pos_pool.csv'\n", + "neg_file = 'neg_pool.csv'\n", + "neu_file = 'neu_pool.csv'\n", + "\n", + "# number of pools\n", + "num_pools = 3\n", + "\n", + "# number of items in pure lists (must be evenly divisible by num_pools)\n", + "num_items_pure = 9\n", + "\n", + "# number of repetitions of each block type\n", + "num_reps = 3 \n", + "\n", + "# verify these numbers make sense\n", + "num_items_mixed = int(num_items_pure / num_pools)\n", + "assert num_items_mixed * num_pools == num_items_pure" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pos_pool: 301\n", + "neg_pool: 292\n", + "neu_pool: 208\n" + ] + } + ], + "source": [ + "# load in the pools (must add in valence)\n", + "pos_pool = [dict({'valence': 'pos'}, **i) \n", + " for i in DictReader(open(pos_file, 'r'))]\n", + "neg_pool = [dict({'valence': 'neg'}, **i) \n", + " for i in DictReader(open(neg_file, 'r'))]\n", + "neu_pool = [dict({'valence': 'neu'}, **i) \n", + " for i in DictReader(open(neu_file, 'r'))]\n", + "\n", + "# print out number of items in each pool\n", + "print('pos_pool:', len(pos_pool))\n", + "print('neg_pool:', len(neg_pool))\n", + "print('neu_pool:', len(neu_pool))\n", + "\n", + "# shuffle the pools\n", + "random.shuffle(pos_pool)\n", + "random.shuffle(neg_pool)\n", + "random.shuffle(neu_pool)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pos_pool: 229\n", + "neg_pool: 220\n", + "neu_pool: 136\n" + ] + }, + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# generate the blocks\n", + "blocks = []\n", + "for r in range(num_reps):\n", + " # generate a pure pos block\n", + " blocks.append(gen_block([pos_pool], 'pos', \n", + " num_items_pure))\n", + " \n", + " # generate a pure neg block\n", + " blocks.append(gen_block([neg_pool], 'neg', \n", + " num_items_pure))\n", + " \n", + " # generate a pure neu block\n", + " blocks.append(gen_block([neu_pool], 'neu', \n", + " num_items_pure))\n", + " \n", + " # generate a mixed pos/neg/neu block\n", + " blocks.append(gen_block([pos_pool, neg_pool, neu_pool], \n", + " 'mixed', num_items_mixed))\n", + "\n", + "# shuffle the blocks\n", + "random.shuffle(blocks)\n", + "\n", + "# let's see how many items we have left\n", + "print('pos_pool:', len(pos_pool))\n", + "print('neg_pool:', len(neg_pool))\n", + "print('neu_pool:', len(neu_pool))\n", + "\n", + "len(blocks)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'study': [{'valence': 'pos',\n", + " 'description': 'silly',\n", + " 'word_no': '981',\n", + " 'valence_mean': '7.4100000000000001',\n", + " 'valence_sd': '1.8',\n", + " 'arousal_mean': '5.8799999999999999',\n", + " 'arousal_sd': '2.3799999999999999',\n", + " 'dominance_mean': '6.0',\n", + " 'dominance_sd': '2.0899999999999999',\n", + " 'word_frequency': '15',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'ambition',\n", + " 'word_no': '14',\n", + " 'valence_mean': '7.04',\n", + " 'valence_sd': '1.98',\n", + " 'arousal_mean': '5.6100000000000003',\n", + " 'arousal_sd': '2.9199999999999999',\n", + " 'dominance_mean': '6.9299999999999997',\n", + " 'dominance_sd': '2.0699999999999998',\n", + " 'word_frequency': '19',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'untroubled',\n", + " 'word_no': '464',\n", + " 'valence_mean': '7.6200000000000001',\n", + " 'valence_sd': '1.4099999999999999',\n", + " 'arousal_mean': '3.8900000000000001',\n", + " 'arousal_sd': '2.54',\n", + " 'dominance_mean': '5.5300000000000002',\n", + " 'dominance_sd': '2.54',\n", + " 'word_frequency': '.',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'politeness',\n", + " 'word_no': '320',\n", + " 'valence_mean': '7.1799999999999997',\n", + " 'valence_sd': '1.5',\n", + " 'arousal_mean': '3.7400000000000002',\n", + " 'arousal_sd': '2.3700000000000001',\n", + " 'dominance_mean': '5.7400000000000002',\n", + " 'dominance_sd': '1.7',\n", + " 'word_frequency': '5',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'food',\n", + " 'word_no': '514',\n", + " 'valence_mean': '7.6500000000000004',\n", + " 'valence_sd': '1.3700000000000001',\n", + " 'arousal_mean': '5.9199999999999999',\n", + " 'arousal_sd': '2.1099999999999999',\n", + " 'dominance_mean': '6.1799999999999997',\n", + " 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{'valence': 'pos',\n", + " 'description': 'applause',\n", + " 'word_no': '640',\n", + " 'valence_mean': '7.5',\n", + " 'valence_sd': '1.5',\n", + " 'arousal_mean': '5.7999999999999998',\n", + " 'arousal_sd': '2.79',\n", + " 'dominance_mean': '6.4800000000000004',\n", + " 'dominance_sd': '2.1099999999999999',\n", + " 'word_frequency': '14',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'warmth',\n", + " 'word_no': '483',\n", + " 'valence_mean': '7.4100000000000001',\n", + " 'valence_sd': '1.8100000000000001',\n", + " 'arousal_mean': '3.73',\n", + " 'arousal_sd': '2.3999999999999999',\n", + " 'dominance_mean': '5.6100000000000003',\n", + " 'dominance_sd': '1.6699999999999999',\n", + " 'word_frequency': '28',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'}],\n", + " 'test': [{'valence': 'pos',\n", + " 'description': 'circus',\n", + " 'word_no': '72',\n", + " 'valence_mean': '7.2999999999999998',\n", + " 'valence_sd': '1.8400000000000001',\n", + " 'arousal_mean': '5.9699999999999998',\n", + " 'arousal_sd': '2.5899999999999999',\n", + " 'dominance_mean': '5.3899999999999997',\n", + " 'dominance_sd': '2.25',\n", + " 'word_frequency': '7',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'ambition',\n", + " 'word_no': '14',\n", + " 'valence_mean': '7.04',\n", + " 'valence_sd': '1.98',\n", + " 'arousal_mean': '5.6100000000000003',\n", + " 'arousal_sd': '2.9199999999999999',\n", + " 'dominance_mean': '6.9299999999999997',\n", + " 'dominance_sd': '2.0699999999999998',\n", + " 'word_frequency': '19',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'untroubled',\n", + " 'word_no': '464',\n", + " 'valence_mean': '7.6200000000000001',\n", + " 'valence_sd': '1.4099999999999999',\n", + " 'arousal_mean': '3.8900000000000001',\n", + " 'arousal_sd': '2.54',\n", + " 'dominance_mean': '5.5300000000000002',\n", 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+ " 'word_no': '809',\n", + " 'valence_mean': '7.0',\n", + " 'valence_sd': '1.72',\n", + " 'arousal_mean': '5.6900000000000004',\n", + " 'arousal_sd': '2.4500000000000002',\n", + " 'dominance_mean': '5.9299999999999997',\n", + " 'dominance_sd': '2.02',\n", + " 'word_frequency': '12',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'food',\n", + " 'word_no': '514',\n", + " 'valence_mean': '7.6500000000000004',\n", + " 'valence_sd': '1.3700000000000001',\n", + " 'arousal_mean': '5.9199999999999999',\n", + " 'arousal_sd': '2.1099999999999999',\n", + " 'dominance_mean': '6.1799999999999997',\n", + " 'dominance_sd': '2.48',\n", + " 'word_frequency': '147',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'glamour',\n", + " 'word_no': '187',\n", + " 'valence_mean': '6.7599999999999998',\n", + " 'valence_sd': '1.6000000000000001',\n", + " 'arousal_mean': '4.6799999999999997',\n", + " 'arousal_sd': '2.23',\n", + " 'dominance_mean': '5.7599999999999998',\n", + " 'dominance_sd': '2.4900000000000002',\n", + " 'word_frequency': '5',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'grin',\n", + " 'word_no': '773',\n", + " 'valence_mean': '7.4000000000000004',\n", + " 'valence_sd': '1.8700000000000001',\n", + " 'arousal_mean': '5.2699999999999996',\n", + " 'arousal_sd': '2.6400000000000001',\n", + " 'dominance_mean': '6.0',\n", + " 'dominance_sd': '1.8600000000000001',\n", + " 'word_frequency': '13',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'wedding',\n", + " 'word_no': '491',\n", + " 'valence_mean': '7.8200000000000003',\n", + " 'valence_sd': '1.5600000000000001',\n", + " 'arousal_mean': '5.9699999999999998',\n", + " 'arousal_sd': '2.8500000000000001',\n", + " 'dominance_mean': '6.6799999999999997',\n", + " 'dominance_sd': '2.0800000000000001',\n", + " 'word_frequency': '32',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'inspire',\n", + " 'word_no': '232',\n", + " 'valence_mean': '6.9699999999999998',\n", + " 'valence_sd': '1.9099999999999999',\n", + " 'arousal_mean': '5.0',\n", + " 'arousal_sd': '2.5299999999999998',\n", + " 'dominance_mean': '6.3399999999999999',\n", + " 'dominance_sd': '2.1099999999999999',\n", + " 'word_frequency': '3',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'friend',\n", + " 'word_no': '174',\n", + " 'valence_mean': '7.7400000000000002',\n", + " 'valence_sd': '1.24',\n", + " 'arousal_mean': '5.7400000000000002',\n", + " 'arousal_sd': '2.5699999999999998',\n", + " 'dominance_mean': '6.7400000000000002',\n", + " 'dominance_sd': '1.8899999999999999',\n", + " 'word_frequency': '133',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'caress',\n", + " 'word_no': '64',\n", + " 'valence_mean': '7.8399999999999999',\n", + " 'valence_sd': '1.1599999999999999',\n", + " 'arousal_mean': '5.1399999999999997',\n", + " 'arousal_sd': '3.0',\n", + " 'dominance_mean': '5.8300000000000001',\n", + " 'dominance_sd': '2.1299999999999999',\n", + " 'word_frequency': '1',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'silly',\n", + " 'word_no': '981',\n", + " 'valence_mean': '7.4100000000000001',\n", + " 'valence_sd': '1.8',\n", + " 'arousal_mean': '5.8799999999999999',\n", + " 'arousal_sd': '2.3799999999999999',\n", + " 'dominance_mean': '6.0',\n", + " 'dominance_sd': '2.0899999999999999',\n", + " 'word_frequency': '15',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'zest',\n", + " 'word_no': '1040',\n", + " 'valence_mean': '6.79',\n", + " 'valence_sd': '2.04',\n", + " 'arousal_mean': '5.5899999999999999',\n", + " 'arousal_sd': '2.6600000000000001',\n", + " 'dominance_mean': '6.0',\n", + " 'dominance_sd': '1.99',\n", + " 'word_frequency': '5',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'easygoing',\n", + " 'word_no': '135',\n", + " 'valence_mean': '7.2000000000000002',\n", + " 'valence_sd': '1.5',\n", + " 'arousal_mean': '4.2999999999999998',\n", + " 'arousal_sd': '2.52',\n", + " 'dominance_mean': '5.25',\n", + " 'dominance_sd': '1.75',\n", + " 'word_frequency': '1',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'useful',\n", + " 'word_no': '466',\n", + " 'valence_mean': '7.1399999999999997',\n", + " 'valence_sd': '1.6000000000000001',\n", + " 'arousal_mean': '4.2599999999999998',\n", + " 'arousal_sd': '2.4700000000000002',\n", + " 'dominance_mean': '5.9299999999999997',\n", + " 'dominance_sd': '2.1000000000000001',\n", + " 'word_frequency': '58',\n", + " 'novelty': 'lure',\n", + " 'cond': 'pos'},\n", + " {'valence': 'pos',\n", + " 'description': 'warmth',\n", + " 'word_no': '483',\n", + " 'valence_mean': '7.4100000000000001',\n", + " 'valence_sd': '1.8100000000000001',\n", + " 'arousal_mean': '3.73',\n", + " 'arousal_sd': '2.3999999999999999',\n", + " 'dominance_mean': '5.6100000000000003',\n", + " 'dominance_sd': '1.6699999999999999',\n", + " 'word_frequency': '28',\n", + " 'novelty': 'target',\n", + " 'cond': 'pos'}]}" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "blocks[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conds: ['pos' 'mixed' 'mixed' 'neu' 'neu' 'mixed' 'pos' 'pos' 'neg' 'neg' 'neu'\n", + " 'neg']\n", + "Cond Counts: [3 3 3 3]\n", + "Num Study: [9 9 9 9 9 9 9 9 9 9 9 9]\n", + "Num Test: [18 18 18 18 18 18 18 18 18 18 18 18]\n", + "It passed all the tests!!!\n" + ] + } + ], + "source": [ + "verify_blocks(blocks, study_key='study', test_key='test', \n", + " cond_key='cond', mixed_cond='mixed',\n", + " novelty_key='novelty', type_key='valence')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Option 2: Scene Study\n", + "\n", + "This study will test whether recognition memory for indoor and outdoor\n", + "scenes is modulated by the structure of the study lists.\n", + "Specifically, participants will study lists that either have indoor\n", + "and outdoor scenes that come in pure blocks or intermixed (similar to\n", + "the Valence study above). The participants will then be given a\n", + "recognition test over all the studied target images plus a matched set\n", + "of non-studied lures. You can access the lists of stimuli available:\n", + "\n", + "- [Indoor Pool](./indoor.csv)\n", + "- [Outdoor Pool](./outdoor.csv)\n", + "\n", + "You will need to read these files in as lists of dictionaries (hint,\n", + "use the ``DictReader`` from the ``csv`` module that was covered in\n", + "class.) For the actual experiment we will give you the images that\n", + "are referenced by the file names in these pools, but for the list\n", + "generation you do not need the images, themselves and should identify\n", + "the image you will be presenting using the file name. Use these pools\n", + "to create lists of trials for two experimental conditions: pure or\n", + "mixed. In the *pure* condition, all of the trials should be images\n", + "from the same category (be sure to have the same number of indoor\n", + "and outdoor pure lists.) In the *mixed* condition, each\n", + "list should contain an equal number of indoor and outdoor\n", + "images in *random* order (hint, use the ``shuffle`` function provided\n", + "by the ``random`` module.)\n", + "\n", + "You will need to generate a matching test list for each study list\n", + "that includes all the studied items, plus a set of lures that match\n", + "the image categories from the studied items.\n", + "\n", + "Be sure to add in information to each trial dictionary that identifies\n", + "the file name, the category of the image, the condition of the list,\n", + "and whether it is a target or a lure.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [ + "# config variables\n", + "indoor_file = 'indoor.csv'\n", + "outdoor_file = 'outdoor.csv'\n", + "\n", + "# number of pools\n", + "num_pools = 2\n", + "\n", + "# number of items in pure lists (must be evenly divisible by num_pools)\n", + "num_items_pure = 10\n", + "\n", + "# number of repetitions of each block type\n", + "num_reps = 3 \n", + "\n", + "# verify these numbers make sense\n", + "num_items_mixed = int(num_items_pure / num_pools)\n", + "assert num_items_mixed * num_pools == num_items_pure" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "indoor: 335\n", + "outdoor: 309\n" + ] + } + ], + "source": [ + "# load in the pools\n", + "indoor_pool = [i for i in DictReader(open(indoor_file, 'r'))]\n", + "outdoor_pool = [i for i in DictReader(open(outdoor_file, 'r'))]\n", + "print('indoor:', len(indoor_pool))\n", + "print('outdoor:', len(outdoor_pool))\n", + "\n", + "# shuffle the pools\n", + "random.shuffle(indoor_pool)\n", + "random.shuffle(outdoor_pool)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "indoor: 245\n", + "outdoor: 219\n" + ] + }, + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# generate the blocks\n", + "blocks = []\n", + "for r in range(num_reps):\n", + " # generate a pure indoor block\n", + " blocks.append(gen_block([indoor_pool], 'indoor', \n", + " num_items_pure))\n", + " \n", + " # generate a pure outdoor block\n", + " blocks.append(gen_block([outdoor_pool], 'outdoor', \n", + " num_items_pure))\n", + " \n", + " # generate a mixed indoor/outdoor block\n", + " blocks.append(gen_block([indoor_pool, outdoor_pool], 'mixed', \n", + " num_items_mixed))\n", + "\n", + "# shuffle the blocks\n", + "random.shuffle(blocks)\n", + "\n", + "# let's see how many items we have left\n", + "print('indoor:', len(indoor_pool))\n", + "print('outdoor:', len(outdoor_pool))\n", + "\n", + "len(blocks)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'study': [OrderedDict([('filename', 'in0003.jpg'),\n", + " ('in_out', 'indoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'out0008_new.jpg'),\n", + " ('in_out', 'outdoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'out0121_new.jpg'),\n", + " ('in_out', 'outdoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'out0092_new.jpg'),\n", + " ('in_out', 'outdoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'out1355.jpg'),\n", + " ('in_out', 'outdoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'in0035.jpg'),\n", + " ('in_out', 'indoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'in0083.jpg'),\n", + " ('in_out', 'indoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'in0236.jpg'),\n", + " ('in_out', 'indoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'out1444.jpg'),\n", + " ('in_out', 'outdoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'in0289.jpg'),\n", + " ('in_out', 'indoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')])],\n", + " 'test': [OrderedDict([('filename', 'in0019.jpg'),\n", + " ('in_out', 'indoor'),\n", + " ('novelty', 'lure'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'out0121_new.jpg'),\n", + " ('in_out', 'outdoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'out0008_new.jpg'),\n", + " ('in_out', 'outdoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'in0003.jpg'),\n", + " ('in_out', 'indoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'out1355.jpg'),\n", + " ('in_out', 'outdoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'out1444.jpg'),\n", + " ('in_out', 'outdoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'out0133_new.jpg'),\n", + " ('in_out', 'outdoor'),\n", + " ('novelty', 'lure'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'in0321.jpg'),\n", + " ('in_out', 'indoor'),\n", + " ('novelty', 'lure'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'in0289.jpg'),\n", + " ('in_out', 'indoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'in0157.jpg'),\n", + " ('in_out', 'indoor'),\n", + " ('novelty', 'lure'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'out0092_new.jpg'),\n", + " ('in_out', 'outdoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'in0236.jpg'),\n", + " ('in_out', 'indoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'out0134_new.jpg'),\n", + " ('in_out', 'outdoor'),\n", + " ('novelty', 'lure'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'out1510.jpg'),\n", + " ('in_out', 'outdoor'),\n", + " ('novelty', 'lure'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'in0373.jpg'),\n", + " ('in_out', 'indoor'),\n", + " ('novelty', 'lure'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'out1063.jpg'),\n", + " ('in_out', 'outdoor'),\n", + " ('novelty', 'lure'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'in0035.jpg'),\n", + " ('in_out', 'indoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'in0083.jpg'),\n", + " ('in_out', 'indoor'),\n", + " ('novelty', 'target'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'out1037.jpg'),\n", + " ('in_out', 'outdoor'),\n", + " ('novelty', 'lure'),\n", + " ('cond', 'mixed')]),\n", + " OrderedDict([('filename', 'in0250.jpg'),\n", + " ('in_out', 'indoor'),\n", + " ('novelty', 'lure'),\n", + " ('cond', 'mixed')])]}" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "blocks[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conds: ['mixed' 'indoor' 'mixed' 'outdoor' 'indoor' 'mixed' 'outdoor' 'outdoor'\n", + " 'indoor']\n", + "Cond Counts: [3 3 3]\n", + "Num Study: [10 10 10 10 10 10 10 10 10]\n", + "Num Test: [20 20 20 20 20 20 20 20 20]\n", + "It passed all the tests!!!\n" + ] + } + ], + "source": [ + "verify_blocks(blocks, study_key='study', test_key='test', \n", + " cond_key='cond', mixed_cond='mixed',\n", + " novelty_key='novelty', type_key='in_out')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/all_data 2.zip b/lessons/all_data 2.zip new file mode 100644 index 0000000..bab5779 Binary files /dev/null and b/lessons/all_data 2.zip differ diff --git a/lessons/ci_within 2.py b/lessons/ci_within 2.py new file mode 100644 index 0000000..0f9bef3 --- /dev/null +++ b/lessons/ci_within 2.py @@ -0,0 +1,109 @@ +# Author Denis A. Engemann +# Adjustments: Josef Perktold, Per Sederberg +# +# License: BSD (3-clause) + +import numpy as np +from scipy import stats +import pandas as pd + +def ci_within(df, indexvar, withinvars, measvar, confint=0.95, + copy=True): + """ Compute CI / SEM correction factor + Morey 2008, Cousinaueu 2005, Loftus & Masson, 1994 + Also see R-cookbook http://goo.gl/QdwJl + Note. This functions helps to generate appropriate confidence + intervals for repeated measure designs. + Standard confidence intervals are are computed on normalized data + and a correction factor is applied that prevents insanely small values. + df : instance of pandas.DataFrame + The data frame objetct. + indexvar : str + The column name of of the identifier variable that representing + subjects or repeated measures + withinvars : str | list of str + The column names of the categorial data identifying random effects + measvar : str + The column name of the response measure + confint : float + The confidence interval + copy : bool + Whether to copy the data frame or not. + """ + if copy: + df = df.copy() + + # Apply Cousinaueu's method: + # compute grand mean + mean_ = df[measvar].mean() + + # compute subject means + subj_means = df.groupby(indexvar)[measvar].mean().values + for subj, smean_ in zip(df[indexvar].unique(), subj_means): + # center + #df[measvar][df[indexvar] == subj] -= smean_ + df.loc[df[indexvar] == subj, measvar] -= smean_ + # add grand average + #df[measvar][df[indexvar] == subj] += mean_ + df.loc[df[indexvar] == subj, measvar] += mean_ + + def sem(x): + return x.std() / np.sqrt(len(x)) + + def ci(x): + se = sem(x) + return se * stats.t.interval(confint, len(x - 1))[1] + + aggfuncs = [np.mean, np.std, sem, ci, len] + out = df.groupby(withinvars)[measvar].agg(aggfuncs) + + # compute & apply correction factor + n_within = np.prod([len(df[k].unique()) for k in withinvars], + dtype= df[measvar].dtype) + cf = np.sqrt(n_within / (n_within - 1.)) + for k in ['sem', 'std', 'ci']: + out[k] *= cf + + out['ci'] = stats.t.isf((1 - confint) / 2., out['len'] - 1) * out['sem'] + + return out + + +if __name__ == '__main__': + ss = ''' + subject condition value + 1 pretest 59.4 + 2 pretest 46.4 + 3 pretest 46.0 + 4 pretest 49.0 + 5 pretest 32.5 + 6 pretest 45.2 + 7 pretest 60.3 + 8 pretest 54.3 + 9 pretest 45.4 + 10 pretest 38.9 + 1 posttest 64.5 + 2 posttest 52.4 + 3 posttest 49.7 + 4 posttest 48.7 + 5 posttest 37.4 + 6 posttest 49.5 + 7 posttest 59.9 + 8 posttest 54.1 + 9 posttest 49.6 + 10 posttest 48.5''' + + import StringIO + df = pd.read_fwf(StringIO.StringIO(ss), widths=[8, 10, 6], header=1) + res = ci_within(df2, 'subject', ['condition'], 'value', confint=0.95) + print(res) + print(res[['len', 'mean', 'std', 'sem', 'ci']]) + + #ci is different from R + #http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_%28ggplot2%29/#error-bars-for-within-subjects-variables + + #dfwc <- summarySEwithin(dfw.long, measurevar="value", withinvars="condition", + # idvar="subject", na.rm=FALSE, conf.interval=.95) + # condition N value value_norm sd se ci + # posttest 10 51.43 51.43 2.262361 0.7154214 1.618396 + # pretest 10 47.74 47.74 2.262361 0.7154214 1.618396 diff --git a/lessons/data/.DS_Store b/lessons/data/.DS_Store new file mode 100644 index 0000000..79dbde4 Binary files /dev/null and b/lessons/data/.DS_Store differ diff --git 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