diff --git a/CS4500_CompMethods/README.md b/CS4500_CompMethods/README.md new file mode 100644 index 0000000..ac132ab --- /dev/null +++ b/CS4500_CompMethods/README.md @@ -0,0 +1,8 @@ +# COMPSY + +This repository contains the content for the course *Computational +Methods in Psychology and Neuroscience*. + +You can find the syllabus [here](syllabus/syllabus.pdf). + +The [lessons](lessons) folder contains Jupyter notebooks for each lesson. diff --git a/CS4500_CompMethods/assignments/A01_Python_Functions.ipynb b/CS4500_CompMethods/assignments/A01_Python_Functions.ipynb new file mode 100644 index 0000000..d0d56f4 --- /dev/null +++ b/CS4500_CompMethods/assignments/A01_Python_Functions.ipynb @@ -0,0 +1,148 @@ +{ + "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: (your name here)\n", + "# User ID: (your userid here)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ***finish this function replacing the `pass` with your code***\n", + "def factorial(n):\n", + " \"\"\"\n", + " Add your docstring here\n", + " \"\"\"\n", + " # Check the input\n", + " pass\n", + "\n", + " # make sure the input is an integer greater than or equal to 1\n", + " if type(n) is not int:\n", + " # Let the user know you need an integer\n", + " pass\n", + " \n", + " # Initialize a variable to 1\n", + " pass\n", + "\n", + " # Successively multiply the variable by the integers 2 up to n\n", + " for blah in range(blahblah):\n", + " # do something here\n", + " pass\n", + " \n", + " # return the result\n", + " return result\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ***your code here for reading in the numbers and operating correctly***\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" + ] + } + ], + "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/CS4500_CompMethods/assignments/A01_Python_Functions_chr4qt.ipynb b/CS4500_CompMethods/assignments/A01_Python_Functions_chr4qt.ipynb new file mode 100644 index 0000000..231b8d5 --- /dev/null +++ b/CS4500_CompMethods/assignments/A01_Python_Functions_chr4qt.ipynb @@ -0,0 +1,165 @@ +{ + "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: Carlos Rodriguez\n", + "# User ID: chr4qt" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def factorial(n):\n", + " \"\"\"\n", + " Takes whole number value 'n' and calculates the factorial\n", + " \"\"\"\n", + " result = 1 # Sets the base for the factorials to be multiplied by\n", + " for i in range(n+1): # Multiplies each of the numbers to result for factorial (+1 since noninclusive)\n", + " if i == 0:\n", + " result *= 1\n", + " else:\n", + " result *= i\n", + " return result # Returns factorial" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Provide inital numbers and add them to a list\n", + "numbers = []\n", + "num1 = input('Choose the first number: ')\n", + "numbers.append(num1)\n", + "num2 = input('Choose the second number: ')\n", + "numbers.append(num2)\n", + "\n", + "\n", + "# Examines input in list to determine if is valid digit. If so, converts to int. If not, asks for another number.\n", + "for each in numbers:\n", + " while type(each) is not int: \n", + " if each.isdigit():\n", + " swap = numbers.index(each)\n", + " each = int(each)\n", + " numbers[swap] = each\n", + " else:\n", + " swap = numbers.index(each)\n", + " each = input('You have entered a number that is not an integer, please try again: ')\n", + " numbers[swap] = each\n", + "\n", + "\n", + "# Applies proper operation depending on input. \n", + "# 1st less than 2nd gives mean. 1st greater than 2nd gives difference. 1st equals 2nd gives factorial using function above. \n", + "if numbers[0] < numbers[1]:\n", + " mean = (numbers[0] + numbers[1]) / 2\n", + " print(mean)\n", + "elif numbers[0] > numbers[1]:\n", + " dif = (numbers[0] - numbers[1])\n", + " print(dif)\n", + "elif numbers[0] == numbers[1]:\n", + " n = numbers[0]\n", + " print(factorial(n))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge problem (not required, but fun!)\n", + "\n", + "- Re-write the factorial function using a recursive algorithm.\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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CS4500_CompMethods/assignments/A02_GitHub.ipynb b/CS4500_CompMethods/assignments/A02_GitHub.ipynb new file mode 100644 index 0000000..7759353 --- /dev/null +++ b/CS4500_CompMethods/assignments/A02_GitHub.ipynb @@ -0,0 +1,138 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment 2: GitHub\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 new git branches\n", + "2. Write a new file and save it to the repository\n", + "3. Upload the updated branch to GitHub" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating new branches, writing new files, and updating the repository" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, GitHub has a main branch called \"master\". It is good practice to create new branches when working on different sub-projects, then merge your branch with the the master branch when you have completed your task. \n", + "\n", + "The master branch should *ideally* be a functional version of the codebase. By working within branches, you are able to make changes and experiment with code without impacting the main branch.\n", + "\n", + "![alt text](https://cdn2.hubspot.net/hubfs/2249672/Imported_Blog_Media/austin_powers-1.jpg \"Git Master\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NOTE: Execute each command on the command line as they appear on this page. You will run into problems if the commands are not run in this precise order. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a Branch\n", + "We will now create a branch. Use the following command:\n", + "\n", + "`git branch my-branch` to create a branch called \"my-branch\". \n", + "\n", + "Great! We have created a new branch. We now need to switch our new branch by using `git checkout my-branch`\n", + "If you are ever unsure which branch you are on, use `git status` to check." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write a .txt file\n", + "Now we will create a text file called \"quotes.txt\". To do so, enter the following:\n", + "\n", + "for Windows: `start notepad quotes.txt` to open Notepad and create and edit the file\n", + "\n", + "for OSX and Linux: `nano quotes.txt` to create and edit the file in the command prompt.\n", + "\n", + "You can also create & open the file via your OS's file system (Finder for OSX, File Explorer for Windows). Type or copy & paste your favorite quotes of all time into the .txt file. Please include the sources of the quotes. For example: \n", + ">\"Strange things are afoot at the Circle-K.\" \n", + " - Ted, _Bill & Ted's Excellent Adventure_\n", + "\n", + "Save the file, then close it. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Stage, Commit, Push\n", + "Next, we will add this file to the staging area, create a commit, and push it to the repository via these commands:\n", + "\n", + "\n", + "`git add quotes.txt`\n", + "\n", + "`git commit -m \"uploading my favorite quotes\"`\n", + "\n", + "`git push --set-upstream origin my-branch` \n", + "\n", + "The \"--set-upstream origin my-branch\" part of this pushes the newly created branch to the repository, along with our new commit. If we wanted to add or change files in the branch, we can simply use `git push` now that the branch exists on the repository. \n", + "\n", + "Check your repository for the new branch and file. To see your new branch, click the top left button that says \"master\" to open a drop-down menu to see the available branches. You should see your branch \"my-branch\" there.\n", + "![alt text](https://i.imgur.com/SzBELPw.png \"Title\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submitting your assignment\n", + "\n", + "Once you are done with the above steps, enter the following at your command prompt:\n", + "\n", + "```\n", + "git log -1 --stat\n", + "```\n", + "\n", + "And paste the output into the Assignment text box on UVACollab." + ] + } + ], + "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/CS4500_CompMethods/assignments/A03_Manipulating_Lists.ipynb b/CS4500_CompMethods/assignments/A03_Manipulating_Lists.ipynb new file mode 100644 index 0000000..86d4d27 --- /dev/null +++ b/CS4500_CompMethods/assignments/A03_Manipulating_Lists.ipynb @@ -0,0 +1,115 @@ +{ + "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": 2, + "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", + " # write your list sorting code here\n", + " # remember, lists are passed by reference, so if you modify my_list, \n", + " # it will be modified in place\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "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", + "\n", + "# Convert the input into a list\n", + "\n", + "# Call your sorting algorithm on the list\n", + "\n", + "# Print out the sorted list\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." + ] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CS4500_CompMethods/assignments/A03_Manipulating_Lists_chr4qt.ipynb b/CS4500_CompMethods/assignments/A03_Manipulating_Lists_chr4qt.ipynb new file mode 100644 index 0000000..c23333a --- /dev/null +++ b/CS4500_CompMethods/assignments/A03_Manipulating_Lists_chr4qt.ipynb @@ -0,0 +1,166 @@ +{ + "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": null, + "metadata": {}, + "outputs": [], + "source": [ + "def my_sort(start_list):\n", + " '''\n", + " Receives a list of numbers and sorts in ascending order using mergesort method\n", + " '''\n", + " # Create a copy of list to preserve original. Also create template for final sorted list\n", + " temp = start_list[:]\n", + " final = []\n", + " track1 = 1\n", + " track2 = 1\n", + " # Use trackers to continue dividing halves until only single numbers remain\n", + " # Runs through my_sort function for each split chunk\n", + " left = temp[:((len(start_list))//2)]\n", + " if len(left) > 1 and track1 == 1:\n", + " left = my_sort(left)\n", + " elif len(left) == 1 and track1 == 1:\n", + " track1 = 0\n", + " right = temp[((len(start_list))//2):]\n", + " if len(right) > 1 and track2 == 1:\n", + " right = my_sort(right)\n", + " elif len(right) == 1 and track2 == 1:\n", + " track2 = 0\n", + " # Compares first of left and right half; appends smaller value to final list first\n", + " # Pops appended value off to continue comparing remaining values\n", + " # Identical values both popped from start of respective half and appended\n", + " while len(left) > 0 and len(right) > 0:\n", + " if left[0] < right[0]:\n", + " final.append(left[0])\n", + " left.pop(0)\n", + " elif left[0] > right[0]:\n", + " final.append(right[0])\n", + " right.pop(0)\n", + " elif left[0] == right[0]:\n", + " final.append(left[0])\n", + " final.append(right[0])\n", + " left.pop(0)\n", + " right.pop(0)\n", + " # When one half is empty, assumes the other half is already sorted and appends it as is\n", + " if len(left) == 0:\n", + " for each in right:\n", + " final.append(each)\n", + " if len(right) == 0:\n", + " for each in left:\n", + " final.append(each)\n", + " # Returns the final list sorted in ascending order\n", + " return final" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 7)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m7\u001b[0m\n\u001b[1;33m for each in start_list:.\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "# Collect numbers with an input method and turn into list\n", + "start_vals = input(\"Please enter the comma seperated list to be sorted: \")\n", + "start_list = start_vals.split(',')\n", + "\n", + "\n", + "# Convert the string input into a list of floats\n", + "for each in start_list:.\n", + " place = start_list.index(each)\n", + " flt_val = float(each)\n", + " start_list[place] = flt_val\n", + "\n", + " \n", + "# Calls the sorting algorithm on the list and prints the result\n", + "print(my_sort(start_list))" + ] + }, + { + "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." + ] + } + ], + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CS4500_CompMethods/assignments/A04_ListGen.ipynb b/CS4500_CompMethods/assignments/A04_ListGen.ipynb new file mode 100644 index 0000000..f9d7e27 --- /dev/null +++ b/CS4500_CompMethods/assignments/A04_ListGen.ipynb @@ -0,0 +1,170 @@ +{ + "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": "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CS4500_CompMethods/assignments/A04_ListGen_Solution.ipynb b/CS4500_CompMethods/assignments/A04_ListGen_Solution.ipynb new file mode 100644 index 0000000..fe65a5c --- /dev/null +++ b/CS4500_CompMethods/assignments/A04_ListGen_Solution.ipynb @@ -0,0 +1,1030 @@ +{ + "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": 2, + "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": 3, + "metadata": {}, + "outputs": [], + "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": 4, + "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": 5, + "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": 6, + "metadata": { + "scrolled": true + }, + "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": 6, + "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": 13, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'study': [{'valence': 'neg',\n", + " 'description': 'wicked',\n", + " 'word_no': '493',\n", + " 'valence_mean': '2.96',\n", + " 'valence_sd': '2.3700000000000001',\n", + " 'arousal_mean': '6.0899999999999999',\n", + " 'arousal_sd': '2.4399999999999999',\n", + " 'dominance_mean': '4.3600000000000003',\n", + " 'dominance_sd': '2.6499999999999999',\n", + " 'word_frequency': '9',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'maniac',\n", + " 'word_no': '862',\n", + " 'valence_mean': '3.7599999999999998',\n", + " 'valence_sd': '2.0',\n", + " 'arousal_mean': '5.3899999999999997',\n", + " 'arousal_sd': '2.46',\n", + " 'dominance_mean': '4.2199999999999998',\n", + " 'dominance_sd': '2.0699999999999998',\n", + " 'word_frequency': '4',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'loser',\n", + " 'word_no': '851',\n", + " 'valence_mean': '2.25',\n", 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'dominance_mean': '3.8599999999999999',\n", + " 'dominance_sd': '2.2599999999999998',\n", + " 'word_frequency': '7',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'maniac',\n", + " 'word_no': '862',\n", + " 'valence_mean': '3.7599999999999998',\n", + " 'valence_sd': '2.0',\n", + " 'arousal_mean': '5.3899999999999997',\n", + " 'arousal_sd': '2.46',\n", + " 'dominance_mean': '4.2199999999999998',\n", + " 'dominance_sd': '2.0699999999999998',\n", + " 'word_frequency': '4',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'python',\n", + " 'word_no': '949',\n", + " 'valence_mean': '4.0499999999999998',\n", + " 'valence_sd': '2.48',\n", + " 'arousal_mean': '6.1799999999999997',\n", + " 'arousal_sd': '2.25',\n", + " 'dominance_mean': '4.5199999999999996',\n", + " 'dominance_sd': '2.5600000000000001',\n", + " 'word_frequency': '14',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'immoral',\n", + " 'word_no': '807',\n", + " 'valence_mean': '3.5',\n", + " 'valence_sd': '2.1600000000000001',\n", + " 'arousal_mean': '4.9800000000000004',\n", + " 'arousal_sd': '2.48',\n", + " 'dominance_mean': '4.6600000000000001',\n", + " 'dominance_sd': '2.3300000000000001',\n", + " 'word_frequency': '5',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'dreadful',\n", + " 'word_no': '131',\n", + " 'valence_mean': '2.2599999999999998',\n", + " 'valence_sd': '1.9099999999999999',\n", + " 'arousal_mean': '5.8399999999999999',\n", + " 'arousal_sd': '2.6200000000000001',\n", + " 'dominance_mean': '4.0999999999999996',\n", + " 'dominance_sd': '2.3599999999999999',\n", + " 'word_frequency': '10',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'slum',\n", + " 'word_no': '401',\n", + " 'valence_mean': '2.3900000000000001',\n", + " 'valence_sd': '1.25',\n", + " 'arousal_mean': '4.7800000000000002',\n", + " 'arousal_sd': '2.52',\n", + " 'dominance_mean': '3.8300000000000001',\n", + " 'dominance_sd': '2.1800000000000002',\n", + " 'word_frequency': '8',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'alimony',\n", + " 'word_no': '634',\n", + " 'valence_mean': '3.9500000000000002',\n", + " 'valence_sd': '2.0',\n", + " 'arousal_mean': '4.2999999999999998',\n", + " 'arousal_sd': '2.29',\n", + " 'dominance_mean': '4.6299999999999999',\n", + " 'dominance_sd': '2.2999999999999998',\n", + " 'word_frequency': '2',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'bankrupt',\n", + " 'word_no': '32',\n", + " 'valence_mean': '2.0',\n", + " 'valence_sd': '1.3100000000000001',\n", + " 'arousal_mean': '6.21',\n", + " 'arousal_sd': '2.79',\n", + " 'dominance_mean': '3.27',\n", + " 'dominance_sd': '2.3900000000000001',\n", + " 'word_frequency': '5',\n", + " 'novelty': 'lure',\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': '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': 'loser',\n", + " 'word_no': '851',\n", + " 'valence_mean': '2.25',\n", + " 'valence_sd': '1.48',\n", + " 'arousal_mean': '4.9500000000000002',\n", + " 'arousal_sd': '2.5699999999999998',\n", + " 'dominance_mean': '3.02',\n", + " 'dominance_sd': '2.1699999999999999',\n", + " 'word_frequency': '1',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'helpless',\n", + " 'word_no': '206',\n", + " 'valence_mean': '2.2000000000000002',\n", + " 'valence_sd': '1.4199999999999999',\n", + " 'arousal_mean': '5.3399999999999999',\n", + " 'arousal_sd': '2.52',\n", + " 'dominance_mean': '2.27',\n", + " 'dominance_sd': '1.8300000000000001',\n", + " 'word_frequency': '21',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'mistake',\n", + " 'word_no': '880',\n", + " 'valence_mean': '2.8599999999999999',\n", + " 'valence_sd': '1.79',\n", + " 'arousal_mean': '5.1799999999999997',\n", + " 'arousal_sd': '2.4199999999999999',\n", + " 'dominance_mean': '3.8599999999999999',\n", + " 'dominance_sd': '2.4199999999999999',\n", + " 'word_frequency': '34',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'blind',\n", + " 'word_no': '43',\n", + " 'valence_mean': '3.0499999999999998',\n", + " 'valence_sd': '1.99',\n", + " 'arousal_mean': '4.3899999999999997',\n", + " 'arousal_sd': '2.3599999999999999',\n", + " 'dominance_mean': '3.2799999999999998',\n", + " 'dominance_sd': '1.9099999999999999',\n", + " 'word_frequency': '47',\n", + " 'novelty': 'target',\n", + " 'cond': 'neg'},\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': 'lure',\n", + " 'cond': 'neg'},\n", + " {'valence': 'neg',\n", + " 'description': 'corrupt',\n", + " 'word_no': '702',\n", + " 'valence_mean': '3.3199999999999998',\n", + " 'valence_sd': '2.3199999999999998',\n", + " 'arousal_mean': '4.6699999999999999',\n", + " 'arousal_sd': '2.3500000000000001',\n", + " 'dominance_mean': '4.6399999999999997',\n", + " 'dominance_sd': '2.2999999999999998',\n", + " 'word_frequency': '8',\n", + " 'novelty': 'lure',\n", + " 'cond': 'neg'}]}" + ] + }, + "execution_count": 13, + "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": 2, + "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": 3, + "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": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "indoor: 245\n", + "outdoor: 219\n" + ] + }, + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 4, + "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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'study': [{'filename': 'in0217.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0339.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0036.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0311.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0343.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0024.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0263.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0157.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0207.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0183.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'}],\n", + " 'test': [{'filename': 'in0147.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'lure',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0132.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'lure',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0267.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'lure',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0339.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0024.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0263.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0071.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'lure',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0343.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0119.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'lure',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0183.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0036.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0369.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'lure',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0157.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0096.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'lure',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0217.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0207.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0229.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'lure',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0311.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'target',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0329.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'lure',\n", + " 'cond': 'indoor'},\n", + " {'filename': 'in0111.jpg',\n", + " 'in_out': 'indoor',\n", + " 'novelty': 'lure',\n", + " 'cond': 'indoor'}]}" + ] + }, + "execution_count": 10, + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CS4500_CompMethods/assignments/A04_ListGen_chr4qt.ipynb b/CS4500_CompMethods/assignments/A04_ListGen_chr4qt.ipynb new file mode 100644 index 0000000..39d2cbe --- /dev/null +++ b/CS4500_CompMethods/assignments/A04_ListGen_chr4qt.ipynb @@ -0,0 +1,307 @@ +{ + "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": "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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## I will be working on Option 2: Scene Study" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'Study 2': [{'stimulus': 'out1118.jpg', 'pool_type': 'outdoor', 'condition': 'pure', 'novelty': 'target'}], 'Test 2': [{'stimulus': 'out0041_new.jpg', 'pool_type': 'outdoor', 'condition': 'pure', 'novelty': 'lure'}, {'stimulus': 'out1118.jpg', 'pool_type': 'outdoor', 'condition': 'pure', 'novelty': 'target'}]}, {'Study 1': [{'stimulus': 'in0262.jpg', 'pool_type': 'indoor', 'condition': 'pure', 'novelty': 'target'}], 'Test 1': [{'stimulus': 'in0262.jpg', 'pool_type': 'indoor', 'condition': 'pure', 'novelty': 'target'}, {'stimulus': 'in0276.jpg', 'pool_type': 'indoor', 'condition': 'pure', 'novelty': 'lure'}]}, {'Study 3': [{'stimulus': 'in0238.jpg', 'pool_type': 'indoor', 'condition': 'mixed', 'novelty': 'target'}, {'stimulus': 'out1352.jpg', 'pool_type': 'outdoor', 'condition': 'mixed', 'novelty': 'target'}], 'Test 3': [{'stimulus': 'in0238.jpg', 'pool_type': 'indoor', 'condition': 'mixed', 'novelty': 'target'}, {'stimulus': 'out1352.jpg', 'pool_type': 'outdoor', 'condition': 'mixed', 'novelty': 'target'}, {'stimulus': 'out0076_new.jpg', 'pool_type': 'outdoor', 'condition': 'mixed', 'novelty': 'lure'}, {'stimulus': 'in0268.jpg', 'pool_type': 'indoor', 'condition': 'mixed', 'novelty': 'lure'}]}]\n" + ] + } + ], + "source": [ + "# Import necesary modules for csv reading and randomization\n", + "import csv\n", + "import random\n", + "\n", + "\n", + "# Variable assignments for total number of trials per condition during experiment,\n", + "# number of stimuli per experiment, block list to hold all Study/Test sets,\n", + "# and identifier number for trial. (Do not alter 'blocks', starts with Trial 1)\n", + "total = 1\n", + "stimnum = 1\n", + "trial = 1\n", + "blocks = []\n", + "\n", + "\n", + "# Create lists of content read from indoor and outdoor csv files, then shuffles\n", + "in_dr = csv.DictReader(open('indoor.csv','r'))\n", + "in_lst = [l for l in in_dr]\n", + "random.shuffle(in_lst)\n", + "out_dr = csv.DictReader(open('outdoor.csv','r'))\n", + "out_lst = [l for l in out_dr]\n", + "random.shuffle(out_lst)\n", + "\n", + "\n", + "def create_set(lst_pick, cond):\n", + " '''\n", + " Creates unique study sets and matching test sets for each trial\n", + " using list pick (in_lst or out_lst) and condition (pure or mixed)\n", + " '''\n", + " # Dictionary with stimuli sets as a list value for the chosen application\n", + " # as its key, either Study or Test and marked with the trial number\n", + " app = {\n", + " ('Study '+str(trial)): [],\n", + " ('Test '+str(trial)): []\n", + " }\n", + " targets = []\n", + " for num in range(stimnum):\n", + " # Assigns Study key the value of a list of dictionaries with stimulus info\n", + " # Also appends dictionaries to 'target' list\n", + " info = lst_pick.pop()\n", + " entry = {\n", + " 'stimulus': info['filename'],\n", + " 'pool_type': info['in_out'], \n", + " 'condition': cond,\n", + " 'novelty': 'target'\n", + " }\n", + " app[('Study '+str(trial))].append(entry)\n", + " targets.append(entry)\n", + " for num in range(stimnum):\n", + " # Assigns Test key the value of a list of dictionaries with stimulus info\n", + " info = lst_pick.pop()\n", + " entry = {\n", + " 'stimulus': info['filename'],\n", + " 'pool_type': info['in_out'], \n", + " 'condition': cond,\n", + " 'novelty': 'lure'\n", + " }\n", + " app[('Test '+str(trial))].append(entry)\n", + " for each in targets:\n", + " # Takes 'target' values that were studied and adds to Test list\n", + " app[('Test '+str(trial))].append(each)\n", + " # Randomizes order of Study/Test material and returns application dictionary\n", + " random.shuffle(app[('Study '+str(trial))])\n", + " random.shuffle(app[('Test '+str(trial))])\n", + " return app\n", + "\n", + "\n", + "def expt():\n", + " '''\n", + " Function returns list of desired number of trials with Study/Test pairs.\n", + " Creates block of Study/Test pair for each condition\n", + " '''\n", + " global total, trial, blocks\n", + " # Runs through the while loop to create a Study/Test dictionary pair for\n", + " # each condition using the desired total amount of trials per condition\n", + " while total > 0:\n", + " # First makes dictionary for Pure Indoor condition and appends to block\n", + " cond = 'pure'\n", + " lst_pick = in_lst\n", + " app = create_set(lst_pick, cond) \n", + " blocks.append(app)\n", + " trial += 1\n", + " # Then makes dictionary for Pure Outdoor condition and appends to block\n", + " cond = 'pure'\n", + " lst_pick = out_lst\n", + " app = create_set(lst_pick, cond)\n", + " blocks.append(app)\n", + " trial += 1\n", + " # Finally makes dictionary for Mixed condition and appends to block\n", + " cond = 'mixed'\n", + " lst_pick = in_lst\n", + " app = create_set(lst_pick, cond)\n", + " lst_pick = out_lst\n", + " app2 = create_set(lst_pick, cond)\n", + " # Made separate Study/Test dictionaries for indoor and outdoor stimuli\n", + " # then combined into one and added it to master 'blocks' list\n", + " app['Study '+str(trial)].extend(app2['Study '+str(trial)])\n", + " app['Test '+str(trial)].extend(app2['Test '+str(trial)])\n", + " blocks.append(app)\n", + " total -= 1\n", + " trial += 1\n", + " return blocks\n", + "\n", + "\n", + "# Runs the experiment function to produce stimuli sets\n", + "expt() \n", + "\n", + "\n", + "# Final randomization for each Study/Test set and overall order of conditions.\n", + "# Prints off final blocks when finished\n", + "for app in blocks:\n", + " for each in app:\n", + " random.shuffle(app[each])\n", + "random.shuffle(blocks)\n", + "print(blocks)" + ] + } + ], + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CS4500_CompMethods/assignments/A05_SMILE_Experiment_chr4qt.ipynb b/CS4500_CompMethods/assignments/A05_SMILE_Experiment_chr4qt.ipynb new file mode 100644 index 0000000..5327798 --- /dev/null +++ b/CS4500_CompMethods/assignments/A05_SMILE_Experiment_chr4qt.ipynb @@ -0,0 +1,438 @@ +{ + "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": [], + "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", + " study_list = [] # fill the study list\n", + " for pool in pools: # loop over 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" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": false + }, + "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\n", + "\n", + "\n", + "# 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", + "\n", + "\n", + "# shuffle the pools\n", + "random.shuffle(indoor_pool)\n", + "random.shuffle(outdoor_pool)\n", + "\n", + "\n", + "# 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" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO ] [Logger ] Record log in C:\\Users\\Carlos Rodriguez\\.kivy\\logs\\kivy_20-11-28_1.txt\n", + "[INFO ] [Kivy ] v1.11.1\n", + "[INFO ] [Kivy ] Installed at \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\__init__.py\"\n", + "[INFO ] [Python ] v3.8.3 (default, Jul 2 2020, 17:30:36) [MSC v.1916 64 bit (AMD64)]\n", + "[INFO ] [Python ] Interpreter at \"C:\\ProgramData\\Anaconda3\\python.exe\"\n", + "[INFO ] [Factory ] 184 symbols loaded\n", + "[INFO ] [Image ] Providers: img_tex, img_dds, img_sdl2, img_pil, img_gif (img_ffpyplayer ignored)\n", + "[INFO ] [Text ] Provider: sdl2\n", + "[CRITICAL] [Camera ] Unable to find any valuable Camera provider. Please enable debug logging (e.g. add -d if running from the command line, or change the log level in the config) and re-run your app to identify potential causes\n", + "picamera - ModuleNotFoundError: No module named 'picamera'\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\__init__.py\", line 59, in core_select_lib\n", + " mod = __import__(name='{2}.{0}.{1}'.format(\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\camera\\camera_picamera.py\", line 18, in \n", + " from picamera import PiCamera\n", + "\n", + "gi - ModuleNotFoundError: No module named 'gi'\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\__init__.py\", line 59, in core_select_lib\n", + " mod = __import__(name='{2}.{0}.{1}'.format(\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\camera\\camera_gi.py\", line 10, in \n", + " from gi.repository import Gst\n", + "\n", + "opencv - ModuleNotFoundError: No module named 'cv2'\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\__init__.py\", line 59, in core_select_lib\n", + " mod = __import__(name='{2}.{0}.{1}'.format(\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\camera\\camera_opencv.py\", line 48, in \n", + " import cv2\n", + "\n", + "[INFO ] [Video ] Provider: null(['video_ffmpeg', 'video_ffpyplayer'] ignored)\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\" graphics system\n", + "[INFO ] [GL ] GLEW initialization succeeded\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: 4, 6\n", + "[INFO ] [GL ] Shading version \n", + "[INFO ] [GL ] Texture max size <16384>\n", + "[INFO ] [GL ] Texture max units <32>\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": [ + "# **The issue of improper image loading does not seem to appear\n", + "# if the cells are all restarted and then run**\n", + "# 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", + "\n", + "# Configuration for font sizes, key inputs, durations,\n", + "# and list of instructions for Scene Study Task\n", + "font_size = 50\n", + "resp_keys = ['T', 'L']\n", + "resp_map = {'target': 'T', 'lure': 'L'}\n", + "ISI_dur = 0.5\n", + "ISI_jitter = 0.25\n", + "inst_font_size = 38\n", + "inst_text = \"\"\"[b][u][size=45]SCENE STUDY INSTRUCTIONS[/size][/b][/u]\n", + "In this task, you will study a series of images\n", + "Afterwards, you must identify whether \n", + "an image shown in a test was studied or not.\n", + "Press T if the image was studied\n", + "Press L if the image is new\n", + " \n", + "Press SPACEBAR to continue.\"\"\"\n", + "\n", + "\n", + "# Create the experiment\n", + "exp = Experiment(name='SceneStudy', show_splash=False, \n", + " fullscreen=True)\n", + "\n", + "\n", + "# Create subroutine to display instructions\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, halign='center')\n", + " with UntilDone():\n", + " KeyPress(keys=['SPACEBAR'])\n", + "\n", + "\n", + "# Create subroutine for study sets to display images for set time,\n", + "# wait an interstimulus interval, and log the stimulus information\n", + "@Subroutine\n", + "def Study(self, block_num, trial_num, cur_trial):\n", + "# Debug(trial_type=cur_trial['in_out'],\n", + "# image_path=cur_trial['in_out'] + '/' + cur_trial['filename'])\n", + " stim = Image(source=(cur_trial['in_out'] + \"/\" + cur_trial['filename']), \n", + " width=400, height=400, allow_stretch=True)\n", + " with UntilDone():\n", + " Wait(ISI_dur*3)\n", + " Wait(ISI_dur, jitter=ISI_jitter)\n", + " Log(name='scene_study', # log the result of the trial\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", + "# Create subroutine for test sets to display either old target images\n", + "# or new lures and wait for one of the configured keypresses to be entered.\n", + "# Logs the information included response and response time after. \n", + "@Subroutine\n", + "def Test(self, block_num, trial_num, cur_trial):\n", + "# Debug(trial_type=cur_trial['in_out'],\n", + "# image_path=cur_trial['in_out'] + '/' + cur_trial['filename'])\n", + " stim = Image(source=(cur_trial['in_out'] + \"/\" + cur_trial['filename']), \n", + " width=400, height=400, allow_stretch=True)\n", + " with UntilDone():\n", + " Wait(until=stim.appear_time) # make sure the stimulus has appeared on the screen\n", + " kp = KeyPress(keys=resp_keys, # collect a response (with no timeout)\n", + " base_time=stim.appear_time['time'],\n", + " correct_resp=Ref.object(resp_map)[cur_trial['novelty']])\n", + " Wait(ISI_dur, jitter=ISI_jitter) # wait the ISI \n", + " Log(name='scene_test', # log the result of the trial\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", + "\n", + "# Have user enter ID info and then display instructions\n", + "InputSubject('Scene Study')\n", + "Instruct() \n", + "Wait(0.5)\n", + "\n", + "\n", + "# Establishes loop to go through Study/Test pairs for each condition\n", + "# using the set number of reps. Introduces the start of each block\n", + "with Loop(blocks) as block: \n", + " Label(text='Press the SPACEBAR to\\nstart the next block', \n", + " font_size=font_size, halign='center')\n", + " with UntilDone():\n", + " KeyPress(keys=['SPACEBAR'])\n", + " Wait(ISI_dur, jitter=ISI_jitter) # add in some delay before the start of the block\n", + " with Loop(block.current['study']) as study_trial:\n", + " Study(block.i, study_trial.i, study_trial.current)\n", + " Label(text='The test is about to start\\n\\nRemember\\nPress T if the image was studied\\nPress L if the image is new\\n',\n", + " font_size=font_size, halign='center')\n", + " with UntilDone():\n", + " Wait(ISI_dur*4) # wait the ISI \n", + " with Loop(block.current['test']) as test_trial:\n", + " Test(block.i, test_trial.i, test_trial.current)\n", + "\n", + "\n", + "# Display termination message at the end of the experiment\n", + "Label(text='Congratulations!\\nYou have finished the experiment\\n\\nPress SPACEBAR to exit', \n", + " font_size=font_size, halign='center')\n", + "with UntilDone():\n", + " KeyPress(keys=['SPACEBAR'])\n", + "\n", + " \n", + "# Run the experiment\n", + "exp.run()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: []\n", + "Index: []" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "\n", + "from smile.log import log2dl\n", + "dl=log2dl('data/SceneStudy/s001/20201020_004358/')\n", + "dl\n", + "\n", + "\n", + "df = pd.DataFrame(dl)\n", + "df.head(40)\n", + "\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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CS4500_CompMethods/assignments/A07_Recog_Plots.ipynb b/CS4500_CompMethods/assignments/A07_Recog_Plots.ipynb new file mode 100644 index 0000000..938fb93 --- /dev/null +++ b/CS4500_CompMethods/assignments/A07_Recog_Plots.ipynb @@ -0,0 +1,708 @@ +{ + "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": 118, + "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": 2, + "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": 3, + "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
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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": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data from each task\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", + "df_i.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Some data clean-up" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "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": 16, + "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": 16, + "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='XXX', # column that identifies a subject\n", + " withinvars=[XXX], # list of columns for grouping within subject\n", + " measvar='XXX') # dependent variable averaging over\n", + "res" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "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
\n", + "
" + ], + "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": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# must unstack and reset index to plot properly\n", + "res.unstack().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Log(RT)')" + ] + }, + "execution_count": 18, + "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='XXX', y='XXX', yerr='XXX', 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": 28, + "metadata": {}, + "outputs": [ + { + "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='XXX', # column that identifies a subject\n", + " withinvars=[XXX], # list of columns for grouping within subject\n", + " measvar='XXX') # dependent variable averaging over\n", + "\n", + "# generate the plot\n", + "ax = res.unstack().reset_index().plot(x='XXX', y='XXX', yerr='XXX', kind=\"bar\")\n", + "ax.set_xlabel('Condition')\n", + "ax.set_ylabel('Performance')" + ] + } + ], + "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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/assignments/A07_Recog_Plots_chr4qt.ipynb b/CS4500_CompMethods/assignments/A07_Recog_Plots_chr4qt.ipynb new file mode 100644 index 0000000..7612a67 --- /dev/null +++ b/CS4500_CompMethods/assignments/A07_Recog_Plots_chr4qt.ipynb @@ -0,0 +1,718 @@ +{ + "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": 14, + "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": 15, + "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": 16, + "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
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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": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data from each task\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", + "df_i.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Some data clean-up" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "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": 18, + "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": 18, + "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": 19, + "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
\n", + "
" + ], + "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": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# must unstack and reset index to plot properly\n", + "res.unstack().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Performance')" + ] + }, + "execution_count": 20, + "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": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Performance')" + ] + }, + "execution_count": 21, + "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')" + ] + } + ], + "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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/assignments/A08_DPrime_Plot.ipynb b/CS4500_CompMethods/assignments/A08_DPrime_Plot.ipynb new file mode 100644 index 0000000..81171d3 --- /dev/null +++ b/CS4500_CompMethods/assignments/A08_DPrime_Plot.ipynb @@ -0,0 +1,548 @@ +{ + "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": 6, + "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": 7, + "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": 8, + "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
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3FJ03239.6249330.0F240.4322950.0001820.807362...1.243.95000000000000022.58000000000000015.37000000000000011.639999999999999919lureneus0010
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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": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data from the word recog task\n", + "task_dir = os.path.join('..', 'lessons', 'data', '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": 9, + "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": 10, + "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": null, + "metadata": {}, + "outputs": [], + "source": [ + "# use the agg method to get the counts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# collapse the multi-index\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# use apply to add the dprime as a new column (axis=1 tells it to go by row)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# use ci_within to calcuate the mean and confidence interval of d-prime" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "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" + ] + } + ], + "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/CS4500_CompMethods/assignments/A08_DPrime_Plot_chr4qt.ipynb b/CS4500_CompMethods/assignments/A08_DPrime_Plot_chr4qt.ipynb new file mode 100644 index 0000000..bf05086 --- /dev/null +++ b/CS4500_CompMethods/assignments/A08_DPrime_Plot_chr4qt.ipynb @@ -0,0 +1,1035 @@ +{ + "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": 12, + "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": 13, + "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": 14, + "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
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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": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data from the word recog task\n", + "task_dir = os.path.join('..', 'lessons', 'data', '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": 15, + "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": 16, + "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": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# use the agg method to get the counts\n", + "wperf = df_w.groupby(['subj', 'cond', 'valence', 'novelty'])['old_resp'].agg(['sum', 'count', 'mean'])\n", + "wperf = wperf.unstack().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "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": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# collapse the multi-index\n", + "wperf.columns = ['_'.join(col).strip() if len(col[1]) > 0 else col[0] \n", + " for col in wperf.columns.values]\n", + "wperf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "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
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138 rows × 10 columns

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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", + "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": 20, + "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", + "wperf['dprime'] = wperf.apply(lambda x: calc_dprime(x['sum_target'], x['count_target'],\n", + " x['sum_lure'], x['count_lure']),\n", + " axis=1)\n", + "\n", + "wperf" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "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": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# use ci_within to calcuate the mean and confidence interval of d-prime\n", + "res = ci_within(wperf, indexvar='subj', \n", + " withinvars=['cond', 'valence'], \n", + " measvar='dprime').reset_index()\n", + "res" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "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", + "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" + ] + } + ], + "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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/assignments/A09_Final_Project_chr4qt.ipynb b/CS4500_CompMethods/assignments/A09_Final_Project_chr4qt.ipynb new file mode 100644 index 0000000..4fa0a47 --- /dev/null +++ b/CS4500_CompMethods/assignments/A09_Final_Project_chr4qt.ipynb @@ -0,0 +1,1824 @@ +{ + "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": 1, + "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", + "import arviz as az\n", + "import bambi as bmb\n", + "import statsmodels.formula.api as smf\n", + "import statsmodels.api as sm\n", + "\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": 2, + "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": 3, + "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": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data from the word recog task\n", + "task_dir = os.path.join('..', 'lessons', 'data', '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": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" + ] + } + ], + "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", + "The ability to correctly recognize studied words will increase under conditions in which tested word associations are uniform. Moreover, recognition performance will be better for words of smaller length than that of large length under uniform association. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Methods\n", + "\n", + "*This section should read like a methods section of a journal article. Fill in the two sub-sections below.*\n", + "\n", + "\n", + "## List generation\n", + "\n", + "Word recognition study/test lists were created using the Python programming language in a Jupyter Notebook. The purpose of this list generation was to create a list of words to be studied and pair that with a list of words to be tested. Test sets contained all of the words from its respective study set, as well as an equal number of unstudied words. The experiment pulled words from CSV files of 301, 292, and 208 words with positive, negative, and neutral emotional valences respectively. The words were randomized in order before being read in, after which time they were given descriptive dictionaries that included the key 'valence', paired with values of 'pos', 'neg', and 'neu' depending on the CSV source. Words were also given the key 'novelty', through which cells included in the study period would be marked as a 'target' and unstudied words appearing only in the test would be marked 'lure'. The experiment consisted of four types of study sets; one set with words from each respective valence (key/value pair 'cond':'pure') and a fourth study/test set containing words from all three valences (key/value pair 'cond':'mixed'). There were nine words in each of the pools, with the mixed pool containing three words from each of the valence pools. Each of the studied words were given the key 'novelty' with a value of 'target' since these words were to be ideally recognized by participants during the subsequent testing period. Unstudied words appearing solely in the test list were given a 'novelty' of 'lure'. A total of three study/test blocks were run for each valence. \n", + "\n", + "List generation used the import of \"random\", \"DictReader\", and \"copy\". \n", + "\n", + "\n", + "\n", + "## SMILE Experiment Details\n", + "\n", + "\n", + "Presentation of the study/test pairs to subjects was achieved using the State Machine Interface (SMILE) Library through Python in a Jupyter Notebook. A window on the participant's computer would open once the experiment was initiated. Before beginning the trials, participants entered an identifier to allow for collection and analysis of experiment results using the Slog method. SMILE subroutines were used to present each component of the experiment, which began with a subroutine to present instructions of the task. The instructions informed participants that they would begin by studying a list of items and then being asked to discriminate between items that were studied and novel items that were unstudied as fast as possible. Response keys were established and presented to subjects to identify 'target' vs 'lure' words ('target' and 'lure' keys were randomized between subject to be either 'F' or 'J'). Participants were alerted that between each study/test set, they were to solve simple math problems that would appear on screen for 20 seconds. \n", + "\n", + "Upon clicking \"Enter\", the screen would empty for 0.5 seconds before asking participants to again click \"Enter\" to begin the next study block. The screen would again go blank, this time for 2 seconds with a variation of 0.5 seconds, before presenting the set of study words from the study/test pair in the list generation one at a time at the center of the screen. Words would remain on screen for 0.5 seconds with 0.5 second variation before being replaced by the next word. After studying all of the words, the instructions for the math problems would appear instructing participants to indicate if the solution provided was correct or not. Response keys for correct problems coincided with the response for 'target' words. Participants were given a trial problem of each condition before clicking \"Enter\" to start the math. \n", + "\n", + "After solving the math problems for 20 seconds (included to avoid active word rehearsal), instructions appeared alerting participants of the start of the test cycle. They were reminded of the key responses and instructed to click \"Enter\" to start. Subjects would then be presented with one of the words from the respective test list of the study/test pair from the list generation. Only after the stimlus appeared would the system accept key press answers a response. For each tested word, the file would log the response of the subject, current trial, study/test block number, trial number, time of stimulus appearance, response time, and whether or not the response was correct when compared against the 'novelty' of the stimulus. After each response, there would be a wait of 0.5 seconds with 0.5 second variation, followed by the appearance of the next test word. Upon completion of the test, the experiment would again wait 0.5 seconds with 0.5 second variation before returning to the top of the loop to begin the next study/test block. This loop continued to repeat until all of the study/test blocks created in the list generation had been completed. Typical participants took approximately 20 minutes to complete the experiment. \n", + "\n", + "\n", + "Experiment presentation used the import of \"\\*\" from \"smile.common\", \"scale\" from \"smile.scale\", \"InputSubject\" from \"smile.startup\", ad \"MathDistract\" from \"smile.math_distract\" " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Results\n", + "\n", + "The purpose of the conducted tests was to determine if the conditions under which participants were tested (mixed vs pure) had a significant impact on recognition performance. It was further questioned whether the length of the words studied had an additional effect on the ability to correctly recognize stimuli. These investigations were achieved using condition and categorical word size as initial independent variables. These variables were measured against the mean performance of subjects in the word recognition task, provided as a percent correct out of the total number of words tested. All of the participants maintained recognition performance between 0.79 and 0.86 (range includes error limits). Initial observations of mean performance revealed no variation beyond provided error limits to support the initial claim.\n", + "\n", + "Results were additionally analyzed using a Bayesian Mixed-effects Regression model, in which the word length was treated as a continous variable relying on the number of letters in each word. It was again determined that neither condition nor word length had a significant effect on performance. Statistical models for both of the independent variables were within the observational likelihood for the general recognition studies. Larger experiments with greater participants may shift the results into significance in light of a current skew in intervals. Interestingly, the preliminary trend being observed in regards to number length is opposite of the proposed correlation. The applied Bayesian models suggest the potential for an an increase in test performance as word length is increased. The suggested trend in regards to condition does align with the one proposed. To reiterate, however, none of these results have enough significance to garner substantial claims. The final results of this study refute the proposed claims of a combinatorial effect of word length and testing conditions on recognition performance. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Data processing and visualization\n", + "\n", + "*With the lessons as a guide, process your data to create the necessary data frame to plot the visualization associated with the question stated above. Then plot those data.*" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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resp_map_lureresp_map_targetblock_numtrial_numstim_on_timestim_on_errorrespresp_time_timeresp_time_errorrt...noveltycondsubjlog_numlog_rtold_respabs_valenceabs_arousalnum_lettersword_size
0FJ00234.3955110.0J235.2848330.0001800.889323...targetpures0010-0.11729510.920.316medium
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4FJ04241.4322090.0F242.5452270.0001921.113017...lurepures00100.10707500.951.324small
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5 rows × 32 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", + " novelty cond subj log_num log_rt old_resp abs_valence abs_arousal \\\n", + "0 target pure s001 0 -0.117295 1 0.92 0.31 \n", + "1 lure pure s001 0 0.138906 0 0.88 0.88 \n", + "2 lure pure s001 0 0.140229 0 0.60 0.45 \n", + "3 lure pure s001 0 -0.213983 0 0.24 1.05 \n", + "4 lure pure s001 0 0.107075 0 0.95 1.32 \n", + "\n", + " num_letters word_size \n", + "0 6 medium \n", + "1 7 medium \n", + "2 5 small \n", + "3 3 small \n", + "4 4 small \n", + "\n", + "[5 rows x 32 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Appends columns to the created list that include\n", + "# categorical and continuous word length groups\n", + "length = [] # Continuous length given in numbers\n", + "size = [] # Categorical size given in \"small\", \"medium\", or \"large\"\n", + "for each in df_w['description']:\n", + " length.append(len(each))\n", + " if len(each) >= 3 and len(each) <= 5:\n", + " size.append('small')\n", + " if len(each) >= 6 and len(each) <= 7:\n", + " size.append('medium')\n", + " if len(each) >= 8 and len(each) <= 11:\n", + " size.append('large')\n", + " \n", + " \n", + "df_w['num_letters'] = length\n", + "df_w['word_size'] = size\n", + "df_w.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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meanstdsemcilen
condword_size
mixedlarge0.8195860.4158440.0127490.0250151064.0
medium0.8269880.4045500.0100850.0197821609.0
small0.8257810.4102340.0098260.0192721743.0
purelarge0.8177950.4086800.0101760.0199591613.0
medium0.8356660.3950290.0079440.0155772473.0
small0.8224170.4042640.0080250.0157352538.0
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" + ], + "text/plain": [ + " mean std sem ci len\n", + "cond word_size \n", + "mixed large 0.819586 0.415844 0.012749 0.025015 1064.0\n", + " medium 0.826988 0.404550 0.010085 0.019782 1609.0\n", + " small 0.825781 0.410234 0.009826 0.019272 1743.0\n", + "pure large 0.817795 0.408680 0.010176 0.019959 1613.0\n", + " medium 0.835666 0.395029 0.007944 0.015577 2473.0\n", + " small 0.822417 0.404264 0.008025 0.015735 2538.0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get the error corrected by condition and whether they said old\n", + "res = ci_within(df_w, \n", + " indexvar='subj', # column that identifies a subject\n", + " withinvars=['cond', 'word_size'], # list of columns for grouping within subject\n", + " measvar='correct') # dependent variable averaging over\n", + "res" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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condmeanstdsemcilen
word_sizelargemediumsmalllargemediumsmalllargemediumsmalllargemediumsmalllargemediumsmall
0mixed0.8195860.8269880.8257810.4158440.4045500.4102340.0127490.0100850.0098260.0250150.0197820.0192721064.01609.01743.0
1pure0.8177950.8356660.8224170.4086800.3950290.4042640.0101760.0079440.0080250.0199590.0155770.0157351613.02473.02538.0
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" + ], + "text/plain": [ + " cond mean std \\\n", + "word_size large medium small large medium small \n", + "0 mixed 0.819586 0.826988 0.825781 0.415844 0.404550 0.410234 \n", + "1 pure 0.817795 0.835666 0.822417 0.408680 0.395029 0.404264 \n", + "\n", + " sem ci len \\\n", + "word_size large medium small large medium small large \n", + "0 0.012749 0.010085 0.009826 0.025015 0.019782 0.019272 1064.0 \n", + "1 0.010176 0.007944 0.008025 0.019959 0.015577 0.015735 1613.0 \n", + "\n", + " \n", + "word_size medium small \n", + "0 1609.0 1743.0 \n", + "1 2473.0 2538.0 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res.unstack().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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condword_sizemeanstdsemcilen
0mixedlarge0.8195860.4158440.0127490.0250151064.0
1mixedmedium0.8269880.4045500.0100850.0197821609.0
2mixedsmall0.8257810.4102340.0098260.0192721743.0
3purelarge0.8177950.4086800.0101760.0199591613.0
4puremedium0.8356660.3950290.0079440.0155772473.0
5puresmall0.8224170.4042640.0080250.0157352538.0
\n", + "
" + ], + "text/plain": [ + " cond word_size mean std sem ci len\n", + "0 mixed large 0.819586 0.415844 0.012749 0.025015 1064.0\n", + "1 mixed medium 0.826988 0.404550 0.010085 0.019782 1609.0\n", + "2 mixed small 0.825781 0.410234 0.009826 0.019272 1743.0\n", + "3 pure large 0.817795 0.408680 0.010176 0.019959 1613.0\n", + "4 pure medium 0.835666 0.395029 0.007944 0.015577 2473.0\n", + "5 pure small 0.822417 0.404264 0.008025 0.015735 2538.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res = res.reset_index()\n", + "res" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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p165dWvH+Cu37aZ/+veXfevSRRzVhwgT5+PhY+8je2Pu+URVOSOFChFoAQIPTuXNnzZ83XxkZGYqMjNQDDzwgHx8fW5cFoAGzv+9vAAAAcM0h1AIAAMDwCLUAAAAwPEItAAAADI9QCwAAAMMj1AIAAMDwCLUAAAAwPEItAAAADI9QCwAAAMMj1AIAAMDwCLUAAAAwPEItAAAADI9QCwAAAMMj1AIAAMDwCLUAAAAwPEItAAAADI9QCwAAAMMj1AIAAMDwCLUAAAAwPEItAAAADI9QCwAAAMNrZOsCJOnUqVNKSEjQzp075eLiori4OMXGxlbZdvPmzUpOTpbFYpG3t7eGDh2qvn37SpK2bdum9957TydPnpTJZFJERIQmTJigZs2a1efmAAAAoJ41iFD7xhtvqLS0VElJScrIyNAzzzyjwMBARUZGVmiXlZWlxYsXa+bMmYqKitLevXsVHx+v0NBQBQUFKSwsTM8//7y8vb1VUlKiFStWKCEhQc8884yNtgwAAAD1webDD4qKirRlyxaNHDlSrq6uCg0NVb9+/bRu3bpKbbOysuTm5qYePXrIwcFBERER8vPz05EjRyRJzZo1k7e3t7W9g4ODjh07Vm/bAgAArm1z5sxRkyZNbF2G1YYNG/Tiiy9Wer2h1VkfbB5qjx49KkkKCgqyvhYSEqJDhw5Vatu+fXv5+/tr69atKisrU1pamnJychQeHm5tc/DgQQ0bNkz33nuvPv30U/3xj3+8+hsBAABgA9WFWntk8+EHRUVFcnFxqfCam5ubCgsLK7U1m83q37+/lixZouLiYplMJk2ePFlNmza1tgkODlZycrLy8vK0du1atW7dutJ6LBaLLBaL9bnJZFLz5s3rcKuMy2w2Wx/P/9temUwm66O994XEvlFf6OeKOA5/x74BXJzNQ62zs3OlAFtQUFAp6ErSzp07lZSUpGeffVbt2rVTenq65s6dK3d3d0VFRVVo6+HhoX79+mnq1Kl6++23K/wAWL16tZYtW2Z9PnbsWE2ePLmOt8yY8vPzJUmenp4VhnLYo/N94e7ubvd9IbFv1Bf6uSKOw9+xbxhDenq6Zs6cqS+++EIFBQWKiorSyy+/rG7dulnbBAcH684771SHDh20YMEC5eTkqG/fvlq2bFmFk2x79uzRww8/rO3btysgIEDx8fFatWqVTp06pQ0bNmjOnDl69tlnJZ0bcilJMTEx2rBhg3UdaWlpmjRpknbu3KmQkBAtWrRIAwYMqJ/OqGc2D7UBAQGSpCNHjqhVq1aSpAMHDlR5hvXQoUMKDw9Xhw4dJJ0bstC9e3elpqZWCrWSVFpaqtzcXJ0+fVru7u7W1wcPHqyYmBjrc5PJpOzs7DrdLqPKzc21Ptp7n5z/BZKfn2/3fSGxb9QX+rkijsPfsW9U1tDCfXZ2tm6++WY1adJEr776qjw9PfXqq6+qX79+2r9/v3x9fa1tU1JStH//fiUkJMhiseixxx7TI488opUrV0qSCgsLddttt8nLy0t/+9vfJEnx8fHKyclRWFiYJOnBBx9Uenq63n//fX311VeSzp3UO+/MmTMaOXKkpkyZotmzZ2vevHkaPHiwDh06dE3ODGXzUOvs7KxevXppxYoVmjJlio4fP67169drxowZldqGhYXpww8/1P79+xUWFqb09HTt2LFDQ4YMkSRt3LhR7dq1U4sWLZSTk6O33npLoaGhFQKtJPn4+MjHx8f63GKxqLS09OpuqEGc74fS0lK775OysjLro733hcS+UV/o54o4Dn/HvtHwLVmyRDk5Odq+fbs1wPbv319t27bVwoUL9dJLL1nblpeXKyUlRU5OTpKkn3/+WS+99JLKyspkMpmUlJSk3377TZs3b1abNm0kSdddd53atWtnDbWBgYEKDAyUyWRSz549K9VTUlKi+fPnW6dJDQ0NVVhYmNauXauRI0de1b6wBZuHWkmaMGGCli5dqrFjx8rV1VUjRoxQly5dJElxcXGKj49XRESEOnXqpFGjRmnRokXKzs6Wm5ub+vTpo1tvvVWSlJGRobffflv5+flydXVV586d9eSTT9py0wAAgJ3417/+pb59+6pp06Y6e/aspHNjoKOjo/Xtt99WaBsTE2MNtJLUsWNHnTlzRpmZmWrZsqW+/fZbRUZGWgOtdC6UdurU6bLrMZlMuuWWW6zP27ZtK0dHR6Wnp9d2Exu0BhFqmzRpopkzZ1a5bNWqVRWeDxw4UAMHDqyy7dChQzV06NA6rw8AAOBSLBaLtm3bpsaNG1daFhoaWuG5l5dXheeOjo6Szl1AL507UVfVRey+vr46c+bMZdXj4uJiXe95jRs3tn7GtaZBhFoAAACja9q0qW6//XY999xzlZZdeFb2cvj5+en777+v9HpmZmaDG0vcUBBqAQAA6sAtt9yiv/3tbwoPD5ebm9sVrSsqKkrvvvuuDhw4YB2C8Msvv2j37t2Kjo62tnN0dFRxcfEVfda1otahNi0tTV9++aX+85//6LffflNhYaGaNWum9u3bq3fv3rr99tuv+H8oAABAQ1NaWqqPPvqo0uvjx4/XihUrFBMTo0cffVRBQUHKysrSf/7zH/n7++vxxx+/7M+4//779cILL+jOO+/U3LlzVV5ervj4eLVs2dI6f7MkhYeH6+zZs3rllVd00003ycPDQ+3bt6+T7TSaGoXa8vJyvfPOO3rllVf0ww8/yMvLS5GRkfL19ZWzs7Oys7P15ZdfaunSpXJzc9PQoUP11FNPKTg4+CqVDwAAUL+KioqsMy9dKCkpSdu2bdPTTz+tJ554QidOnJCvr6969uype+65p0af4eLion/961+aOHGihg8froCAAD3zzDN666235OnpaW131113adKkSZo3b54yMzPVu3fvCvPU2hOH8vLy8stt3LFjRxUVFWn06NEaMmSIIiIiqmyXn5+vtWvX6oMPPtAXX3yhZcuWafjw4XVWdF278O5i9i49PV3XXXedvvvuOwUGBtq6HJvKyMhQZGSk0tLS5OfnZ+tybI59o37QzxVxHP6OfaOyC6fntAcnTpxQSEiIpk6dqvj4eFuX0+DU6EztU089pWHDhlU47V0Vd3d3xcXFKS4uTgcOHNDRo0evqEgAAAB78+c//1ktWrRQcHCwMjIytHDhQpWVlemBBx6wdWkNUo1C7YgRI2r8AW3atKkwxxpgFCdPnpQknTp1ysaVAADskdls1gsvvKD09HQ1atRIN9xwg7766ivrHVhR0cVPuV5Ev379tG/fviqX/fTTT+rXr1+tiwJs5cyZM3r//ffV95a+6tOnjySpV69eum/4ffr6669tWxwAwK786U9/0v79+1VYWKj8/HytW7dOUVFRti6rwar17AcbNmxQXl5elcvy8vK0cePGWhcF2EJBQYFGjBqhnWk71XpIqG577i45eTnp1OF8/frhLxo2YpgmjJ+gOfFz5ODgYOtyAQD1KD8//6qt293d/aqt255c0Ty11f1i//e//2295zFgFJMe+T/tObxXt/zjDrn6/T4dnaufm3xvaKngIaFa9uAy+fv5a8KECTasFAAA/K8aDT+YN2+ePDw85OHhIQcHB/Xt29f6/Px/Tk5OevzxxzV48OCrVTNQ5/bt26c1//hcPf5yc4VAeyHfG1qq84zrtPiVxSopKannCgEAwMXU6EztTTfdpGnTpqm8vFxz587VsGHDKk0r4ujoqPDwcN111111WihwNb3z7jvyi/KXd8emF20XfE9b7Vn8g9auXau77767nqoDAACXUqNQGxMTo5iYGEnnhh489NBD8vf3vyqFAfVp195danpj80u2a9yksXwifbVv3z5CLQAADUitx9ROnTq12qmOMjIy5O7uriZNmtS6MKDBcjh3dz0AAC5UXl6ujRs3KiUlRbk5OfL08tKgQYPUu3fvWl1gHBwcrMTERN1+++1XodprT62n9HrwwQc1e/bsKpfFx8dr/PjxtS4KqG8d23fUye0nLtnu7OkzsqRl2c19tQsLC7Vy5Urdesvt6tnzJjk4mHXffSP03nvv6fTp07YuDwAajNTUVHW/7jrdc/fd+vmfa2X6Yad+/uda3XP33ep+3XVKTU21dYnXvFqH2o0bN+qOO+6ocllsbKy++eabWhcF1Lcxo8coY1u6cv6bfdF2B//+i1wcnRUbG1tPldnO4cOH1Semv2bMeFpnirrq5hteVb/oN9TYoZeefvp59erVW7/88outywQAm0tNTdUdsQPVpZGDvhl8p97s20vP3dhdb/btpW8G36lIs3RH7MB6CbZnzpy56p/RUNU61GZnZ1c7r5qbm5tOnLj0WS+goYiIiNCAgQO0fcpmnf6toMo2WamZ2vXSd3rskcfk5ORUzxXWr9zcXN1zz70qKW6hewZuUPeuMxXoF6NAvxh16zJDfxj4tcwK1T333MuxDsCulZeXa/y4cYptFaAXe3aTr6tLheW+ri6ad2N3DQz01/hx42o1fG3Hjh266aab5OXlpZYtW2rSpEkqLi62LndwcNDSpUvVvn17NWvWTJKUnJyskJAQeXt7609/+pN69uypt99+2/qe9957TxEREfLy8lJ0dLT27NlTuw5oQGodakNCQrRu3boql61fv17BwcG1XTVgE4mvJSqsRVutv3uNdi3eqbyfc1R0olCW77K0Y9ZWbRz9pUaPGK1JkybZutSr7t1331Vu7hn1vjFBjRtXHhvfuJGronv+RcVFjnrrrbdsUCEANAwbN27UgYMH9XiXiGrHzTo4OOjxrp104NBBbdq0qcafYTabtXDhQlksFm3btk1ff/21Xn311QptPvroI23atEnHjx/Xf//7X40bN05//etflZmZqebNm1c4S/yPf/xDs2fPVnJysk6cOKGRI0fqrrvuMvx0lVc0pnbx4sV66aWXZLFYJEkWi0ULFizQyy+/rIceeqjOigTqQ5MmTfTxhx9r7pNzdfabYq0d+Kk+7blK6+PWKOBES72T9I5efOHFa/5uYmVlZVq+/G21DR6uxo1cq21nNjupbZuReuutd3T27Nl6rBAAGo6UlBRFB/pXOkP7v1q4uig6wF8pKSk1/ozrrrtON910kxo1aqTg4GBNmDCh0jDPmTNnytfXVy4uLvrggw8UGxurfv36qXHjxpo+fbq8vb2tbV9//XU98cQTioyMlNls1oQJE+Tg4KBt27bVuLaGpNazHzz++OP65ZdfNGvWLM2aNUuNGjWy/mKbOHGipk2bVmdFAvXFyclJY8aM0ejRo/XDDz/o1ltv1caNGxUeHm7r0upNVlaWjh49rKjI/pdsGxRwi7bvnKv09HS+nQFgl3JzctTC+fKGpPk6Oyon++LXblTlp59+0tSpU7Vjxw6dPn1aZ8+eVZcuXSq0CQoKsv772LFjatWqlfW5yWRSQECA9fnBgwc1ffp0zZo1y/paSUmJjh49WuPaGpJah1oHBwclJCToscce0/r163Xy5Ek1a9ZM/fr1U1hYWF3WCNQ7BwcHtWjRQpLk5eVl42rq1/k/Ts3mS/+QNpkcK7wHAOyNp5eXfi4qvnRDSZlFJQq74Izp5Xr44YfVuXNnvf/++/Lw8NArr7yi5OTkCm1Mpt+/fPf391daWpr1eVlZWYXAGhQUpBkzZmjs2LE1rqUhq3WoPS8sLIwQC1xDmjVrJmcnF53I3q0mbgEXbXsye48aNXK0/gEAAPZm0KBBumf5cmWeLrzoEITjpwu16egxTRs0qMafkZ+fLw8PD7m7u+unn35SYmKiPD09q20/ZMgQzZ8/Xxs2bNDNN9+sl19+WdkXnCF++OGH9cQTT+i6665TZGSkCgoK9PXXX6tPnz7VTgJgBLUeUyudmzYiMTFR48aN02233ab9+/dLkj744AP9+OOPdVIggPrl7Oyswffeq58PJF+y7f4DKzRo0F2G/iEIAFeid+/eahMcrMXf7652ZoPy8nK9/P1uhQS3UXR0dI0/Y+HChVq1apXc3d01btw4DRky5KLtw8PDtWzZMt1///1q3ry5jh8/rsjISOvMPXfffbfi4+M1ZswYeXl5KSwsTO+9916N62poan2m9tdff9Utt9yirKwsdenSRVu3blV+fr6kc1cCfvHFF0pKSqqzQnqxBPsAACAASURBVAHUn/HjH9QHH9yiH/e/p/CwUVW22f/rh0o/tlF/fXhtPVcHAA2Hg4OD3ly+XHfEDpS27tDUrp0qnLHNPF2oxd/v1tr0Y1qz9osaXWx88OBB67/37dtXYdncuXOt/64qTI8YMUIjRoyQdG6IWMuWLSuMsx02bJiGDRt22bUYQa1D7ZQpU9S8eXNt375dXl5ecnR0tC6LiYmpMPgYgLF07NhRr72WoIcfnqQT2T+oQ+ho+TSLlCSdyN6j//78nn45+ImWLHlZXbt2tXG1AGBb3bp10+dr1mr8uHGK+fgzRQf4y9fZUZlFJdp09JjatA7WmrVf6Prrr6+3mv7xj3+ob9++MpvNev755+Xq6qoePXrU2+fbQq1D7YYNG5ScnCwfHx+VlpZWWNayZUtlZGRccXH1xdHR8ZqfTP9yubm5WR/t/SvlnJwcSZKLi4td9sXo0aMVGhqqefP+rDXr75Wjo6vOni1VaWmRYmL66dXXPlefPn1sXeY1h2OwIns/Di/EvtGwdevWTTu++06bNm1SSkqKcrKzFebtrWmDBik6Orrep4P8/PPPNXr0aJWWlioyMlKffvpphROQ16Jah9pGjRpVO3bk+PHjatKk8oTtDVVJSYnhJxyuKwUFBdbH88NJ7FVhYaH10V77IjIyUsnJK3T48GFt2bJFU6ZMUUpKim688UZJstt+uZo4BiviOPwd+0ZlDe2ElIODg3r37q3evXvbuhQlJiYqMTHR1mXUq1pfKBYTE6NFixZVuMewg4ODysvL9eabb6p//0vPcQnAGIKCgqwXN1w4JgsAgIai1mdq58+fr169eik8PFx33323dd7a3bt3a//+/dq+fXtd1gkAAABUq9ZnasPDw5WamqpevXopOTlZZrNZn332mdq2bavt27crNDS0LusEAAAAqlWrM7XFxcVaunSpbrvtNr3zzjt1XRMAAABQI7UKtU5OTpo9e7aioqLquh4AAIAGhxknGr5aDz/o2rWr9u7dW5e1AAAAALVS6wvFXnnlFY0cOVK+vr4aOHCgXFyqv98xAAC4Muen0axuOk3A3tU61Pbr108lJSXW+w+7urpWmFjYwcFBubm5V14hAAB2bOvWrVq+PElr1qyRJN14402668679OBD49StWzcbV2c/rubcwAxtqBu1DrV/+tOf6rIOAABwgbKyMj355FN666231LpVf918wxK5ODfX6cLf9O321Vr98UA98sgUPf30U/V+tyqgIapVqC0pKVGnTp3UtWtXpu4CAOAqeOmll7Tibx/o9r7vy7f5hWdku6h14ABlHP+3El+fKG9vL02ePNlmdQINRa0uFHN0dNSIESN05MiRuq4HAAC7l52drVdfTdAN1z//P4H2d34tblL3rrO1cOHL1lvoAvas1rMfdOjQgVALAMBV8MEHH8jFuamCAgdctF1I60EqL2+kv//97/VUGdBw1TrUzps3T88//7xSU1Prsh4AAOzezp3fydcnWiaT+aLtzGYntWx+o3744Yd6qgwXU15erm+++UbTpk3TuAfHadq0afrmm28a5IwVLVu21IYNGyRJL774osaOHWvTeupCrS8UmzFjhiwWi3r06CEfHx/5+vpWmv2AgwwAgJo7e7ZUJpPrZbV1cGiss2fPXuWKcCmpqakaN36cDh44KP/oQDm1cFbxz0Vafs9yBbcJ1vI3lzfY2SqefPJJW5dQJ2odart166bu3bvXZS0AAEBS27Yh2r7ty0u2Ky8vV3buD2rTZmQ9VIXqpKamauAdsQqIbaU73x4sF9/f/yApzDyt3Yu/18A7YrX28zUNNtheC2o9/ODtt99WUlLSRf8DAAA1N2zYMGVa9ijrxPcXbZdx/N/KzTuioUOH1lNl+F/l5eUaN36cAmJbqduLPSsEWkly8XVV93k3yn9goMaNH1ejoQjBwcFasGCBrr/+erm5uWnQoEHKzs7WQw89JE9PT7Vv317bt2+XJOXl5WnChAkKDAxUy5YtNXnyZBUVFVnX9fLLLysgIEC+vr5atGhRhc+ZM2eO7rvvPknShg0b1LJlywrLe/bsqbffflvSufzXs2dPzZw5U82aNZO/v7/+/ve/a8OGDerYsaO8vLw0ceJEmwy5qHWovdDRo0e1a9cuHT16tC5WBwCAXWvTpo3uuGOQtqU+ocLCrCrbFJzO0PbvnlZc3FC1aNGinivEeRs3btTBAwcV8XiXaucLdnBwUKfHu+rggUPatGlTjda/atUqffbZZzp27Jh+/fVX9ezZU3fffbdOnjypoUOHWqdzu//++1VcXKy9e/dq37592r9/v5577jlJ0pdffqnnn39e//jHP3T48GHt379fFoul1tucmpqqNm3aKDMzU88995wefPBBJSYmasuWLdq7d6/+/ve/a+3atbVef21dUahdsWKFQkJCFBQUpK5duyooKEghISF6//3366o+AADs0quvLlFwm2Za+/UftXvfX1VUfFKSVFhk0a69iVr71T3q3DlEf/7zPBtXat9SUlLkHx1Y6Qzt/3Jp4Sr/6AClpKTUaP2TJ0+Wv7+/PD09FRsbK39/f915550ym82677779P333yszM1MpKSl69dVX5eHhIS8vLz399NNKTk6WJCUnJ2vs2LG6/vrr5ezsrPnz56usrKzW2xwYGKgJEybIbDZr2LBhOnnypB5++GF5e3vL399fMTEx2rlzZ63XX1u1HlObnJysUaNG6bbbblN8fLxatmyp3377TStXrtSoUaNkMpmsp7IBAEDNNGnSRJ98slpvvvmmli9/W6k//FkODiaVl5epVas2euKJRzVu3Dg5OjraulS7lpObI6cWzpfV1tHXWdk52TVa/4Vn4V1dXSs9P3PmjPbt26fS0lK1atXKuqy8vFylpaWSpGPHjqlLly7WZV5eXvLw8KhRHRerqarXTp06Vev111atQ+28efN0//33a/ny5RVeHzNmjMaNG6cXX3yRUAsAwBVwcXHRo48+qsmTJ+urr77S8OHDtXLlSvXt21cmU52MIMQV8vL0UvHPRZduKKkks0jeYd51XkO7du3UqFEjZWZmVvlHjr+/vw4fPmx9npubq7y8vCrX1aRJE50+fbrCa7/99lvdFnyV1PqI+Omnn6oNrUOHDtVPP/1U66IAAMDvzGazwsPDJUnt27cn0DYggwYN0rFN6SrMPH3RdoXHT+vYpqMaNGhQndfg5eWlO+64Q48++qiys7NVXl6uI0eO6IsvvpB0Lpe98847+v7771VUVKRZs2ZVuw+1a9dOpaWl+vjjj3X27FklJCQY5pqpWh8VPj4+2rNnT5XL9uzZIx8fn1oXBQAAYAS9e/dWcJtg7V78fbVX/JeXl2v3y9+rTUiwoqOjr0od77zzjho1aqSuXbvK09NTAwYMsJ5gHDBggGbNmqU77rhDQUFBCg0NrTaneXh4KDExUY888ohatGih3377zTDTkDmU13LOhWnTpikxMVGLFy9WXFycvL29lZOTo1WrVmnq1Kl6+OGHtWDBgrqu96q4kisArzXp6em67rrr9N133ykwMNDW5dhURkaGIiMjlZaWJj8/P1uXY3PsG/WDfq6I4/B37BuV1ecJtPz8/GqXnZ+n1n9goDpN7VrlPLXH1qbrizVrdf3111d6v7u7+1Wp2d7Uekztiy++qIMHD+rhhx/WpEmT1KhRI509e1bl5eUaPHiwXnjhhcte16lTp5SQkKCdO3fKxcVFcXFxio2NrbLt5s2blZycLIvFIm9vbw0dOlR9+/aVJK1fv16ff/65jh07JmdnZ/Xo0UP333+/XFxcaruZduXw4cPWu8BdOLcdAACoXrdu3bT28zUaN36cPov5WP7RAXL0dVZJZpGObTqq4Datqw20qDu1DrVOTk5avXq1du3apU2bNik7O1tNmzbVzTffrM6dO9doXW+88YZKS0uVlJSkjIwMPfPMMwoMDFRkZGSFdllZWVq8eLFmzpypqKgo7d27V/Hx8QoNDVVQUJCKi4v1wAMPqF27dioqKtKCBQuUlJSkSZMm1XYz7cKaNWuUuHSptn77rfW122+9VSNGj9b//d//ydfX14bVAQDQ8HXr1k3f7fhOmzZtUkpKirJzsuUd5q1B0wYpOjq62jlsUXdqHWrP69y5c41D7IWKioq0ZcsWLVmyRK6urgoNDVW/fv20bt26KkOtm5ubevToIUmKiIiQn5+fjhw5oqCgoApndx0dHTVgwAB98MEHta7NHjz/3HN6/bXXFNe2jZ4aNEBhXp4qPHtWXxxKV9KqD/Tpxx/r408/VUhIiK1LBQCgQXNwcFDv3r3Vu3dvW5dil2odaj/44AMdPnxY06dPr7Rs4cKFat26tYYMGXLJ9Zy/oi4oKMj6WkhIiD755JNKbdu3by9/f39t3bpVN9xwg3bv3q2cnBzrFaH/a/fu3RXWe57FYqkwjtZkMql58+aXrPVa8/777yvxtde0vN/N6un3+/xybo0ba3DbNrqzTZAe2bhNw4cO1ZZt2+xuLsTzV4aaTCaZzWYbV2N75/vAbDbTH1eRPfZzXl5etdMLHT9+vMLj//Lw8Lii+TaNxB73DaAmah1q58+fr/vvv7/KZS4uLpo/f/5lhdqioqJKY17d3NxUWFhYqa3ZbFb//v21ZMkSFRcXy2QyafLkyWratGmlttu2bdPGjRu1cOHCSstWr16tZcuWWZ+PHTvWeps5e1FeXq6/vPyyJkS0rxBoL+RkNmtRrx6K/vhzffPNN4qLi6vnKm3r/EUB7u7u8vau+3kFjeZ8f3h6etIfV5E99vMrr7yiZ5999qJtbr311ipfj4+P15w5c65CVQ2PPe4bQE3UOtT+9NNP6tSpU5XLOnbseNnz1Do7O1cKsAUFBVVe3LVz504lJSXp2WefVbt27ZSenq65c+fK3d1dUVFR1nY//PCDli5dqqefflr+/v6V1jN48GDFxMRYn5tMJmVn1+wOH0a3detW/XrggIYOvvOi7dwdG+sPbYL02l/+Uu0vlWvV+V8g+fn5drd/VCU3N9f6SH9cPfbYz/fff78GDx5c5TKTySR3d3fl5+dXeVtPDw8Pu+kne9w3LoVwjwvVOtQ6OztX+3VQRkaGGjW6vFUHBARIko4cOWK9vduBAwfUunXrSm0PHTqk8PBwdejQQdK5IQvdu3dXamqqNdSmpaXppZde0hNPPKGOHTtW+Zk+Pj4VpgGxWCzWW8nZi/379yvAy1O+rpeeGaKrT1Nt2P+z3fXR+V+gZWVldrftVTnfB6WlpfTHVWSP/ezm5iY3N7cql5nNZnl7eys7O7va/rCXfrLHfQOoiVrffCEmJkbz589XQUFBhdcLCgr00ksvqU+fPpe1HmdnZ/Xq1UsrVqzQ6dOndeDAAa1fv179+/ev1DYsLEz79u3T/v37JZ2bs2/Hjh1q06aNJGnXrl2aP3++pk2bVukiM1RkNptVWsVZj6qcLSu77D9SAAC4Frm7u1+1/1A3rmie2htvvFGhoaG699575e/vr2PHjumjjz5SSUmJVq5cednrmjBhgpYuXaqxY8fK1dVVI0aMUJcuXSRJcXFxio+PV0REhDp16qRRo0Zp0aJFys7Olpubm/r06WP9WnzlypU6ffq05s+fb1138+bNlZCQUNvNvGZ16dJF6Tm5+jknT229Ln6RxcbfMhXZtWs9VQYAAFBztQ61HTp00Lfffqv4+HitXr1aJ06cULNmzXTrrbcqPj5ebdu2vex1NWnSRDNnzqxy2apVqyo8HzhwoAYOHFhl25rc8MHehYeHq0e36/X2jz/p+Ru7V9su/VSBvjx4RB8uWlKP1QEAANTMFX2n3LZtW61YsaKuakE9m/nU04obMkTtvTw1Kjys0vLjpws1fsO/ddONN6pXr142qBAAgIbhYrfJvVIMQagbDJS0Y9HR0Xrt9df1f5MmKeXwUQ0Lba123p46feas1h5K1ycHDqtzly5Kevdd7oQCXGVFRUVKSUnRV199JencnRYffPDBKi+aBQBUVqMLxe6991599913l92+sLBQr7zyiv7617/WuDDUj3vuuUebNm/WDXf/QfPSftQ9n32pEf/8WnscnbXoL3/R6k8+sZuJzQFbee+99xTZqZNmTZumop079MfQYH2z+iNFRUXp/tGjrVM5AQCqV6MztcHBwerVq5c6dOige++9V7169VLnzp2tNz8oKSnRgQMHlJqaqrVr1yolJUXt2rVTYmLiVSkedSM0NFTPP/+8nnvuOf3888+66aab9NY77yowMNDWpQHXvNdff13PPfusnugWqbiwELn8/5lGysvL9b3lhGZv364/3n23Pv3sMzVp0sTG1QJAw1WjM7ULFy7U/v37deedd2rZsmXq27evmjdvrsaNG8vFxUUuLi7q2LGjxo4dq7y8PK1YsULffvutunXrdrXqRx1ycHCo8qYXAK6OX3/9VfHx8Vp08w0aE97OGmilc8fjdc199LdbopV77KgWLVpkw0oBoOGr8ZjagIAAzZ07V3PnztXPP/+sHTt2KCMjQ0VFRWratKnat2+vHj16yNXV9WrUCwDXjKSkJEX6NtfA4FbVtvFyctLE8DC99N57euKJJ+Ts7FyPFQKoifLycm3cuFEpKSnKycmVl5enBg0apN69e3NtSj2ocajds2eP3njjDR04cEABAQEaPHiw7rvvvqtRGwBc0z779BM91Kb6QHveHW2CNOfb7/Tvf/9b/fr1q4fKANRUamqqxo0br4MHDyjQP1rOTi1UVPyzli+/R8HBbbR8+ZsN8pvr4OBgJSYm6vbbb9ecOXO0b9++Gt1roCGpUajdvHmz+vfvr7Nnz8rHx0cnT57UsmXLlJCQoIkTJ16tGgHgmpSXl6+Wl/GtlkujRvJyduGCMaCBSk1NVezAO9QqIFaD73xbri6+1mWnCzP1/e7Fih14h9as/bxBBttrRY3G1M6ZM0cdO3bUwYMHdfz4cZ04cUJ/+MMf9PTTT1+t+gDgmuXu3kRZhYWXbFdcWqrcoiJmIgEaoPLyco0bN16tAmLVs9uLFQKtJLm6+OrG7vMU6D9Q48aNV3l5uY0qvfbVKNSmpaVp9uzZatXq3NdlHh4eWrRokU6ePKkjR45clQIB4Fp1x12D9PHBS//sXHPwsJydnXXjjTfWQ1UAamLjxo06ePCAukQ8Xu24WQcHB3Xt9LgOHTygTZs2Xfa6//znPyswMFAeHh4KCwvT2rVrNWfOHA0ePFgPPPCAPDw81LZtW23ZskWrVq1SSEiImjZtqrlz51rXceDAAfXv31/NmjWTj4+Phg0bpuzs7Cve7oaoRqHWYrFUmubpfMC1WCx1VxUA2IH7H3hAOzOOa93ho9W2ySspUeLe/bpvxAguwAUaoJSUFAX6R1c6Q/u/XF1aKMA/WikpKZe13v/+979aunSptm/frry8PK1bt05t27aVJH322Wf64x//qOzsbA0fPlzDhg3TunXrtHv3bm3evFkvvvii9uzZI+ncmeQnnnhCx44d048//qijR49q9uzZV7bRDVSNQq0krt4DgDrStm1bPfnkk3ps0zat/OkXFZeWVli+92S2Rq/fJCef5po+fbqNqgRwMTk5uXJ2anFZbZ0dfZWdnXNZbc1ms4qLi7Vnzx6dOXNGrVu3VljYuVva33TTTbrzzjtlNpt133336ciRI3rqqafk6uqqjh07KjIy0nqzrJCQEN12221ycnJS8+bNNXXqVH3zzTe129gGrsazH/Tt21cmU+UsHB0dXeF1BwcHLmoAgEt47LHH5ObmpvkvvqAlaXsV09JXzo3M+jE3X9/9lqlb+vVTwuuvM54WaKC8vDxVVPzzZbUtKsmUt3fYZbVt27atlixZorlz5youLk633HKLFi9eLElq0eL3EH3+G5z/fe3UqVOSpMzMTE2ZMkWbNm1Sfn6+ysrK5O7uflk1GE2NQm18fPzVqgMA7JKDg4PGjx+vESNG6OOPP9ZX69frk88/1+ixY/XahAnWrxsBNEyDBg3S8uX36HRh5kWHIJwuPK6jxzZp0KBpl73u4cOHa/jw4crLy9OkSZM0depURURE1Ki+WbNmqaysTGlpaWrWrJk+/fRTTZgwoUbrMApCLQA0AG5ubho1apT69u2rzz7/XI8++ii3qgYMoHfv3goObqPvdy/Wjd3nVTlMs7y8XN/vflnBbUIUHR19Wev973//q6NHj6pXr15ydnaWi4uLzp49W+P68vPz5ebmJk9PTx07dkwLFy6s8TqMosZjagEAAHCOg4ODli9/U+nH1mrrjlk6XZhZYfnpwkxt3TFL6cfWavnyNy/72qTi4mLNmjVLPj4+atGihY4ePaoFCxbUuL74+HilpaXJy8tLsbGx+sMf/lDjdRhFjcfUAgAA4HfdunXTmrWfa9y48fr4sxgF+EfL2dFXRSWZOnpsk1oHt9HaL9bo+uuvv+x1RkZG6j//+U+l1+fMmVPheXBwcKW5bzds2GD9d0REhFJTUyssnzbt9yEQBw8erHbdRkOoBQAAuELdunXTd9/t0KZNm5SSkqLs7Bx5e4dp0KBpio6OZvaoekCoBQAAqAMODg7q3bu3evfubetS7BJjagEAAGB4hFoAAAAYHqEWAAAAhkeoBQAAgOFxoRgAAMAlXKu3lr2WEGolOTo6ysnJydZlNAhubm7WR3s/gHNyciRJLi4udt8XEvtGfaGfKzo/DZKbm1uluTjtDfsGcHGEWkklJSUqKSmxdRkNQkFBgfUxPz/fxtXYVmFhofXR3vtCYt+oL/RzRWazWY6OjiooKFBpaamty7Ep9o3KOCGFCzGmFgAAAIZHqAUAAIDhEWoBAABgeIRaAAAAGB6hFgAAAIZHqAUAAIDhEWoBAABgeIRaAAAAGB6hFgAAAIZ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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = (pn.ggplot(res, pn.aes('cond','mean', fill='word_size'))\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(correct)\", fill='Length')\n", + " )\n", + "p" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A standard ggplot was used to display the performance of subjects as a function of condition. Performance was displayed as the mean percent of correct responses provided during the testing period for each conditin. Groups were additionally subdivided based on the categorical size of each word stimulus. Words with 3-5 letters were classified as \"small\", 6-7 letters as \"medium\", and 9-11 letters as \"large\". The categorical group distribution was adjusted to accomodate for large variation between words of each number legnth in the current study. Error bars were calculated using confidence intervals of correct performance among subjects. Observation of the error bars shows an initial lack of significance between performance and condition or word size. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Statistical test and interpretation\n", + "\n", + "*Perform a statistical test to support your conclusions with regard to your question outlined above. This can be with either statsmodels or with bambi.*" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjcondword_sizenoveltycorrect
0s001mixedlargelure0.952381
1s001mixedlargetarget0.807692
2s001mixedmediumlure0.925000
3s001mixedmediumtarget0.857143
4s001mixedsmalllure0.942857
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271s023purelargetarget0.700000
272s023puremediumlure0.745455
273s023puremediumtarget0.692308
274s023puresmalllure0.673077
275s023puresmalltarget0.723077
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276 rows × 5 columns

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" + ], + "text/plain": [ + " subj cond word_size novelty correct\n", + "0 s001 mixed large lure 0.952381\n", + "1 s001 mixed large target 0.807692\n", + "2 s001 mixed medium lure 0.925000\n", + "3 s001 mixed medium target 0.857143\n", + "4 s001 mixed small lure 0.942857\n", + ".. ... ... ... ... ...\n", + "271 s023 pure large target 0.700000\n", + "272 s023 pure medium lure 0.745455\n", + "273 s023 pure medium target 0.692308\n", + "274 s023 pure small lure 0.673077\n", + "275 s023 pure small target 0.723077\n", + "\n", + "[276 rows x 5 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wperf = df_w.groupby(['subj', 'cond', 'word_size', 'novelty'])['correct'].mean()\n", + "wperf = wperf.reset_index()\n", + "wperf" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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: 0.164
Model: OLS Adj. R-squared: 0.129
Method: Least Squares F-statistic: 4.704
Date: Tue, 08 Dec 2020 Prob (F-statistic): 1.40e-06
Time: 21:59:55 Log-Likelihood: 190.24
No. Observations: 276 AIC: -356.5
Df Residuals: 264 BIC: -313.0
Df Model: 11
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept 0.8739 0.026 33.749 0.000 0.823 0.925
cond[T.pure] -0.0142 0.037 -0.387 0.699 -0.086 0.058
novelty[T.target] -0.1085 0.037 -2.963 0.003 -0.181 -0.036
word_size[T.medium] 0.0122 0.037 0.334 0.738 -0.060 0.084
word_size[T.small] 0.0239 0.037 0.652 0.515 -0.048 0.096
cond[T.pure]:novelty[T.target] 0.0238 0.052 0.460 0.646 -0.078 0.126
cond[T.pure]:word_size[T.medium] -0.0032 0.052 -0.061 0.951 -0.105 0.099
cond[T.pure]:word_size[T.small] -0.0114 0.052 -0.221 0.825 -0.113 0.091
novelty[T.target]:word_size[T.medium] -0.0095 0.052 -0.184 0.854 -0.112 0.092
novelty[T.target]:word_size[T.small] -0.0363 0.052 -0.701 0.484 -0.138 0.066
cond[T.pure]:novelty[T.target]:word_size[T.medium] 0.0305 0.073 0.416 0.678 -0.114 0.175
cond[T.pure]:novelty[T.target]:word_size[T.small] 0.0185 0.073 0.252 0.801 -0.126 0.163
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Omnibus: 28.978 Durbin-Watson: 1.601
Prob(Omnibus): 0.000 Jarque-Bera (JB): 34.888
Skew: -0.815 Prob(JB): 2.66e-08
Kurtosis: 3.614 Cond. No. 25.6


Warnings:
[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: 0.164\n", + "Model: OLS Adj. R-squared: 0.129\n", + "Method: Least Squares F-statistic: 4.704\n", + "Date: Tue, 08 Dec 2020 Prob (F-statistic): 1.40e-06\n", + "Time: 21:59:55 Log-Likelihood: 190.24\n", + "No. Observations: 276 AIC: -356.5\n", + "Df Residuals: 264 BIC: -313.0\n", + "Df Model: 11 \n", + "Covariance Type: nonrobust \n", + "======================================================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "----------------------------------------------------------------------------------------------------------------------\n", + "Intercept 0.8739 0.026 33.749 0.000 0.823 0.925\n", + "cond[T.pure] -0.0142 0.037 -0.387 0.699 -0.086 0.058\n", + "novelty[T.target] -0.1085 0.037 -2.963 0.003 -0.181 -0.036\n", + "word_size[T.medium] 0.0122 0.037 0.334 0.738 -0.060 0.084\n", + "word_size[T.small] 0.0239 0.037 0.652 0.515 -0.048 0.096\n", + "cond[T.pure]:novelty[T.target] 0.0238 0.052 0.460 0.646 -0.078 0.126\n", + "cond[T.pure]:word_size[T.medium] -0.0032 0.052 -0.061 0.951 -0.105 0.099\n", + "cond[T.pure]:word_size[T.small] -0.0114 0.052 -0.221 0.825 -0.113 0.091\n", + "novelty[T.target]:word_size[T.medium] -0.0095 0.052 -0.184 0.854 -0.112 0.092\n", + "novelty[T.target]:word_size[T.small] -0.0363 0.052 -0.701 0.484 -0.138 0.066\n", + "cond[T.pure]:novelty[T.target]:word_size[T.medium] 0.0305 0.073 0.416 0.678 -0.114 0.175\n", + "cond[T.pure]:novelty[T.target]:word_size[T.small] 0.0185 0.073 0.252 0.801 -0.126 0.163\n", + "==============================================================================\n", + "Omnibus: 28.978 Durbin-Watson: 1.601\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 34.888\n", + "Skew: -0.815 Prob(JB): 2.66e-08\n", + "Kurtosis: 3.614 Cond. No. 25.6\n", + "==============================================================================\n", + "\n", + "Warnings:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m0 = smf.ols(\"correct ~ cond * novelty * word_size\", wperf).fit()\n", + "m0.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sum_sqdfFPR(>F)
cond0.0000751.00.0048469.445557e-01
novelty0.7419571.048.1097133.100126e-11
word_size0.0086652.00.2809427.552975e-01
cond:novelty0.0278331.01.8047551.802923e-01
cond:word_size0.0027112.00.0878989.158810e-01
novelty:word_size0.0140962.00.4569916.336857e-01
cond:novelty:word_size0.0027112.00.0879019.158786e-01
Residual4.071460264.0NaNNaN
\n", + "
" + ], + "text/plain": [ + " sum_sq df F PR(>F)\n", + "cond 0.000075 1.0 0.004846 9.445557e-01\n", + "novelty 0.741957 1.0 48.109713 3.100126e-11\n", + "word_size 0.008665 2.0 0.280942 7.552975e-01\n", + "cond:novelty 0.027833 1.0 1.804755 1.802923e-01\n", + "cond:word_size 0.002711 2.0 0.087898 9.158810e-01\n", + "novelty:word_size 0.014096 2.0 0.456991 6.336857e-01\n", + "cond:novelty:word_size 0.002711 2.0 0.087901 9.158786e-01\n", + "Residual 4.071460 264.0 NaN NaN" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sm.stats.anova_lm(m0, typ=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\plotnine\\stats\\stat_bin.py:93: PlotnineWarning: 'stat_bin()' using 'bins = 45'. Pick better value with 'binwidth'.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Show the distributions of valence values treating word length as a continous variable\n", + "p = (pn.ggplot(df_w, pn.aes('num_letters', fill='word_size'))\n", + " + pn.geom_histogram(alpha=.8)\n", + " )\n", + "p" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = (pn.ggplot(df_w.loc[df_w['novelty']=='target'], pn.aes('num_letters', 'correct'))\n", + " + pn.geom_smooth(method='loess')\n", + " )\n", + "p" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:2963: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\bambi\\models.py:267: UserWarning: Modeling the probability that correct=='1'\n", + "Auto-assigning NUTS sampler...\n", + "INFO:pymc3:Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "INFO:pymc3:Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "INFO:pymc3:Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [1|description_offset, 1|description_sd, 1|subj_offset, 1|subj_sd, cond:num_letters, num_letters, cond, Intercept]\n", + "INFO:pymc3:NUTS: [1|description_offset, 1|description_sd, 1|subj_offset, 1|subj_sd, cond:num_letters, num_letters, cond, Intercept]\n", + "Sampling 4 chains, 3 divergences: 100%|█████████████████████████| 6000/6000 [02:31<00:00, 39.71draws/s]\n", + "There were 3 divergences after tuning. Increase `target_accept` or reparameterize.\n", + "ERROR:pymc3:There were 3 divergences after tuning. Increase `target_accept` or reparameterize.\n", + "The number of effective samples is smaller than 10% for some parameters.\n", + "WARNING:pymc3:The number of effective samples is smaller than 10% for some parameters.\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:87: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n" + ] + } + ], + "source": [ + "# First initialize the model with the data frame we're using\n", + "model = bmb.Model(df_w.loc[df_w['novelty']=='target'])\n", + "\n", + "# Next build the regression with both fixed and random effects\n", + "results = model.fit('correct ~ cond * num_letters', \n", + " random=['1|subj', '1|description'], \n", + " family='bernoulli')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the inference traces for the fit\n", + "az.plot_trace(results, compact=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the fixed effect posteriors to examine significance\n", + "az.plot_posterior(results, ref_val=0.0, \n", + " var_names=['cond', 'num_letters', 'cond:num_letters']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Word lengths were treated as continous variables ranging from 3 to 11 letters. The first plot displays the fixed effect posterior in regard to the condition of each stimuli to determine if observations fall outside of expected significance ranges for the test. Despite a slight trend in favor of pure conditions, the 94% highest density interval overlaps with zero, indicating a lack of statistical significance between condition and overall performance. The second plot shows the same holds true for the relation between word length and performance. The final plot shows there was no significant interaction between word condition and word length, which conclusively refutes the proposed hypothesis of their combinatorial effect on performance." + ] + }, + { + "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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/assignments/Experiments_Ran.ipynb b/CS4500_CompMethods/assignments/Experiments_Ran.ipynb new file mode 100644 index 0000000..c34f995 --- /dev/null +++ b/CS4500_CompMethods/assignments/Experiments_Ran.ipynb @@ -0,0 +1,195 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from smile.common import *\n", + "from smile.scale import scale as s\n", + "from smile.startup import InputSubject\n", + "from smile.math_distract import MathDistract\n", + "​\n", + "from util import proc_subjid\n", + "​\n", + "# config section\n", + "font_size = 75\n", + "resp_keys = ['F', 'J']\n", + "​\n", + "resp_map_p = {'target': 'F', 'lure': 'J'}\n", + "resp_map_c = {'lure': 'F', 'target': 'J'}\n", + "​\n", + "ISI_dur = 0.5\n", + "ISI_jitter = 0.5\n", + "STUDY_TIME = 1.5\n", + "MATHDURATION = 20\n", + "MATHVARIABLES = 3\n", + "​\n", + "inst_title_font_size = 40\n", + "inst_font_size = 25\n", + "​\n", + "inst_text = \"\"\"[u][size={}]WORD RECOGNITION INSTRUCTIONS[/size][/u]\n", + "​\n", + "In this task, you will be studying a list of items and then be asked to discriminate between items that you studied (targets) and items you did not study (lures).\n", + "​\n", + "In between each study and test phase, you will be asked to do 30 seconds of math problems.\n", + "​\n", + "During the study phase, pay careful attention to each item as it is on the screen.\n", + "​\n", + "During the test phase, you will press {} if the item you are viewing is one that was on the study list, and press {} if the item is new and hadn't been studied previously. You will be reminded of these keys before each test phase.\n", + "​\n", + "Please answer as quickly and as accurately as possible during the experiment.\n", + " \n", + "Press the ENTER key to continue.\"\"\"\n", + "​\n", + "recall_instructions = \"\"\"REMEMBER: \n", + "For a old word you studied press: {}\n", + "​\n", + "For a new word you did not study press: {}\n", + "​\n", + "Press the ENTER key to start the TEST.\n", + "\"\"\"\n", + "​\n", + "math_instructions = \"\"\"You are about to see a series of math problems.\n", + "​\n", + "If the problem is correct (e.g., 1+4=5) press: {}\n", + "​\n", + "If the problem is incorrect (e.g., 1-9=-7) press: {}\n", + "​\n", + "Press the ENTER key to start the MATH.\n", + "\"\"\"\n", + "​\n", + "@Subroutine\n", + "def Instruct(self, resp_map):\n", + " # show the instructions\n", + " Label(text=Ref(inst_text.format, Ref(int, s(inst_title_font_size)),\n", + " resp_map['target'], resp_map['lure']), \n", + " font_size=s(inst_font_size),\n", + " text_size=(self.exp.screen.width*0.75, None),\n", + " markup=True)\n", + " with UntilDone():\n", + " KeyPress(keys=['ENTER'])\n", + "​\n", + "@Subroutine\n", + "def StudyTrial(self, block_num, trial_num, cur_trial):\n", + " # present the stimulus\n", + " stim = Label(text=cur_trial['description'],\n", + " font_size=s(font_size),\n", + " duration=STUDY_TIME)\n", + " # wait the ISI with jitter\n", + " Wait(ISI_dur, jitter=ISI_jitter)\n", + "​\n", + " # log the result of the trial\n", + " Log(name='word_study', \n", + " log_dict=cur_trial,\n", + " block_num=block_num,\n", + " trial_num=trial_num)\n", + "​\n", + "@Subroutine\n", + "def TestTrial(self, block_num, trial_num, cur_trial, resp_map):\n", + " # present the stimulus\n", + " stim = Label(text=cur_trial['description'],\n", + " font_size=s(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=resp_map[cur_trial['novelty']])\n", + " # wait the ISI with jitter\n", + " Wait(ISI_dur, jitter=ISI_jitter)\n", + "​\n", + " # log the result of the trial\n", + " Log(name='word_test', \n", + " resp_map=resp_map,\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", + "​\n", + "@Subroutine\n", + "def WordExp(self, blocks):\n", + " count_balance = Func(proc_subjid, Ref.getattr(self.exp, \"subject\")).result\n", + " with If(count_balance):\n", + " self.resp_map_word = resp_map_c\n", + " with Else():\n", + " self.resp_map_word = resp_map_p\n", + " \n", + " # show the instructions\n", + " Instruct(self.resp_map_word)\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 STUDY block.', \n", + " font_size=s(inst_title_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*2, jitter=ISI_jitter)\n", + " \n", + " # loop over the trials\n", + " with Loop(block.current['study']) as trial:\n", + " StudyTrial(block.i, trial.i, trial.current)\n", + " \n", + " # MATH INSTRUCTIONS\n", + " Label(text=Ref(math_instructions.format,\n", + " self.resp_map_word['target'], \n", + " self.resp_map_word['lure']), \n", + " font_size=s(inst_font_size), halign='center')\n", + " with UntilDone():\n", + " KeyPress(keys=['ENTER'])\n", + "​\n", + " # MATH Time\n", + " MathDistract(duration=MATHDURATION,\n", + " keys={'True':self.resp_map_word['target'], \n", + " 'False':self.resp_map_word['lure']},\n", + " num_vars=MATHVARIABLES)\n", + " \n", + " # make sure they are ready to continue\n", + " Label(text=Ref(recall_instructions.format,\n", + " self.resp_map_word['target'], \n", + " self.resp_map_word['lure']), \n", + " font_size=s(inst_font_size), halign='center')\n", + " with UntilDone():\n", + " KeyPress(keys=['ENTER'])\n", + " # loop over the trials\n", + " with Loop(block.current['test']) as trial:\n", + " TestTrial(block.i, trial.i, trial.current,\n", + " self.resp_map_word)\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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CS4500_CompMethods/assignments/ManList_Test.ipynb b/CS4500_CompMethods/assignments/ManList_Test.ipynb new file mode 100644 index 0000000..94f125e --- /dev/null +++ b/CS4500_CompMethods/assignments/ManList_Test.ipynb @@ -0,0 +1,116 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def my_sort(start_list):\n", + " '''\n", + " Receives a list of numbers and sorts in ascending order using mergesort method\n", + " '''\n", + " # Create a copy of list to preserve original. Also create template for final sorted list\n", + " temp = start_list[:]\n", + " final = []\n", + " track1 = 1\n", + " track2 = 1\n", + " # Use trackers to continue dividing halves until only single numbers remain\n", + " # Runs through my_sort function for each split chunk\n", + " left = temp[:((len(start_list))//2)]\n", + " if len(left) > 1 and track1 == 1:\n", + " left = my_sort(left)\n", + " elif len(left) == 1 and track1 == 1:\n", + " track1 = 0\n", + " right = temp[((len(start_list))//2):]\n", + " if len(right) > 1 and track2 == 1:\n", + " right = my_sort(right)\n", + " elif len(right) == 1 and track2 == 1:\n", + " track2 = 0\n", + " # Compares first of left and right half; appends smaller value to final list first\n", + " # Pops appended value off to continue comparing remaining values\n", + " # Identical values both popped from start of respective half and appended\n", + " while len(left) > 0 and len(right) > 0:\n", + " if left[0] < right[0]:\n", + " final.append(left[0])\n", + " left.pop(0)\n", + " elif left[0] > right[0]:\n", + " final.append(right[0])\n", + " right.pop(0)\n", + " elif left[0] == right[0]:\n", + " final.append(left[0])\n", + " final.append(right[0])\n", + " left.pop(0)\n", + " right.pop(0)\n", + " # When one half is empty, assumes the other half is already sorted and appends it as is\n", + " if len(left) == 0:\n", + " for each in right:\n", + " final.append(each)\n", + " if len(right) == 0:\n", + " for each in left:\n", + " final.append(each)\n", + " # Returns the final list sorted in ascending order\n", + " return final" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Please enter the comma seperated list to be sorted: 23,3243,453,4,64,32\n", + "[4.0, 23.0, 32.0, 64.0, 453.0, 3243.0]\n" + ] + } + ], + "source": [ + "# Collect numbers with an input method and turn into list\n", + "start_vals = input(\"Please enter the comma seperated list to be sorted: \")\n", + "start_list = start_vals.split(',')\n", + "\n", + "\n", + "# Convert the string input into a list of floats\n", + "for each in start_list:\n", + " place = start_list.index(each)\n", + " flt_val = float(each)\n", + " start_list[place] = flt_val\n", + "\n", + " \n", + "# Calls the sorting algorithm on the list and prints the result\n", + "print(my_sort(start_list))" + ] + }, + { + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CS4500_CompMethods/assignments/Testing.ipynb b/CS4500_CompMethods/assignments/Testing.ipynb new file mode 100644 index 0000000..83e4aee --- /dev/null +++ b/CS4500_CompMethods/assignments/Testing.ipynb @@ -0,0 +1,305 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "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", + " study_list = [] # fill the study list\n", + " for pool in pools: # loop over 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": "code", + "execution_count": 2, + "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\n", + "\n", + "\n", + "# 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", + "\n", + "\n", + "# shuffle the pools\n", + "random.shuffle(indoor_pool)\n", + "random.shuffle(outdoor_pool)\n", + "\n", + "\n", + "# 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)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO ] [Logger ] Record log in C:\\Users\\Carlos Rodriguez\\.kivy\\logs\\kivy_20-10-22_1.txt\n", + "[INFO ] [Kivy ] v1.11.1\n", + "[INFO ] [Kivy ] Installed at \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\__init__.py\"\n", + "[INFO ] [Python ] v3.8.3 (default, Jul 2 2020, 17:30:36) [MSC v.1916 64 bit (AMD64)]\n", + "[INFO ] [Python ] Interpreter at \"C:\\ProgramData\\Anaconda3\\python.exe\"\n", + "[INFO ] [Factory ] 184 symbols loaded\n", + "[INFO ] [Image ] Providers: img_tex, img_dds, img_sdl2, img_pil, img_gif (img_ffpyplayer ignored)\n", + "[INFO ] [Text ] Provider: sdl2\n", + "[CRITICAL] [Camera ] Unable to find any valuable Camera provider. Please enable debug logging (e.g. add -d if running from the command line, or change the log level in the config) and re-run your app to identify potential causes\n", + "picamera - ModuleNotFoundError: No module named 'picamera'\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\__init__.py\", line 59, in core_select_lib\n", + " mod = __import__(name='{2}.{0}.{1}'.format(\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\camera\\camera_picamera.py\", line 18, in \n", + " from picamera import PiCamera\n", + "\n", + "gi - ModuleNotFoundError: No module named 'gi'\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\__init__.py\", line 59, in core_select_lib\n", + " mod = __import__(name='{2}.{0}.{1}'.format(\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\camera\\camera_gi.py\", line 10, in \n", + " from gi.repository import Gst\n", + "\n", + "opencv - ModuleNotFoundError: No module named 'cv2'\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\__init__.py\", line 59, in core_select_lib\n", + " mod = __import__(name='{2}.{0}.{1}'.format(\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\camera\\camera_opencv.py\", line 48, in \n", + " import cv2\n", + "\n", + "[INFO ] [Video ] Provider: null(['video_ffmpeg', 'video_ffpyplayer'] ignored)\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\" graphics system\n", + "[INFO ] [GL ] GLEW initialization succeeded\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: 4, 6\n", + "[INFO ] [GL ] Shading version \n", + "[INFO ] [GL ] Texture max size <16384>\n", + "[INFO ] [GL ] Texture max units <32>\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": [ + "# **The issue of improper image loading does not seem to appear\n", + "# if the cells are all restarted and then run**\n", + "# 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", + "\n", + "# Configuration for font sizes, key inputs, durations,\n", + "# and list of instructions for Scene Study Task\n", + "font_size = 50\n", + "resp_keys = ['T', 'L']\n", + "resp_map = {'target': 'T', 'lure': 'L'}\n", + "ISI_dur = 0.5\n", + "ISI_jitter = 0.25\n", + "inst_font_size = 38\n", + "inst_text = \"\"\"[b][u][size=45]SCENE STUDY INSTRUCTIONS[/size][/b][/u]\n", + "In this task, you will study a series of images\n", + "Afterwards, you must identify whether \n", + "an image shown in a test was studied or not.\n", + "Press T if the image was studied\n", + "Press L if the image is new\n", + " \n", + "Press SPACEBAR to continue.\"\"\"\n", + "\n", + "\n", + "# Create the experiment\n", + "exp = Experiment(name='SceneStudy', show_splash=False, \n", + " fullscreen=True)\n", + "\n", + "\n", + "# Create subroutine to display instructions\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, halign='center')\n", + " with UntilDone():\n", + " KeyPress(keys=['SPACEBAR'])\n", + "\n", + "\n", + "# Create subroutine for study sets to display images for set time,\n", + "# wait an interstimulus interval, and log the stimulus information\n", + "@Subroutine\n", + "def Study(self, block_num, trial_num, cur_trial):\n", + "# Debug(trial_type=cur_trial['in_out'],\n", + "# image_path=cur_trial['in_out'] + '/' + cur_trial['filename'])\n", + " stim = Image(source=(cur_trial['in_out'] + \"/\" + cur_trial['filename']), \n", + " width=400, height=400, allow_stretch=True)\n", + " with UntilDone():\n", + " Wait(ISI_dur*3)\n", + " Wait(ISI_dur, jitter=ISI_jitter)\n", + " Log(name='scene_study', # log the result of the trial\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", + "# Create subroutine for test sets to display either old target images\n", + "# or new lures and wait for one of the configured keypresses to be entered.\n", + "# Logs the information included response and response time after. \n", + "@Subroutine\n", + "def Test(self, block_num, trial_num, cur_trial):\n", + "# Debug(trial_type=cur_trial['in_out'],\n", + "# image_path=cur_trial['in_out'] + '/' + cur_trial['filename'])\n", + " stim = Image(source=(cur_trial['in_out'] + \"/\" + cur_trial['filename']), \n", + " width=400, height=400, allow_stretch=True)\n", + " with UntilDone():\n", + " Wait(until=stim.appear_time) # make sure the stimulus has appeared on the screen\n", + " kp = KeyPress(keys=resp_keys, # collect a response (with no timeout)\n", + " base_time=stim.appear_time['time'],\n", + " correct_resp=Ref.object(resp_map)[cur_trial['novelty']])\n", + " Wait(ISI_dur, jitter=ISI_jitter) # wait the ISI \n", + " Log(name='scene_test', # log the result of the trial\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", + "\n", + "# Have user enter ID info and then display instructions\n", + "InputSubject('Scene Study')\n", + "Instruct() \n", + "Wait(0.5)\n", + "\n", + "\n", + "# Establishes loop to go through Study/Test pairs for each condition\n", + "# using the set number of reps. Introduces the start of each block\n", + "with Loop(blocks) as block: \n", + " Label(text='Press the SPACEBAR to\\nstart the next block', \n", + " font_size=font_size, halign='center')\n", + " with UntilDone():\n", + " KeyPress(keys=['SPACEBAR'])\n", + " Wait(ISI_dur, jitter=ISI_jitter) # add in some delay before the start of the block\n", + " with Loop(block.current['study']) as study_trial:\n", + " Study(block.i, study_trial.i, study_trial.current)\n", + " Label(text='The test is about to start\\n\\nRemember\\nPress T if the image was studied\\nPress L if the image is new\\n',\n", + " font_size=font_size, halign='center')\n", + " with UntilDone():\n", + " Wait(ISI_dur*4) # wait the ISI \n", + " with Loop(block.current['test']) as test_trial:\n", + " Test(block.i, test_trial.i, test_trial.current)\n", + "\n", + "\n", + "# Display termination message at the end of the experiment\n", + "Label(text='Congratulations!\\nYou have finished the experiment\\n\\nPress SPACEBAR to exit', \n", + " font_size=font_size, halign='center')\n", + "with UntilDone():\n", + " KeyPress(keys=['SPACEBAR'])\n", + "\n", + " \n", + "# Run the experiment\n", + "exp.run()" + ] + } + ], + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CS4500_CompMethods/assignments/ci_within.py b/CS4500_CompMethods/assignments/ci_within.py new file mode 100644 index 0000000..0f9bef3 --- /dev/null +++ b/CS4500_CompMethods/assignments/ci_within.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/CS4500_CompMethods/assignments/data/SceneStudy/Testing/20201020_004512/log_scenestudy_0.slog 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num_items): + # fill the study list + study_list = [] + + # loop over pools + for pool in pools: + # loop over items to add from that pool + # this will be num_items/num_types for mixed lists + for i in range(num_items): + study_item = pool.pop() + study_item.update({'novelty': 'target', + 'cond': cond}) + study_list.append(study_item) + + # shuffle the study_list + random.shuffle(study_list) + + # copy the study list to be the start of the test list + test_list = copy.deepcopy(study_list) + + # loop over pools + for pool in pools: + # loop over items to add from that pool + # this will be num_items/num_types for mixed lists + for i in range(num_items): + test_item = pool.pop() + test_item.update({'novelty': 'lure', + 'cond': cond}) + test_list.append(test_item) + + # shuffle the test list + random.shuffle(test_list) + + return {'study': study_list, 'test': test_list} + +# config variables +indoor_file = 'indoor.csv' +outdoor_file = 'outdoor.csv' + +# number of pools +num_pools = 2 + +# number of items in pure lists (must be evenly divisible by num_pools) +num_items_pure = 10 + +# number of repetitions of each block type +num_reps = 3 + +# verify these numbers make sense +num_items_mixed = int(num_items_pure / num_pools) +assert num_items_mixed * num_pools == num_items_pure + + +# load in the pools +indoor_pool = [i for i in DictReader(open(indoor_file, 'r'))] +outdoor_pool = [i for i in DictReader(open(outdoor_file, 'r'))] + + +# shuffle the pools +random.shuffle(indoor_pool) +random.shuffle(outdoor_pool) + + +# generate the blocks +blocks = [] +for r in range(num_reps): + # generate a pure indoor block + blocks.append(gen_block([indoor_pool], 'indoor', + num_items_pure)) + + # generate a pure outdoor block + blocks.append(gen_block([outdoor_pool], 'outdoor', + num_items_pure)) + + # generate a mixed indoor/outdoor block + blocks.append(gen_block([indoor_pool, outdoor_pool], 'mixed', + num_items_mixed)) + +# shuffle the blocks +random.shuffle(blocks) + +# Load in the most common SMILE states +from smile.common import * +from smile.scale import scale as s +from smile.startup import InputSubject +# import os + + +# enter configuration variables here (including the listgen variables) +font_size = 50 +resp_keys = ['T', 'L'] +resp_map = {'target': 'T', 'lure': 'L'} +ISI_dur = 0.5 +ISI_jitter = 0.25 +inst_font_size = 38 +inst_text = """[b][u][size=45]SCENE STUDY INSTRUCTIONS[/size][/b][/u] +In this task, you will study a series of images +Afterwards, you must identify whether +an image shown in a test was studied or not. +Press T if the image was studied +Press L if the image is new + +Press SPACEBAR to continue.""" + + +# create the experiment +exp = Experiment(name='SceneStudy', show_splash=False, + fullscreen=True) +# resolution=(1024, 768)) + + +@Subroutine +def Instruct(self): + # show the instructions + Label(text=inst_text, font_size=inst_font_size, + text_size=(exp.screen.width*0.75, None), + markup=True, halign='center') + with UntilDone(): + KeyPress(keys=['SPACEBAR']) + + +@Subroutine +def Study(self, block_num, trial_num, cur_trial): + Debug(trial_type=cur_trial['in_out'], + image_path=cur_trial['in_out'] + '/' + cur_trial['filename']) + stim = Image(source=(cur_trial['in_out'] + "/" + cur_trial['filename']), + width=400, height=400, allow_stretch=True) +# stim = Image(source=os.path.join(cur_trial['in_out'], cur_trial['filename']), +# width=400, height=400, allow_stretch=True) + with UntilDone(): + Wait(ISI_dur*3) +# Set up a log file here too + +@Subroutine +def Test(self, block_num, trial_num, cur_trial): + Debug(trial_type=cur_trial['in_out'], + image_path=cur_trial['in_out'] + '/' + cur_trial['filename']) + stim = Image(source=(cur_trial['in_out'] + "/" + cur_trial['filename']), + width=400, height=400, allow_stretch=True) +# stim = Image(source=(os.path.join(cur_trial['in_out'], cur_trial['filename'])), +# width=400, height=400, allow_stretch=True) + with UntilDone(): + Wait(until=stim.appear_time) # make sure the stimulus has appeared on the screen + kp = KeyPress(keys=resp_keys, # collect a response (with no timeout) + base_time=stim.appear_time['time'], + correct_resp=Ref.object(resp_map)[cur_trial['novelty']]) + Wait(ISI_dur) # wait the ISI + Log(name='scenestudy', # log the result of the trial + log_dict=cur_trial, + block_num=block_num, + trial_num=trial_num, + stim_on=stim.appear_time, + resp=kp.pressed, + resp_time=kp.press_time, + rt=kp.rt, + correct=kp.correct, + ) + + +InputSubject('Scene Study') +Instruct() # show the instructions +Wait(0.5) + + +with Loop(blocks) as block: # loop over the blocks + Label(text='Press the SPACEBAR to\nstart the next block', + font_size=font_size, halign='center') + with UntilDone(): + KeyPress(keys=['SPACEBAR']) + Wait(ISI_dur, jitter=ISI_jitter) # add in some delay before the start of the block + with Loop(block.current['study']) as study_trial: + Study(block.i, study_trial.i, study_trial.current) + Label(text='Remember\nPress T if the image was studied\nPress L if the image is new\n', + font_size=font_size, halign='center') + with UntilDone(): + Wait(ISI_dur*4) # wait the ISI + with Loop(block.current['test']) as test_trial: + Test(block.i, test_trial.i, test_trial.current) + + +Label(text='Congratulations!\nYou have finished the experiment\n\nPress SPACEBAR to exit', + font_size=font_size, halign='center') +with UntilDone(): + KeyPress(keys=['SPACEBAR']) + + +# run the experiment +exp.run() + diff --git a/CS4500_CompMethods/environment.yml b/CS4500_CompMethods/environment.yml new file mode 100644 index 0000000..240c793 --- /dev/null +++ b/CS4500_CompMethods/environment.yml @@ -0,0 +1,9 @@ +name: compsy +channels: + - defaults +dependencies: + - numpy + - scipy + - matplotlib + - pandas + - seaborn \ No newline at end of file diff --git a/CS4500_CompMethods/lessons/01_Introduction.ipynb b/CS4500_CompMethods/lessons/01_Introduction.ipynb new file mode 100644 index 0000000..21c3e95 --- /dev/null +++ b/CS4500_CompMethods/lessons/01_Introduction.ipynb @@ -0,0 +1,914 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Computational Methods in Psychology (and Neuroscience)\n", + "## or: *How I learned to stop worrying and love to program*\n", + "### Psychology 4500 --- Fall 2020\n", + "By: Per B. Sederberg, PhD\n", + "\n", + "![](http://compmem.org/assets/img/cmlab_logo.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Quick Reference\n", + "\n", + "\n", + "- *Credit*: 3 units ; Class # 18865\n", + "\n", + "- *Time*: Thursday, 14:00 -- 16:30\n", + "\n", + "- *Place*: Online\n", + "\n", + "- *Text*: Assigned readings\n", + "\n", + "- *Course Web Page*: GitHub (https://github.com/compmem/compsy)\n", + "\n", + "- *Course assistants*: Ryan Kirkpatrick (and other CompMem lab members)\n", + "\n", + "- *Instructor*: Dr. Per Sederberg\n", + "\n", + "- *Office*: Online\n", + "\n", + "- *E-mail*: pbs5u@virginia.edu (but use Slack whenever possible)\n", + "\n", + "- *Lab Website*: Computational Memory Lab (https://compmem.org)\n", + "\n", + "- *Office hours*: TBA\n", + "\n", + "- *Final*: Project-based\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Acknowledgements\n", + "\n", + "Thank you to all the contributors to the various Python projects, including:\n", + "\n", + "- Python Software Foundation (http://www.python.org)\n", + "- Scientific Tools for Python (http://www.scipy.org)\n", + "\n", + "And especially to the following, from which much of the inspiration or content of the lectures \n", + "and tutorials was borrowed:\n", + "\n", + "* Think Python (https://greenteapress.com/wp/think-python-2e/)\n", + "\n", + "* Scientific Python Lectures (https://scipy-lectures.org/index.html)\n", + "\n", + "And to my former postdoc, [Troy A. Smith, PhD](https://ung.edu/psychology/faculty-staff-bio/troy-smith.php), who helped develop the first version of this course in 2012." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Online Course Expectations\n", + "\n", + "This course will be taking place entirely online with \"synchronous\" classes on Zoom. As much as possible, I hope we can make this feel like we are all in this together, meeting in the same room. As such, these are the primary guidelines and expectations for our Zoom meetings:\n", + "\n", + "- You should keep your video on unless a transient issue arises (e.g., there is something seriously distracting going on in the room.) \n", + "- You can, however, keep your microphone muted when not talking.\n", + "- Feel free to ask questions anytime! It's often hard for me to see everyone, so interjecting by voice is perfectly fine (i.e., you don't need to use the hand-raising feature in the Zoom chat.)\n", + "- We will be recording the lessons, which will be made available *only* to those in the class via UVACollab. \n", + "\n", + "*If you have any concerns about any of these policies, please set up a meeting with me and I will do my best to accommodate your needs.*\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Why are we here?\n", + "\n", + "![The memory drum.](http://learnmem.cshlp.org/content/6/2/77/F2.medium.gif)\n", + "\n", + "- Science is hard, and folks didn't always have it so easy...\n", + "- Today, we have computers, yet they are rarely employed to their full potential.\n", + " - This limits the productivity, reproducibility, and quality of our work. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## This course\n", + "\n", + "- Is designed to break the fetters of commercial, and often inflexible, applications with the power of computer programming. \n", + "- Makes no assumptions of prior programming experience.\n", + "- Focuses on the Python language and specifically on how it can help with *every* stage of our scientific workflow in Psychology and Neuroscience. \n", + "\n", + "***The goal is that you will:***\n", + "- gain a better understanding of how a computer works (and can work for you)\n", + "- improve how you solve problems\n", + "- optimize and speed up your workflow\n", + "- but, most importantly, that you will lessen the need to tailor your research questions based on the *status quo*." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Computing Requirements\n", + "\n", + "- This is a computational class and all work will be performed on a computer, and almost entirely with the Python programming language within Jupyter notebooks. \n", + " - You will need to bring a laptop running Windows, OSX, or Linux to every class. \n", + "\n", + "- You will run the [Jupyter](https://jupyter.org) notebooks directly on your computer. This will also allow you to incorporate these approaches into your own research more easily. \n", + " - Thus, my recommendation is that you install and use the [Anaconda Python](https://www.anaconda.com/) distribution for your OS. \n", + "\n", + "- We will spend time on the first day of class to ensure everyone has a functioning computer that will be able to run everything necessary for the course.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Schedule\n", + "\n", + "The following is the general order of the topics covered in the course. Please note that sometimes we may cover multiple topics in a single lecture, or spend more than one lecture on a single topic, and this list is subject to modification at any time. \n", + "\n", + "0. Intro and Ecosystem setup\n", + "1. Version control with git\n", + "2. Python programming\n", + "3. Experiment design and implementation\n", + "4. Data collection and processing\n", + "5. Data visualization\n", + "6. Data analysis and statistics\n", + "7. Presentations\n", + "8. Advanced topics\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Evaluation\n", + "\n", + "This is a upper-level course, which means that much of the burden of staying motivated to learn is transferred to the student. As such, there will not be any in-class exams. Students will be evaluated on the basis of:\n", + "\n", + "- Lesson exercises / class participation (30 pts)\n", + "- List generation project (20 pts)\n", + "- Experiment project (20 pts)\n", + "- Data Analysis project (30 pts)\n", + "\n", + "for a total of 100 points. \n", + "\n", + "The course will be graded using the standard grading scale.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## GitHub\n", + "\n", + "All course material will be available on a public [GitHub](https://github.com/compmem/compsy) repository. \n", + "\n", + "There you will find the current version of the syllabus, all Jupyter Notebooks for the lessons, and links to any associated readings.\n", + "\n", + "I encourage you all to sign up for a GitHub account and make use of Git in your research.\n", + "\n", + "In fact, we will be making use of [Git](https://git-scm.com/) to manage code and assignments in this course. \n", + "\n", + "***We will provide an introduction to Git and GitHub in the next class.***\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Communications\n", + "\n", + "We will use [Slack](https://slack.com/) for all class communication and discussions. \n", + "\n", + "Please do not email me unless there is an issue with Slack. If you'd prefer to have a one-on-one discussion it is possible to send direct messages in Slack.\n", + "\n", + "There will also be traditional office hours for in-person conversations.\n", + "\n", + "***Look for an email after class inviting you to the Slack Workspace for the course!***\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Let's get started!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What do we do?\n", + "\n", + "- Get data (simulation, experiment, survey, etc.)\n", + "\n", + "- Manipulate and process data.\n", + "\n", + "- Visualize results... to understand what we are doing!\n", + "\n", + "- Perform statistical analyses.\n", + "\n", + "- Communicate results: produce figures for reports or publications,\n", + " write presentations.\n", + "\n", + "![](http://phdcomics.com/comics/archive/phd053104s.gif)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What do we want?\n", + "\n", + "\n", + "- A rich collection of already existing modules and libraries with code for\n", + " performing routine tasks, including classical numerical methods: \n", + " \n", + " - We don't want to re-program the plotting of a curve, a Fourier transform,\n", + " or a fitting algorithm. \n", + "\n", + " - Don't reinvent the wheel!\n", + "\n", + "- Easy to learn:\n", + "\n", + " - Computer science neither is our job nor our education. \n", + "\n", + " - We want to be able to draw a curve, smooth a signal, do a Fourier transform, or run a statistical test with a few lines of code.\n", + " \n", + "- Easy communication with collaborators, students, customers, to make the code\n", + " live within a lab: \n", + "\n", + " - Code should be as readable as a book.\n", + "\n", + " - Thus, the language should contain as few syntax symbols or unneeded routines\n", + " that would divert the reader from the mathematical or scientific understanding\n", + " of the code.\n", + "\n", + "- Efficient code that executes quickly...\n", + "\n", + " - But needless to say that a very fast code becomes useless if we\n", + " spend too much time writing it. \n", + "\n", + " - So, we need both a quick development time and a quick execution\n", + " time.\n", + "\n", + "- A single environment/language for everything\n", + "\n", + " - Avoid learning a new software for each new problem.\n", + " \n", + "- Should be cross-platform so that you can use the same code and tools\n", + " on any operating system (Linux, Windows, OS X).\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## How can we get it?\n", + "\n", + "What choices do we have?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Compiled languages: C, C++, Fortran, etc...\n", + "\n", + "- Advantages:\n", + "\n", + " - Very fast. Very optimized compilers. For heavy computations, it's difficult\n", + " to outperform these languages.\n", + "\n", + " - Some very optimized scientific libraries have been written for these\n", + " languages. Ex: blas (vector/matrix operations)\n", + "\n", + "- Drawbacks:\n", + "\n", + " - Painful usage: \n", + " \n", + " - no interactivity during development,\n", + " - mandatory compilation steps, \n", + " - verbose syntax (&, ::, }}, ; etc.),\n", + " - manual memory management (tricky in C). \n", + " \n", + "These are **difficult languages** for non computer scientists.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Scripting languages: Matlab\n", + "\n", + "- Advantages: \n", + "\n", + " - Very rich collection of libraries with numerous algorithms, for many\n", + " different domains. \n", + " \n", + " - Fast execution because these libraries are often written\n", + " in a compiled language.\n", + "\n", + " - Pleasant development environment: comprehensive and well organized help,\n", + " integrated editor, etc.\n", + "\n", + " - Commercial support is available.\n", + "\n", + "- Drawbacks: \n", + "\n", + " - Base language is quite poor and can become restrictive for advanced users.\n", + "\n", + " - Not free." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Scripting languages: Julia\n", + "\n", + "- Advantages:\n", + " - Fast code, yet interactive and simple.\n", + " - Easily connects to Python or C.\n", + "\n", + "- Drawbacks:\n", + "\n", + " - Ecosystem limited to numerical computing.\n", + " - Still young.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Other scripting languages\n", + "\n", + "**--e.g., Scilab, Octave, R, IDL, etc.--**\n", + "\n", + "- Advantages:\n", + "\n", + " - Open-source, free, or at least cheaper than Matlab.\n", + "\n", + " - Some features can be very advanced (statistics in R, etc.)\n", + "\n", + "- Drawbacks:\n", + "\n", + " - Fewer available algorithms than in Matlab, and the language\n", + " is not more advanced.\n", + "\n", + " - Some software are dedicated to one domain. e.g., Gnuplot or xmgrace\n", + " to draw curves. These programs are very powerful, but they are\n", + " restricted to a single type of usage, such as plotting. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "What about Python?\n", + "------------------\n", + "\n", + "- Advantages:\n", + " \n", + " - Very rich scientific computing libraries (a bit less than Matlab,\n", + " though)\n", + " \n", + " - Well thought out language, allowing to write very readable and well structured\n", + " code: we \"code what we think\".\n", + "\n", + " - Many libraries for other tasks than scientific computing (web server\n", + " management, serial port access, etc.)\n", + " \n", + " - A variety of powerful environments to work in, such as IPython, Spyder, Jupyter notebooks, Pycharm, Visual Studio Code...\n", + "\n", + " - Free and open-source software, widely spread, with a vibrant community.\n", + " \n", + " - Works on all major operating systems: Linux, Windows, Mac OS X\n", + "\n", + "\n", + "- Drawbacks: \n", + "\n", + " - Not all the algorithms that can be found in more specialized\n", + " software or toolboxes.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "## What can you do with Python?\n", + "\n", + "- Write programs to run experiments\n", + "\n", + "- Computational modeling (cognitive models, neural networks, simulation studies)\n", + "\n", + "- Data processing from start to finish\n", + "\n", + " - Reading and parsing log files\n", + " \n", + " - Data preprocessing (esp, important for EEG and fMRI) and filtering\n", + " \n", + " - Statistical analyses\n", + "\n", + "- Produce papers, presentations, websites...\n", + "\n", + "- Just about anything else you can think of\n", + "\n", + " - Games, graphics, audio apps, even cell phone apps\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Jupyter Notebooks\n", + "\n", + "The core computational and interactive infrastructure for the entire class will be [Jupyter](https://jupyter.org) notebooks. They provide an interactive way of interspersing code, text, and graphics, akin to a dynamic electronic lab notebook. \n", + "\n", + "Jupyter notebooks are *VERY* powerful, and have many useful extensions (for example, this presentation is a live and editable rendering of a Jupyter notebook!)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A quick tour of Jupyter notebook features...\n", + "\n", + "- Client/Server architecture\n", + "- Cells of code/text\n", + "- HTML-based front-end\n", + "- Cell output can be tables and (interactive) graphics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Markdown cells can have fancy formatting:\n", + "\n", + "- Headings with #, ##, ###, etc...\n", + "- *italics*\n", + "- **bold**\n", + "- Inline equations like $t_i = \\rho t_{i-1} + (1 - \\rho)f_i$\n", + "- Or a full equation on its on line:\n", + "\n", + " $$\\frac{dx}{dt} = \\frac{(\\rho - \\kappa x)}{\\tau}$$\n", + "- Images:\n", + " ![](https://jupyter.org/assets/main-logo.svg)\n", + "- Tables:\n", + "\n", + "| This | is |\n", + "|------|------|\n", + "| a | table|" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "# Best practice is to put imports and settings at the top\n", + "\n", + "# load matplotlib in inline mode (you can use notebook if you are running local)\n", + "%matplotlib inline\n", + "\n", + "# 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 ipywidgets as widgets # interactive widgets\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Other useful libraries\n", + "\n", + "- [SciPy](https://www.scipy.org): Wide range of tools scientific computing\n", + "- [StatsModels](https://www.statsmodels.org/stable/index.html): Many statistics\n", + "- [NiLearn](https://nilearn.github.io): Machine learning of neuroimaging data\n", + "- [Numba](http://numba.pydata.org): Just-in-time compiler to speed up Python\n", + "- [PlotNine](https://plotnine.readthedocs.io/en/stable/): Port of ggplot2 to Python\n", + "- [Seaborn](https://seaborn.pydata.org): Statistical data visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Some libraries from my lab\n", + "\n", + "- [SMILE](https://github.com/compmem/SMILE): A library for writing Psychology/Neuroscience experiments in Python.\n", + "- [RunDEMC](https://github.com/compmem/RunDEMC): Library for running Bayesian inference on hierarchical models.\n", + "- [PTSA](https://github.com/compmem/ptsa): Short for Python Time Series Analysis, this library aids in analysis of EEG and other forms of neural data represented as time series." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "# get some random data\n", + "dat = np.random.randn(1000, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.14 ms ± 12.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "# calculate the mean of each row\n", + "mdat = []\n", + "for i in range(len(dat)):\n", + " mdat.append(sum(dat[i])/len(dat[i]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21.4 µs ± 226 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "# numpy can help us do things like this much faster\n", + "mdat = dat.mean(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# sample some normally-distributed random numbers\n", + "x = np.random.randn(10000)\n", + "\n", + "# plot a histogram of them\n", + "plt.hist(x, bins='auto'); # the ; suppresses the printout of the return values" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9c72d40e07f24d8ba82293c423778c4a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=10, description='x', max=30, min=-10), Output()), _dom_classes=('widget-…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# you can even have interactive widgets!\n", + "def f(x):\n", + " print(x)\n", + "widgets.interact(f, x=10);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Why do we use Anaconda?\n", + "\n", + "![](https://imgs.xkcd.com/comics/python_environment.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Why do we use Anaconda?\n", + "\n", + "1. Relatively easy to install and well-supported\n", + " - Can be installed with GUI or command line\n", + " - Easy to find solutions to installation or runtime problems online\n", + "2. Allows you to create virtual environments\n", + " - Essential when packages share dependencies but require different versions of the same package\n", + "3. Has a powerful dependency solver\n", + " - Will upgrade/downgrade your packages as needed when you install a new package\n", + "4. Development team maintains a core scientific stack (numpy, scipy, matplotlib, etc.)\n", + " - Takes pressure off the user to determine if their scientific packages are working as expected\n", + " - More resources for troubleshooting\n", + "5. Has a highly customizable and user-friendly interactive python shell\n", + " - Can be easier to test code quickly in an interactive shell than by running a script and waiting for output\n", + " - Crucial for visualization of data and results\n", + "6. Makes programming much easier on windows\n", + " - No need to worry about system variables" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Installing Anaconda\n", + "\n", + "Follow the instructions to download and install Anaconda on this website:\n", + "\n", + "https://docs.anaconda.com/anaconda/install/\n", + "\n", + "- Make sure to follow the instructions specific to your operating system!\n", + "- Each operating system works differently, so the Anaconda team has designed installation software specifically\n", + " tailored for each operating system" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Why use a virtual environment\n", + "\n", + "A *virtual environment* creates a folder that contains all of the libraries and code necessary to use the packages needed by a specific project. \n", + "- Virtual environments allow the user to have different versions of the same package on the same system. \n", + "- This is important because some packages are only compatible with early versions of other packages.\n", + "\n", + "- Example:\n", + " - Assume you have two packages you want to install on your machine, Package A and Package B\n", + " - Package A and Package B are both dependent on the same package, Package C\n", + " - Package A only works with version 1.1.19 or earlier of Package C\n", + " - Package B only works with version 1.2.1 or later of Package C\n", + "\n", + " - If a user were to attempt to install both versions of Package C in the same environment (so they could use Package A and Package B) and a script calls a function from Package C, then the package manager would have conflicting versions of Package C that could lead to unexpected behavior invalidating the results of your program.\n", + "\n", + " - With the virtual environment approach, the user can make a separate virtual environment to accommodate the two conflicting but necessary versions of Package C. \n", + " - In one environment, the user can install Package A and version 1.1.19 of Package C.\n", + " - In the other environment, the user can install Package B and version 1.2.1 of Package C.\n", + " - No conflicts!\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Creating a virtual environment\n", + "\n", + "Now we want to create a virtual environment that we will use for the rest of the semester.\n", + "\n", + "0. Download the experiment.yml file from the course [GitHub](https://github.com/compmem/compsy).\n", + "1. In your Anaconda prompt (or Terminal in OSX/linux), navigate to the directory with the `environment.yml` file that you downloaded.\n", + "2. Type `conda env create -f environment.yml` in your Anaconda Prompt.\n", + " - This command creates a new virtual environment separate from the base environment and installs all the packages specified in `environment.yml`\n", + "3. After some time (it can take 5 to 15 minutes depending on your computer), you will see the following lines (it may also show lots of packages getting downloaded): \n", + "***\n", + " Collecting package metadata : done \n", + " Solving environment: done \n", + " Preparing transaction: done \n", + " Verifying transaction: done \n", + " Executing transaction: done \n", + "***\n", + "4. You have now created a virtual environment\n", + "5. To activate your virtual environment, type `conda activate compsy`\n", + " - This command switches you from your `base` environment to `compsy`\n", + " - You will then see `(compsy)` to the left of your current directory in the command prompt.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment before next class\n", + "\n", + "- Find the assignment on UVACollab page for this course. Input your conda environment details.\n", + "- Look for an email about joining our Slack workspace and join!\n", + "\n", + "You will receive ***points*** for the participation/homework grade by finishing these two tasks!\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": "3.7.6" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/02a_Git_Version_Control.ipynb b/CS4500_CompMethods/lessons/02a_Git_Version_Control.ipynb new file mode 100644 index 0000000..9db8a89 --- /dev/null +++ b/CS4500_CompMethods/lessons/02a_Git_Version_Control.ipynb @@ -0,0 +1,388 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Intro to Git and GitHub\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. What version control is and why to use it\n", + "2. About the Git version control system and how to use it with GitHub\n", + "3. How to fork and clone Git repositories\n", + "4. How to commit and upload changes to a Git repository\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# The what and the why of version control\n", + "\n", + "Have you been here before?\n", + "\n", + "![](https://i0.wp.com/devs.wiresmithtech.com/wp-content/uploads/2015/01/geek-and-poke-version-control.jpg)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Why version control?\n", + "\n", + "- Save different versions of software or documents in a safe and organized way.\n", + "- Flexible ways to revert to previous versions to correct errors (i.e., fix bugs).\n", + "- Make it *much* easier to collaborate as a team on the same documents.\n", + "- Can act like a backup (but should not *replace* actual backups)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What is Git?\n", + "\n", + ">Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.\n", + "---Linus Trovalds (author of Git and the Linux Kernel)\n", + "\n", + "![](https://www.groovecommerce.com/hs-fs/hub/188845/file-4063238095-png/blog-files/distributed-version-control-system.png?width=499&height=262&name=distributed-version-control-system.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What is Git?\n", + "\n", + "It helps users:\n", + "- Update their code to the latest version\n", + "- Keep track of all changes made and who made them\n", + "- Submit modifications to the original codebase\n", + "- Revert their code to an earlier version" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Git is confusing at first, but extremely powerful\n", + "\n", + "![alt text](https://imgs.xkcd.com/comics/git.png \"Title\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Why do we use Git?\n", + "\n", + "- Git allows us to track ONLY the *changes* we make in our files (`git commit`)\n", + "- Allows us to greatly save space while also having a complete history of a document or entire project. (`git log`)\n", + "- Allows us to know exactly which collaborator (or which version of your former self) made the change to your code that completely broke everything (`git blame`)\n", + "- Make potential changes to our project without affecting a working master version (`git branch`, `git request-pull`)\n", + "- GitHub allows us to upload the history of our files to one place (`git push`)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## ***In-class Assignment***\n", + "\n", + "Everyone needs to sign up for a GitHub account so that they can follow along with the steps. \n", + "\n", + "Please go to https://github.com/ and use your UVA email to create an account.\n", + "\n", + "![alt text](https://octodex.github.com/images/original.png \"OctoCat\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Forking and Cloning\n", + "\n", + "- Forking takes the entire history of a project/repository (usually one that you're not allowed to modify) and copies it to a new location that you can edit.\n", + "- We will now:\n", + " 1. *Fork* the class project onto your own GitHub accounts\n", + " 2. *Clone* this version onto our computers (like downloading but better)\n", + " 3. Create a remote connection to the original compsy repository (so you can download the lectures onto your computer with one command every week)!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 1. Fork the project\n", + "\n", + "- Make sure you are signed into the GitHub web page.\n", + "- Go to the GitHub url for the class: https://github.com/compmem/compsy \n", + "- Click the Fork button (see image below) \n", + " - This creates a copy of the class repository into your account and takes you to that newly forked *compsy* repository!\n", + "![alt text](https://github-images.s3.amazonaws.com/help/bootcamp/Bootcamp-Fork.png \"Fork button\")\n", + "\n", + "***For more info, see https://guides.github.com/activities/forking/***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 2. Clone your fork to your computer\n", + "\n", + "Now, on your computer, we are going to create a place where you will *clone* this project. \n", + "\n", + "- Open your Anaconda Prompt or Terminal\n", + "- Change directory to a folder where you'd like to store your work:\n", + " e.g., `cd class`\n", + " - You must first create the folder if it doesn't already exist: `mkdir class`\n", + "- Now we can pull down *your* copy of *compsy* from github using `git clone`:\n", + " `git clone https://github.com//compsy`\n", + " - Make sure to replace `` with what it says (i.e., mine is `psederberg`)\n", + "\n", + "You should see something like:\n", + "```Cloning into 'compsy'...\n", + "remote: Enumerating objects: 37, done.\n", + "remote: Counting objects: 100% (37/37), done.\n", + "remote: Compressing objects: 100% (27/27), done.\n", + "remote: Total 37 (delta 11), reused 31 (delta 8), pack-reused 0\n", + "Unpacking objects: 100% (37/37), 291.10 KiB | 2.80 MiB/s, done.\n", + "```\n", + "- Ensure everything is there by changing to the compsy directory (`cd compsy`) and listing the contents with either `ls` or `dir`, depending on your OS.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 3. Create a remote connection to the main repo\n", + "\n", + "You now have a *local* copy/clone of your fork of the main class repository, but you need an easy way to pull in any new changes we make to this main repo. \n", + "\n", + "- Set up a remote connection with the `git remote` command:\n", + " `git remote add compmem https://github.com/compmem/compsy`\n", + "- Verify that the new remote is added by running `git remote` with no additional arguments.\n", + " - You should see that you have 2 entires: `origin`, which points to your own github fork, and `compmem`, which points to the original version of the class repo. \n", + "- Now we can pull the latest version of all the files: `git pull compmem`\n", + " - and you will be able to see that you now have access to the *branch* prof/master, which we will use to update you with class materials every week!\n", + " \n", + "-*NOTE*: You may see a message asking you to specify how you want to handle merges. If so, we suggest running the following: `git config pull.rebase false`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Branches\n", + "\n", + "Branching and Merging are what set Git apart from all other version control systems.\n", + "\n", + "![](https://git-scm.com/images/about/branches@2x.png)\n", + "\n", + "Think of git as a giant tree. If you want to test changes to your code without affecting the main trunk.\n", + "\n", + "- `git checkout -b new_name` creates a new branch branch for exploration and fun! " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Updating a branch\n", + "\n", + "### `git pull` before making changes whenever possible!\n", + "\n", + "- Once you have made edits to your files or created new files, the time has come to push your work to the branch. \n", + "- You update a branch by creating a **commit**. \n", + " -For example, if you have created a text file called _github-is-great.txt_ and made modifications to the script _super-cool-analysis-funcs.py_, you can add these files to the **commit** individually by first adding the files to a staging area via entering the following two commands in your command prompt:\n", + "\n", + "```\n", + "git add github-is-great.txt\n", + "git add super-cool-analysis-funcs.py\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Commit your changes\n", + "\n", + "- After adding all necessary files to the staging area, use `git commit -m \"Brief description of the commit\"` to make the commit. \n", + "- Be sure to include a brief description of the commit, as this is very useful if you ever need to go back and track down when a certain change was made. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Push your commit\n", + "\n", + "- Finally, it is time to update the branch on the GitHub repository by using the command `git push`\n", + "\n", + "- **NOTE:** Pulling the latest commits from your branch prior to making changes can save you a lot of time and headaches. Always try to `git pull` your branch prior to pushing your commits to help prevent any merge errors.\n", + "\n", + "![alt text](http://www.quickmeme.com/img/3b/3bff12c922b4b369b9cb2894f19ce360e9a26ee190602090e91fc7184f06f89a.jpg \"Title\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Exercise: Create a .txt file and push it to a branch\n", + "By default, GitHub has a main branch called \"master\". It is good practice to create new branches when working on different sub-projects, then merging your branch with the the master branch when you have completed your task. \n", + "\n", + "We will now create a branch. Use the following command:\n", + "\n", + "`git branch my-branch` to create a branch called \"my-branch\". \n", + "\n", + "Great! We have created a new branch. We now need to switch our new branch by using `git checkout my-branch`\n", + "If you are ever unsure which branch you are on, use `git status` to check.\n", + "\n", + "Now we will create a text file called \"quotes.txt\". To do so, enter the following:\n", + "\n", + "for Windows: `start noetpad quotes.txt` to open Notepad and create and edit the file\n", + "\n", + "for OSX and Linux: `nano quotes.txt` to create and edit the file in the command prompt.\n", + "\n", + "You can also create & open the file via your OS's file system (Finder for OSX, File Explorer for Windows). Type or copy & paste your favorite quotes of all time into the .txt file. Please include the sources of the quotes. Save the file, then close it. \n", + "\n", + "Next, we will add this file to the staging area, create a commit, and push it to the repository via these commands:\n", + "\n", + "\n", + "`git add quotes.txt`\n", + "\n", + "`git commit -m \"uploading my favorite quotes\"`\n", + "\n", + "`git push --set-upstream origin my-branch` The \"--set-upstream origin my-branch\" part of this pushes the newly created branch to the repository, along with our new commit. If we wanted to add or change files in the branch, we can simply use `git push` now that the branch exists on the repository. \n", + "\n", + "Check your repository for the new branch and file. To see your new branch, click the top left button that says \"master\"\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# GitHub Student Developer Pack\n", + "\n", + "GitHub provides a wide array of services to students for free! This includes unlimited private repositories on GitHub, student Amazon Web Services (AWS) account, and much more. Go to https://education.github.com/pack#offers to register your GitHub account and get your Student Developer Pack!" + ] + } + ], + "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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/02b_Jupyter_Python-chr4qt.ipynb b/CS4500_CompMethods/lessons/02b_Jupyter_Python-chr4qt.ipynb new file mode 100644 index 0000000..865eb33 --- /dev/null +++ b/CS4500_CompMethods/lessons/02b_Jupyter_Python-chr4qt.ipynb @@ -0,0 +1,1581 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Intro to Jupyter and Python\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 be able to:\n", + "\n", + "1. Start and use a Jupyter Notebook\n", + "2. Identify three kinds of errors that occur in programming and describe \n", + " how to correct them\n", + "3. Identify the data types and keywords in the Python language\n", + "4. Understand how variable assignment works in Python\n", + "5. Identify and use math, string, and comparison operators\n", + "6. Use conditional statements in Python\n", + "7. Use Boolean logic to create complex conditionals\n", + "8. Use the \"for\" statement to create loops\n", + "9. Write in-code comments\n", + "10. State the purpose of functions and modules in Python\n", + "11. Use built-in functions\n", + "12. Create custom void functions and functions that return a value\n", + "13. Use parameters to make functions generalizable\n", + "14. Use docstrings to document function interfaces\n", + "\n", + "Whew!!!\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# A short intro to...\n", + "\n", + "![Python](https://www.python.org/static/img/python-logo.png)\n", + "\n", + "The Python programming language was invented by Guido van Rossum, who just recently stepped down as benevelont dictator for life to be replaced by an elected steering committee. \n", + "\n", + "Python has a set of core principles provided by the Zen of Python, which are available within Python, itself (so Zen, right?!):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + "\n", + "Beautiful is better than ugly.\n", + "Explicit is better than implicit.\n", + "Simple is better than complex.\n", + "Complex is better than complicated.\n", + "Flat is better than nested.\n", + "Sparse is better than dense.\n", + "Readability counts.\n", + "Special cases aren't special enough to break the rules.\n", + "Although practicality beats purity.\n", + "Errors should never pass silently.\n", + "Unless explicitly silenced.\n", + "In the face of ambiguity, refuse the temptation to guess.\n", + "There should be one-- and preferably only one --obvious way to do it.\n", + "Although that way may not be obvious at first unless you're Dutch.\n", + "Now is better than never.\n", + "Although never is often better than *right* now.\n", + "If the implementation is hard to explain, it's a bad idea.\n", + "If the implementation is easy to explain, it may be a good idea.\n", + "Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], + "source": [ + "import this" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Objects everywhere!\n", + "\n", + "Python is an object-oriented language and supports (though does not require) an object-oriented programming model. This simply means it makes it easy to create new objects, inheriting features from other objects." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What is an object?\n", + "\n", + "Everything (yes, everything) in Python is an object (or an instance of an object), which is really useful!\n", + "\n", + "- Objects have *attributes* that tell us about that object instance\n", + "- Objects can have *methods* that a functions that object can perform\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What is an instance?\n", + "\n", + "When you initialize an object, you create an *instance* of it, taking up memory in the computer. \n", + "\n", + "Thus, any variable we define is simply pointing the variable's name (in the current namespace) to a chunk of memory containing the instance of that object.\n", + "\n", + "Python keeps track of all object instances and cleans them when they are no longer needed." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "int" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's make an int object instance\n", + "x = 42\n", + "type(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "42" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's explore x\n", + "# (press tab after x.)\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = 'The answer to the ultimate question.'\n", + "type(y)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Now let's jump in with both feet\n", + "\n", + "![](https://kids.nationalgeographic.com/content/dam/kids/photos/animals/Birds/A-G/adelie-penguin-jumping-ocean.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Commands (a.k.a, instruction)\n", + "\n", + "- A command or instruction tells the computer to take some action\n", + "- Commands have a *syntax*:\n", + "\n", + "```python\n", + " 3 + 7 # good\n", + " 3 7 + # bad\n", + "```\n", + "- In Python, commands are *interpreted*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Programs\n", + "\n", + "- A program is a sequence of commands\n", + "\n", + " - Input\n", + " - Output\n", + " - Math and data manipulation\n", + " - Conditional execution\n", + " - Repetition" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Scripts\n", + "\n", + "- A script is a file that contains a program in a high-level language for an interpreter\n", + "- Python scripts are text files ending in .py\n", + " - e.g., HelloWorld.py\n", + "\n", + "```python\n", + " print('Hello, World!')\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Scripts (example)\n", + "\n", + "- Open a text editor (e.g., Notepad, nano, etc...) and create a file called `hello.py` with the following contents (not indented):\n", + "\n", + "```python\n", + "name = input('What is your name? ')\n", + "print('Hello,', name)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Executing scripts\n", + "\n", + "- Python scripts are run in a \"shell\"\n", + "\n", + "- This can be a terminal shell (OS X, Linux, Unix) or command prompt window \n", + " (MS Windows), or an interactive interpreter (IPython/Jupyter Notebook)\n", + " \n", + "- We'll run that script right in our notebook (though you could also call `python hello.py` from your Anaconda Prompt or Terminal)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is your name? Per\n", + "Hello, Per\n" + ] + } + ], + "source": [ + "run hello.py" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Jupyter Notebooks\n", + "\n", + "From the [Jupyter](https://jupyter.org) website:\n", + "\n", + "\n", + "> The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.\n", + "\n", + "\n", + "Thus, it's an interactive way of interspersing code, text, and graphics, akin to a dynamic electronic lab notebook. \n", + "\n", + "***Let's spend some time now getting started using Jupyter Notebooks!***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### A quick tour of Jupyter notebook features...\n", + "\n", + "- Client/Server architecture\n", + "- Cells of code/text\n", + "- HTML-based front-end\n", + "- Cell output can be tables and (interactive) graphics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Markdown cells can have fancy formatting:\n", + "\n", + "- Headings with #, ##, ###, etc...\n", + "- *italics*\n", + "- **bold**\n", + "- Inline equations like $t_i = \\rho t_{i-1} + (1 - \\rho)f_i$\n", + "- Or a full equation on its on line:\n", + "\n", + " $$\\frac{dx}{dt} = \\frac{(\\rho - \\kappa x)}{\\tau}$$\n", + "- Images:\n", + " ![](https://jupyter.org/assets/main-logo.svg)\n", + "- Tables:\n", + "\n", + "| This | is |\n", + "|------|------|\n", + "| a | table|" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Notes on Notebooks\n", + "\n", + "***NOTE 1***: If you need help doing something in a Jupyter notebook, come back to this mini tutorial or Google it!\n", + "\n", + "***NOTE 2***: If you click the `Help` menu at the top, and then select `User Inteface Tour`, the notebook will walk you through all of its most useful features. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Starting a Jupyter Notebook \n", + "\n", + "Our general workflow will be as follows:\n", + "\n", + "0. Open the Anaconda Prompt/Terminal.\n", + "1. Navigate via `cd` to the directory where you have cloned the `compsy` repo.\n", + "2. Ensure your *virtual environment* is active: `conda activate compsy`\n", + "3. Start up the jupyter notebook server, which will open a web-based file browser: `jupyter notebook`\n", + "4. Identify the notebook you'd like to open and `Duplicate` it if it's one of the notebooks provided by the class repo:\n", + " - Check the box to the left of the filename.\n", + " - Click the button that appears at the top of the list called `Duplicate`.\n", + " - Agree to `Duplicate` on the pop-up window.\n", + " - Follow similar steps to `Rename` the notebook appending your computing id (e.g., `01_Introduction_mst3k`.\n", + "5. Click the name of the notebook to open a new tab with the notebook. This will also start a new Python kernel running in the background\n", + " - NOTE: It is important to select `File -> Close and Halt` once you've saved your work and are done working with a notebook, otherwise you might run through your computer's RAM." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The Two Modes\n", + "\n", + "- **Edit mode**: When you double-click or press `Enter` in a cell, you are able to modify its contents. \n", + " - It works just like any other place where you can type text (ctrl-c, ctrl-v, etc).\n", + "- **Command mode**: Pressing `Esc` or clicking outside the cell will put you into the mode that allows you to navigate, reorder, and modify the cell types. \n", + " - For example, you can combine, split, create and delete cells, move them around, convert them between code and markdown modes, etc... \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Back to Python (now all in Jupyter)\n", + "\n", + "![](https://jupyter.org/assets/nav_logo.svg)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Debugging\n", + "\n", + "\n", + "- You will make mistakes while programming!\n", + "- Mistakes are called *bugs*\n", + "- The process of finding and fixing bugs is *debugging*\n", + "- There are 3 main types of errors you will encounter\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Syntax Errors\n", + "\n", + "- All programming languages are picky about syntax" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m 1+2) + 3\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "1+2) + 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "- Syntax errors can be identified \n", + "\n", + " - automated code checking and highlighting (color coding)\n", + " - error messages when you try to run a command or script\n", + "\n", + "- Use documentation, help, and Google when in doubt!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Runtime errors\n", + "\n", + "- Errors that occur when program is running\n", + "- Also called \"exceptions\"\n", + "- Ex: runtime.py\n", + "- Identified by testing the program\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "ZeroDivisionError", + "evalue": "division by zero", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" + ] + } + ], + "source": [ + "1/(1-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Semantic or logical error\n", + "\n", + "- The worst kind!!!\n", + "- Program runs (and does exactly what you told it to do)\n", + "- But you made a mistake in the design or implementation\n", + "- Identified by testing program and checking results" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def sum(x,y):\n", + " return x*y\n", + "\n", + "sum(2,3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data types\n", + "\n", + "- Built-in\n", + "\n", + " - Numeric types:\n", + "\n", + " - int, float, long, complex \n", + "\n", + " - string\n", + "\n", + " - Boolean\n", + "\n", + " - True / False\n", + "\n", + "- User defined" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Data types (cont.)\n", + "\n", + "- Use the type() function to find the type for a value or variable\n", + "- Data can *often* be converted using cast commands:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "int" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = 17\n", + "type(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b = str(17)\n", + "type(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "invalid literal for int() with base 10: '3a'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'3a'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '3a'" + ] + } + ], + "source": [ + "int('3a')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Variables\n", + "\n", + "- As in mathematics, much of the power of computer programs is in the use of variables\n", + "- Python variables are identified by name\n", + "\n", + "### Variable naming conventions\n", + "\n", + "- Must start with a letter\n", + "- Can contain letters, numbers, and underscore character (_)\n", + "- Can be any (reasonable) length\n", + "- Case sensitive\n", + " - `big` is NOT the same as `BIG` or `Big`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Naming style guidelines\n", + "\n", + "The Python community provides clear style guidelines (PEP8): https://www.python.org/dev/peps/pep-0008/\n", + "\n", + "- Use descriptive names\n", + "\n", + "- Use lowercase with underscores for variable names\n", + "\n", + " - pres_rate\n", + " - word_stim\n", + "\n", + "- Don't use \"I\", \"l\", or \"O\" for single letter names\n", + "\n", + "- Be consistent" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Keywords\n", + "\n", + "The following words are not allowed as variable names:\n", + "\n", + "```\n", + "False class finally is return\n", + "None continue for lambda try\n", + "True def from nonlocal while\n", + "and del global not with\n", + "as elif if or yield\n", + "assert else import pass\n", + "break except in raise\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment and initialization\n", + "\n", + "- Variables can be created at any time (no need to declare them)\n", + "- The Python interpreter keeps track of the `namespace`\n", + "- Variable names are actually pointers to memory locations\n", + "- Two variables can refer to the same memory location" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = \"Howdy\"\n", + "b = a\n", + "a is b" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Howdy'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Math operators\n", + "\n", + "- Arithmetic\n", + "\n", + " - +, -, *, and /\n", + " - Exponentiation **\n", + " - Modulo %\n", + "\n", + "- Standard order of operations (PEMDAS)\n", + "\n", + "| | |\n", + "|--|--------|\n", + "|P|Parentheses first|\n", + "|E|Exponents (ie Powers and Square Roots, etc.)|\n", + "|MD|Multiplication and Division (left-to-right)|\n", + "|AS|Addition and Subtraction (left-to-right)|\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Math operators (examples)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "23" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "3 + 10 * 2" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "26" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(3 + 10) * 2" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "103" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "3 + 10**2" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6.5" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " (3 + 10) / 2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## String operators\n", + "\n", + "- String concatenation uses the `+` sign:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "this is a string\n" + ] + } + ], + "source": [ + "print('this' + ' is a string')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- String repetition uses the `*`" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spam! Spam! Spam! Spam! \n" + ] + } + ], + "source": [ + "lunch = 'Spam! ' * 4\n", + "print(lunch)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Comparison operators\n", + "\n", + "- Perform logical comparison and return Boolean value\n", + "\n", + " - Equality: `==`\n", + " - Inequality\t`!=`\n", + " - Relative value `<` `>` `<=` `>=`\n", + " - `is` (test whether two variables point to the *same* object)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = [1,2,3]\n", + "b = [1,2,3]\n", + "a is b" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Numerical types\n", + "\n", + "- Integer variables::\n", + "b\n", + " >>> 1 + 1\n", + " 2\n", + " >>> a = 4\n", + "\n", + "\n", + "floats ::\n", + "\n", + " >>> c = 2.1\n", + "\n", + "complex (a native type in Python!) ::\n", + "\n", + " >>> a = 1.5 + 0.5j\n", + " >>> a.real\n", + " 1.5\n", + " >>> a.imag\n", + " 0.5\n", + "\n", + "and booleans::\n", + "\n", + " >>> 3 > 4\n", + " False\n", + " >>> test = (3 > 4)\n", + " >>> test\n", + " False\n", + " >>> type(test)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Conditional execution (if...)\n", + "\n", + "- Syntax (note the indented section):\n", + "```python\n", + " if condition:\n", + " do_something\n", + "```\n", + "- Condition must be statement that evaluates to a boolean value (True or False)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x = 33\n", + "You entered a digit!\n" + ] + } + ], + "source": [ + "x = input('x = ')\n", + "if x.isdigit():\n", + " print('You entered a digit!')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Alternative execution\n", + "\n", + "- Syntax:\n", + "\n", + "```python\n", + " if condition:\n", + " do_something\n", + " else:\n", + " do_alternative\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x = 3a\n", + "You did not enter a digit!\n" + ] + } + ], + "source": [ + "x = input('x = ')\n", + "if x.isdigit():\n", + " print('You entered a digit!')\n", + "else:\n", + " print('You did not enter a digit!')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Chained conditionals\n", + "\n", + "Conditions are evaluated in order and once one it met, the associated code is run.\n", + "\n", + "- Syntax:\n", + "\n", + "```python\n", + " if condition:\n", + " do_something\n", + " elif condition:\n", + " do_alternative1\n", + " else:\n", + " do_alternative2\n", + "```\n", + "\n", + "- NOTE: The `else` at the end is *not* required, so it may be that nothing runs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Complex condition statements\n", + "\n", + "- Use Boolean logic operators `and`, `or`, and `not` to chain together simple conditions:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x = 1\n", + "y = 2\n", + "z = 2\n", + "x = y OR y = z\n", + "x = y = z is False\n" + ] + } + ], + "source": [ + "x = int(input(\"x = \"))\n", + "y = int(input(\"y = \"))\n", + "z = int(input(\"z = \"))\n", + "\n", + "if x == y and y == z:\n", + " print('x = y = z')\n", + "if x == y or y == z:\n", + " print('x = y OR y = z')\n", + "if not (x == y and x == z):\n", + " print('x = y = z is False')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Notes on conditionals\n", + "\n", + "- You have to have at least one statement inside each branch\n", + "- You can use the `pass` command while stubbing\n", + "\n", + "```python\n", + " if x < 0:\n", + " pass # Handle neg values \n", + "```\n", + "\n", + "- You can have as many `elif` branches as you need\n", + "- Conditionals can be nested\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The `for` loop\n", + "\n", + "- Used to repeat a section of code a set number of times\n", + "- Syntax:\n", + "\n", + "```python\n", + " for target in sequence:\n", + " do_statements\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n" + ] + } + ], + "source": [ + "for i in range(10):\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter a number: 22\n", + "Cumsum of 22 is 253\n" + ] + } + ], + "source": [ + "# ask for a number\n", + "x = int(input('Enter a number: '))\n", + "\n", + "# initialize our cumulative sum\n", + "csum = 0\n", + "\n", + "# loop and calculate the cumulative sum\n", + "for i in range(1, x+1):\n", + " csum += i\n", + "\n", + "print('Cumsum of %d is %d' % (x, csum))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Commenting your code\n", + "\n", + "- The `#` symbol indicates the rest of the line is a comment\n", + "- The Python interpreter ignores everything after the `#`\n", + "- ***Use comments liberally***!!!\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What is a function?\n", + "\n", + "- A named sequence of statements that performs a computation or action\n", + "- Functions are called by name\n", + "- Most functions accept inputs (arguments)\n", + "- Some functions return results (return value)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## We've already seen some functions\n", + "\n", + "- `type()`\n", + "\n", + "- Type casting functions\n", + "\n", + " - `int()`, `float()`, `str()`\n", + "\n", + "- `input()`\n", + "\n", + "- `range()`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Creating your own functions\n", + "\n", + "- You have to define the function" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I'm a lumberjack, and I'm okay.\n", + "I sleep all night and I work all day.\n" + ] + } + ], + "source": [ + "def print_lyrics():\n", + " print(\"I'm a lumberjack, and I'm okay.\")\n", + " print(\"I sleep all night and I work all day.\")\n", + "\n", + "# the parentheses are important\n", + "print_lyrics()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Parameters and arguments\n", + "\n", + "- A variable that is an input to a function is called a *parameter*\n", + "- The value of the parameter you pass in when you call the function is an *argument*\n", + "- Functions can accept multiple parameters\n", + "\n", + "```python\n", + "def perimeter(length, n_sides):\n", + "```\n", + "\n", + "- You can also use named parameters that specify a default value\n", + "\n", + "```python\n", + "def perimeter(length, n_sides=4):\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Parameters and arguments (cont.)\n", + "\n", + "- When you call a function, you pass in arguments for each parameter\n", + "\n", + "```python\n", + "perimeter(3)\n", + "perimeter(3, 6)\n", + "perimeter(3, n_sides=6)\n", + "perimeter(length=3, n_sides=6)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Functions can return output\n", + "\n", + "- Use the *return* statement\n", + "- Syntax:\n", + " `return [expression]`\n", + "- Exits the function and returns the value of expression as a result of the call to the function\n", + "- If expression is omitted, returns None\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Using docstrings\n", + "\n", + "\n", + "* A docstring is a string literal that occurs as the first statement\n", + " in a module, function, class, or method definition\n", + "* The built-in PyDoc module and Sphinx\n", + " can automatically generate documentation if the programmer uses\n", + " docstrings\n", + "* See PEP-257 for details:\n", + " http://www.python.org/dev/peps/pep-0257/\n", + "\n", + "```python\n", + "def perimeter(length):\n", + " \"\"\"Calculate the perimeter of a square.\"\"\"\n", + " pass\n", + "\n", + "def complex(real=0.0, imag=0.0):\n", + " \"\"\"\n", + " Form a complex number.\n", + "\n", + " Keyword arguments:\n", + " real -- the real part (default 0.0)\n", + " imag -- the imaginary part (default 0.0)\n", + " \"\"\"\n", + " return (real + imag*1j)\n", + "\n", + "\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment before next class\n", + "\n", + "- Look for the Exercise posted by tomorrow on UVACollab and the lab website.\n", + "- Follow the instructions and upload the final PDF to UVACollab.\n", + "\n", + "You will receive ***points*** for the participation/homework grade by finishing this exercise!\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/CS4500_CompMethods/lessons/02b_Jupyter_Python.ipynb b/CS4500_CompMethods/lessons/02b_Jupyter_Python.ipynb new file mode 100644 index 0000000..8081662 --- /dev/null +++ b/CS4500_CompMethods/lessons/02b_Jupyter_Python.ipynb @@ -0,0 +1,1581 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Intro to Jupyter and Python\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 be able to:\n", + "\n", + "1. Start and use a Jupyter Notebook\n", + "2. Identify three kinds of errors that occur in programming and describe \n", + " how to correct them\n", + "3. Identify the data types and keywords in the Python language\n", + "4. Understand how variable assignment works in Python\n", + "5. Identify and use math, string, and comparison operators\n", + "6. Use conditional statements in Python\n", + "7. Use Boolean logic to create complex conditionals\n", + "8. Use the \"for\" statement to create loops\n", + "9. Write in-code comments\n", + "10. State the purpose of functions and modules in Python\n", + "11. Use built-in functions\n", + "12. Create custom void functions and functions that return a value\n", + "13. Use parameters to make functions generalizable\n", + "14. Use docstrings to document function interfaces\n", + "\n", + "Whew!!!\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# A short intro to...\n", + "\n", + "![Python](https://www.python.org/static/img/python-logo.png)\n", + "\n", + "The Python programming language was invented by Guido van Rossum, who just recently stepped down as benevelont dictator for life to be replaced by an elected steering committee. \n", + "\n", + "Python has a set of core principles provided by the Zen of Python, which are available within Python, itself (so Zen, right?!):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + "\n", + "Beautiful is better than ugly.\n", + "Explicit is better than implicit.\n", + "Simple is better than complex.\n", + "Complex is better than complicated.\n", + "Flat is better than nested.\n", + "Sparse is better than dense.\n", + "Readability counts.\n", + "Special cases aren't special enough to break the rules.\n", + "Although practicality beats purity.\n", + "Errors should never pass silently.\n", + "Unless explicitly silenced.\n", + "In the face of ambiguity, refuse the temptation to guess.\n", + "There should be one-- and preferably only one --obvious way to do it.\n", + "Although that way may not be obvious at first unless you're Dutch.\n", + "Now is better than never.\n", + "Although never is often better than *right* now.\n", + "If the implementation is hard to explain, it's a bad idea.\n", + "If the implementation is easy to explain, it may be a good idea.\n", + "Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], + "source": [ + "import this" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Objects everywhere!\n", + "\n", + "Python is an object-oriented language and supports (though does not require) an object-oriented programming model. This simply means it makes it easy to create new objects, inheriting features from other objects." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What is an object?\n", + "\n", + "Everything (yes, everything) in Python is an object (or an instance of an object), which is really useful!\n", + "\n", + "- Objects have *attributes* that tell us about that object instance\n", + "- Objects can have *methods* that a functions that object can perform\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What is an instance?\n", + "\n", + "When you initialize an object, you create an *instance* of it, taking up memory in the computer. \n", + "\n", + "Thus, any variable we define is simply pointing the variable's name (in the current namespace) to a chunk of memory containing the instance of that object.\n", + "\n", + "Python keeps track of all object instances and cleans them when they are no longer needed." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "int" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's make an int object instance\n", + "x = 42\n", + "type(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "42" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's explore x\n", + "# (press tab after x.)\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = 'The answer to the ultimate question.'\n", + "type(y)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Now let's jump in with both feet\n", + "\n", + "![](https://kids.nationalgeographic.com/content/dam/kids/photos/animals/Birds/A-G/adelie-penguin-jumping-ocean.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Commands (a.k.a, instruction)\n", + "\n", + "- A command or instruction tells the computer to take some action\n", + "- Commands have a *syntax*:\n", + "\n", + "```python\n", + " 3 + 7 # good\n", + " 3 7 + # bad\n", + "```\n", + "- In Python, commands are *interpreted*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Programs\n", + "\n", + "- A program is a sequence of commands\n", + "\n", + " - Input\n", + " - Output\n", + " - Math and data manipulation\n", + " - Conditional execution\n", + " - Repetition" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Scripts\n", + "\n", + "- A script is a file that contains a program in a high-level language for an interpreter\n", + "- Python scripts are text files ending in .py\n", + " - e.g., HelloWorld.py\n", + "\n", + "```python\n", + " print('Hello, World!')\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Scripts (example)\n", + "\n", + "- Open a text editor (e.g., Notepad, nano, etc...) and create a file called `hello.py` with the following contents (not indented):\n", + "\n", + "```python\n", + "name = input('What is your name? ')\n", + "print('Hello,', name)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Executing scripts\n", + "\n", + "- Python scripts are run in a \"shell\"\n", + "\n", + "- This can be a terminal shell (OS X, Linux, Unix) or command prompt window \n", + " (MS Windows), or an interactive interpreter (IPython/Jupyter Notebook)\n", + " \n", + "- We'll run that script right in our notebook (though you could also call `python hello.py` from your Anaconda Prompt or Terminal)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is your name? Per\n", + "Hello, Per\n" + ] + } + ], + "source": [ + "run hello.py" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Jupyter Notebooks\n", + "\n", + "From the [Jupyter](https://jupyter.org) website:\n", + "\n", + "\n", + "> The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.\n", + "\n", + "\n", + "Thus, it's an interactive way of interspersing code, text, and graphics, akin to a dynamic electronic lab notebook. \n", + "\n", + "***Let's spend some time now getting started using Jupyter Notebooks!***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### A quick tour of Jupyter notebook features...\n", + "\n", + "- Client/Server architecture\n", + "- Cells of code/text\n", + "- HTML-based front-end\n", + "- Cell output can be tables and (interactive) graphics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Markdown cells can have fancy formatting:\n", + "\n", + "- Headings with #, ##, ###, etc...\n", + "- *italics*\n", + "- **bold**\n", + "- Inline equations like $t_i = \\rho t_{i-1} + (1 - \\rho)f_i$\n", + "- Or a full equation on its on line:\n", + "\n", + " $$\\frac{dx}{dt} = \\frac{(\\rho - \\kappa x)}{\\tau}$$\n", + "- Images:\n", + " ![](https://jupyter.org/assets/main-logo.svg)\n", + "- Tables:\n", + "\n", + "| This | is |\n", + "|------|------|\n", + "| a | table|" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Notes on Notebooks\n", + "\n", + "***NOTE 1***: If you need help doing something in a Jupyter notebook, come back to this mini tutorial or Google it!\n", + "\n", + "***NOTE 2***: If you click the `Help` menu at the top, and then select `User Inteface Tour`, the notebook will walk you through all of its most useful features. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Starting a Jupyter Notebook \n", + "\n", + "Our general workflow will be as follows:\n", + "\n", + "0. Open the Anaconda Prompt/Terminal.\n", + "1. Navigate via `cd` to the directory where you have cloned the `compsy` repo.\n", + "2. Ensure your *virtual environment* is active: `conda activate compsy`\n", + "3. Start up the jupyter notebook server, which will open a web-based file browser: `jupyter notebook`\n", + "4. Identify the notebook you'd like to open and `Duplicate` it if it's one of the notebooks provided by the class repo:\n", + " - Check the box to the left of the filename.\n", + " - Click the button that appears at the top of the list called `Duplicate`.\n", + " - Agree to `Duplicate` on the pop-up window.\n", + " - Follow similar steps to `Rename` the notebook appending your computing id (e.g., `01_Introduction_mst3k`.\n", + "5. Click the name of the notebook to open a new tab with the notebook. This will also start a new Python kernel running in the background\n", + " - NOTE: It is important to select `File -> Close and Halt` once you've saved your work and are done working with a notebook, otherwise you might run through your computer's RAM." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The Two Modes\n", + "\n", + "- **Edit mode**: When you double-click or press `Enter` in a cell, you are able to modify its contents. \n", + " - It works just like any other place where you can type text (ctrl-c, ctrl-v, etc).\n", + "- **Command mode**: Pressing `Esc` or clicking outside the cell will put you into the mode that allows you to navigate, reorder, and modify the cell types. \n", + " - For example, you can combine, split, create and delete cells, move them around, convert them between code and markdown modes, etc... \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Back to Python (now all in Jupyter)\n", + "\n", + "![](https://jupyter.org/assets/nav_logo.svg)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Debugging\n", + "\n", + "\n", + "- You will make mistakes while programming!\n", + "- Mistakes are called *bugs*\n", + "- The process of finding and fixing bugs is *debugging*\n", + "- There are 3 main types of errors you will encounter\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Syntax Errors\n", + "\n", + "- All programming languages are picky about syntax" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m 1+2) + 3\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "1+2) + 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "- Syntax errors can be identified \n", + "\n", + " - automated code checking and highlighting (color coding)\n", + " - error messages when you try to run a command or script\n", + "\n", + "- Use documentation, help, and Google when in doubt!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Runtime errors\n", + "\n", + "- Errors that occur when program is running\n", + "- Also called \"exceptions\"\n", + "- Ex: runtime.py\n", + "- Identified by testing the program\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "ZeroDivisionError", + "evalue": "division by zero", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" + ] + } + ], + "source": [ + "1/(1-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Semantic or logical error\n", + "\n", + "- The worst kind!!!\n", + "- Program runs (and does exactly what you told it to do)\n", + "- But you made a mistake in the design or implementation\n", + "- Identified by testing program and checking results" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def sum(x,y):\n", + " return x*y\n", + "\n", + "sum(2,3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data types\n", + "\n", + "- Built-in\n", + "\n", + " - Numeric types:\n", + "\n", + " - int, float, long, complex \n", + "\n", + " - string\n", + "\n", + " - Boolean\n", + "\n", + " - True / False\n", + "\n", + "- User defined" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Data types (cont.)\n", + "\n", + "- Use the type() function to find the type for a value or variable\n", + "- Data can *often* be converted using cast commands:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "int" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = 17\n", + "type(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b = str(17)\n", + "type(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "invalid literal for int() with base 10: '3a'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'3a'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '3a'" + ] + } + ], + "source": [ + "int('3a')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Variables\n", + "\n", + "- As in mathematics, much of the power of computer programs is in the use of variables\n", + "- Python variables are identified by name\n", + "\n", + "### Variable naming conventions\n", + "\n", + "- Must start with a letter\n", + "- Can contain letters, numbers, and underscore character (_)\n", + "- Can be any (reasonable) length\n", + "- Case sensitive\n", + " - `big` is NOT the same as `BIG` or `Big`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Naming style guidelines\n", + "\n", + "The Python community provides clear style guidelines (PEP8): https://www.python.org/dev/peps/pep-0008/\n", + "\n", + "- Use descriptive names\n", + "\n", + "- Use lowercase with underscores for variable names\n", + "\n", + " - pres_rate\n", + " - word_stim\n", + "\n", + "- Don't use \"I\", \"l\", or \"O\" for single letter names\n", + "\n", + "- Be consistent" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Keywords\n", + "\n", + "The following words are not allowed as variable names:\n", + "\n", + "```\n", + "False class finally is return\n", + "None continue for lambda try\n", + "True def from nonlocal while\n", + "and del global not with\n", + "as elif if or yield\n", + "assert else import pass\n", + "break except in raise\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment and initialization\n", + "\n", + "- Variables can be created at any time (no need to declare them)\n", + "- The Python interpreter keeps track of the `namespace`\n", + "- Variable names are actually pointers to memory locations\n", + "- Two variables can refer to the same memory location" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = \"Howdy\"\n", + "b = a\n", + "a is b" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Howdy'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Math operators\n", + "\n", + "- Arithmetic\n", + "\n", + " - +, -, *, and /\n", + " - Exponentiation **\n", + " - Modulo %\n", + "\n", + "- Standard order of operations (PEMDAS)\n", + "\n", + "| | |\n", + "|--|--------|\n", + "|P|Parentheses first|\n", + "|E|Exponents (ie Powers and Square Roots, etc.)|\n", + "|MD|Multiplication and Division (left-to-right)|\n", + "|AS|Addition and Subtraction (left-to-right)|\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Math operators (examples)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "23" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "3 + 10 * 2" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "26" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(3 + 10) * 2" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "103" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "3 + 10**2" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6.5" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " (3 + 10) / 2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## String operators\n", + "\n", + "- String concatenation uses the `+` sign:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "this is a string\n" + ] + } + ], + "source": [ + "print('this' + ' is a string')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- String repetition uses the `*`" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spam! Spam! Spam! Spam! \n" + ] + } + ], + "source": [ + "lunch = 'Spam! ' * 4\n", + "print(lunch)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Comparison operators\n", + "\n", + "- Perform logical comparison and return Boolean value\n", + "\n", + " - Equality: `==`\n", + " - Inequality\t`!=`\n", + " - Relative value `<` `>` `<=` `>=`\n", + " - `is` (test whether two variables point to the *same* object)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = [1,2,3]\n", + "b = [1,2,3]\n", + "a is b" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Numerical types\n", + "\n", + "- Integer variables::\n", + "b\n", + " >>> 1 + 1\n", + " 2\n", + " >>> a = 4\n", + "\n", + "\n", + "floats ::\n", + "\n", + " >>> c = 2.1\n", + "\n", + "complex (a native type in Python!) ::\n", + "\n", + " >>> a = 1.5 + 0.5j\n", + " >>> a.real\n", + " 1.5\n", + " >>> a.imag\n", + " 0.5\n", + "\n", + "and booleans::\n", + "\n", + " >>> 3 > 4\n", + " False\n", + " >>> test = (3 > 4)\n", + " >>> test\n", + " False\n", + " >>> type(test)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Conditional execution (if...)\n", + "\n", + "- Syntax (note the indented section):\n", + "```python\n", + " if condition:\n", + " do_something\n", + "```\n", + "- Condition must be statement that evaluates to a boolean value (True or False)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x = 33\n", + "You entered a digit!\n" + ] + } + ], + "source": [ + "x = input('x = ')\n", + "if x.isdigit():\n", + " print('You entered a digit!')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Alternative execution\n", + "\n", + "- Syntax:\n", + "\n", + "```python\n", + " if condition:\n", + " do_something\n", + " else:\n", + " do_alternative\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x = 3a\n", + "You did not enter a digit!\n" + ] + } + ], + "source": [ + "x = input('x = ')\n", + "if x.isdigit():\n", + " print('You entered a digit!')\n", + "else:\n", + " print('You did not enter a digit!')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Chained conditionals\n", + "\n", + "Conditions are evaluated in order and once one it met, the associated code is run.\n", + "\n", + "- Syntax:\n", + "\n", + "```python\n", + " if condition:\n", + " do_something\n", + " elif condition:\n", + " do_alternative1\n", + " else:\n", + " do_alternative2\n", + "```\n", + "\n", + "- NOTE: The `else` at the end is *not* required, so it may be that nothing runs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Complex condition statements\n", + "\n", + "- Use Boolean logic operators `and`, `or`, and `not` to chain together simple conditions:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x = 1\n", + "y = 2\n", + "z = 2\n", + "x = y OR y = z\n", + "x = y = z is False\n" + ] + } + ], + "source": [ + "x = int(input(\"x = \"))\n", + "y = int(input(\"y = \"))\n", + "z = int(input(\"z = \"))\n", + "\n", + "if x == y and y == z:\n", + " print('x = y = z')\n", + "if x == y or y == z:\n", + " print('x = y OR y = z')\n", + "if not (x == y and x == z):\n", + " print('x = y = z is False')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Notes on conditionals\n", + "\n", + "- You have to have at least one statement inside each branch\n", + "- You can use the `pass` command while stubbing\n", + "\n", + "```python\n", + " if x < 0:\n", + " pass # Handle neg values \n", + "```\n", + "\n", + "- You can have as many `elif` branches as you need\n", + "- Conditionals can be nested\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The `for` loop\n", + "\n", + "- Used to repeat a section of code a set number of times\n", + "- Syntax:\n", + "\n", + "```python\n", + " for target in sequence:\n", + " do_statements\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n" + ] + } + ], + "source": [ + "for i in range(10):\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter a number: 22\n", + "Cumsum of 22 is 253\n" + ] + } + ], + "source": [ + "# ask for a number\n", + "x = int(input('Enter a number: '))\n", + "\n", + "# initialize our cumulative sum\n", + "csum = 0\n", + "\n", + "# loop and calculate the cumulative sum\n", + "for i in range(1, x+1):\n", + " csum += i\n", + "\n", + "print('Cumsum of %d is %d' % (x, csum))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Commenting your code\n", + "\n", + "- The `#` symbol indicates the rest of the line is a comment\n", + "- The Python interpreter ignores everything after the `#`\n", + "- ***Use comments liberally***!!!\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What is a function?\n", + "\n", + "- A named sequence of statements that performs a computation or action\n", + "- Functions are called by name\n", + "- Most functions accept inputs (arguments)\n", + "- Some functions return results (return value)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## We've already seen some functions\n", + "\n", + "- `type()`\n", + "\n", + "- Type casting functions\n", + "\n", + " - `int()`, `float()`, `str()`\n", + "\n", + "- `input()`\n", + "\n", + "- `range()`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Creating your own functions\n", + "\n", + "- You have to define the function" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I'm a lumberjack, and I'm okay.\n", + "I sleep all night and I work all day.\n" + ] + } + ], + "source": [ + "def print_lyrics():\n", + " print(\"I'm a lumberjack, and I'm okay.\")\n", + " print(\"I sleep all night and I work all day.\")\n", + "\n", + "# the parentheses are important\n", + "print_lyrics()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Parameters and arguments\n", + "\n", + "- A variable that is an input to a function is called a *parameter*\n", + "- The value of the parameter you pass in when you call the function is an *argument*\n", + "- Functions can accept multiple parameters\n", + "\n", + "```python\n", + "def perimeter(length, n_sides):\n", + "```\n", + "\n", + "- You can also use named parameters that specify a default value\n", + "\n", + "```python\n", + "def perimeter(length, n_sides=4):\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Parameters and arguments (cont.)\n", + "\n", + "- When you call a function, you pass in arguments for each parameter\n", + "\n", + "```python\n", + "perimeter(3)\n", + "perimeter(3, 6)\n", + "perimeter(3, n_sides=6)\n", + "perimeter(length=3, n_sides=6)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Functions can return output\n", + "\n", + "- Use the *return* statement\n", + "- Syntax:\n", + " `return [expression]`\n", + "- Exits the function and returns the value of expression as a result of the call to the function\n", + "- If expression is omitted, returns None\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Using docstrings\n", + "\n", + "\n", + "* A docstring is a string literal that occurs as the first statement\n", + " in a module, function, class, or method definition\n", + "* The built-in PyDoc module and Sphinx\n", + " can automatically generate documentation if the programmer uses\n", + " docstrings\n", + "* See PEP-257 for details:\n", + " http://www.python.org/dev/peps/pep-0257/\n", + "\n", + "```python\n", + "def perimeter(length):\n", + " \"\"\"Calculate the perimeter of a square.\"\"\"\n", + " pass\n", + "\n", + "def complex(real=0.0, imag=0.0):\n", + " \"\"\"\n", + " Form a complex number.\n", + "\n", + " Keyword arguments:\n", + " real -- the real part (default 0.0)\n", + " imag -- the imaginary part (default 0.0)\n", + " \"\"\"\n", + " return (real + imag*1j)\n", + "\n", + "\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment before next class\n", + "\n", + "- Look for the Exercise posted by tomorrow on UVACollab and the lab website.\n", + "- Follow the instructions and upload the final PDF to UVACollab.\n", + "\n", + "You will receive ***points*** for the participation/homework grade by finishing this exercise!\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": "3.7.6" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/03_More_Python.ipynb b/CS4500_CompMethods/lessons/03_More_Python.ipynb new file mode 100644 index 0000000..82a6b08 --- /dev/null +++ b/CS4500_CompMethods/lessons/03_More_Python.ipynb @@ -0,0 +1,1419 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# More Python\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. Describe the characteristics of the list, tuple, and dictionary data structures in Python\n", + "\n", + "2. Perform basic operations with lists including creation, concatenation, \n", + " repetition, slicing, and traversing\n", + "\n", + "3. Perform basic operations with tuples including creation, conversion, \n", + " repetition, slicing, and traversing\n", + "\n", + "4. Perform basic operations with dictionaries including creation, copying, \n", + " updating, and traversing\n", + "\n", + "5. Use lists, tuples, and dictionaries in functions\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The list data structure\n", + "\n", + "* In Python, a list is a *mutable* sequence of values\n", + "* Each value in the list is an element or item\n", + "* Elements can be any Python data type\n", + "* Lists can mix data types\n", + "* Elements can be nested lists\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Creating lists\n", + "\n", + "* Lists are created with comma-separated values inside brackets." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3, 4]\n", + "['swiss', 'cheddar', 'ricotta', 'gouda']\n" + ] + } + ], + "source": [ + "numbers = [1, 2, 3, 4]\n", + "print(numbers)\n", + "cheeses = ['swiss', 'cheddar',\n", + " 'ricotta', 'gouda']\n", + "print(cheeses)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Creating lists\n", + "\n", + "* You can mix types" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 'a', 3.45]\n" + ] + } + ], + "source": [ + "mixed = [1, 'a', 3.45]\n", + "print(mixed)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* and lists with single items (or no items) are lists" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['z']\n", + "[]\n" + ] + } + ], + "source": [ + "single = ['z']\n", + "print(single), type(single)\n", + "empty = []\n", + "print(empty)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Repeating a list\n", + "\n", + "* Use the `*` operator to expand a list via repetition:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['spam', 'spam', 'spam', 'spam']\n", + "[1, 2, 3, 1, 2, 3, 1, 2, 3]\n" + ] + } + ], + "source": [ + "meat = ['spam']*4\n", + "print(meat)\n", + "print([1, 2, 3]*3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## List indexing\n", + "\n", + "* Elements within a list are indexed (*starting with 0*)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['swiss', 'cheddar', 'ricotta', 'gouda']\n", + "swiss\n" + ] + } + ], + "source": [ + "print(cheeses)\n", + "print(cheeses[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Lists are *mutable*" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Feta', 'cheddar', 'ricotta', 'gouda']\n" + ] + } + ], + "source": [ + "cheeses[0] = 'Feta'\n", + "print(cheeses)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Slicing a list\n", + "\n", + "* Like strings and other sequences, lists can be *sliced*.\n", + "\n", + "* **Slicing syntax**: `l[start:stop:stride]`\n", + "\n", + "* All slicing parameters are optional:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4, 5]\n", + "[1, 2, 3]\n", + "[1, 3, 5]\n" + ] + } + ], + "source": [ + "l = [1, 2, 3, 4, 5]\n", + "print(l[3:])\n", + "print(l[:3])\n", + "print(l[::2]) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Note that `l[start:stop]` contains the elements with indices `i`\n", + " such as `start<= i < stop` (`i` ranging from `start` to\n", + " `stop-1`). \n", + "\n", + "* Therefore, `l[start:stop]` has `(stop-start)`\n", + " elements." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Changing a slice\n", + "\n", + "* You can use slices to modify the contents of a list:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Meghan', 'Tricia', 'Juan', 'Alton', 'Darrel', 'Jen']\n", + "['Meghan', 'Sam', 'Kerri', 'Alton', 'Darrel', 'Jen']\n", + "['Meghan', 'Sam', 'Kerri', 'Tayla', 'Jen']\n" + ] + } + ], + "source": [ + "roster = ['Meghan', 'Tricia', 'Juan',\n", + " 'Alton', 'Darrel', 'Jen']\n", + "print(roster)\n", + "roster[1:3] = ['Sam', 'Kerri']\n", + "print(roster)\n", + "roster[3:5] = ['Tayla']\n", + "print(roster)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Inserting elements\n", + "\n", + "* Slice notation" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Meghan', 'Sam', 'Kerri', 'Tayla', 'Jen']\n", + "['Meghan', 'Sam', 'Dana', 'Ryan', 'Kerri', 'Tayla', 'Jen']\n" + ] + } + ], + "source": [ + "print(roster)\n", + "roster[2:2] = ['Dana', 'Ryan']\n", + "print(roster)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Lists have an ``insert`` method:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Meghan', 'Sam', 'Jakob', 'Dana', 'Ryan', 'Kerri', 'Tayla', 'Jen']\n" + ] + } + ], + "source": [ + "roster.insert(2, 'Jakob')\n", + "print(roster)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Deleting elements\n", + "\n", + "* Setting a slice to empty list will delete those elements:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Meghan', 'Sam', 'Jakob', 'Dana', 'Ryan', 'Kerri', 'Tayla', 'Jen']\n", + "['Meghan', 'Sam', 'Jakob', 'Kerri', 'Tayla', 'Jen']\n" + ] + } + ], + "source": [ + "print(roster)\n", + "roster[3:5] = []\n", + "print(roster)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Or you can use the del keyword:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Meghan', 'Sam', 'Jakob', 'Kerri', 'Tayla', 'Jen']\n", + "['Meghan', 'Kerri', 'Tayla', 'Jen']\n" + ] + } + ], + "source": [ + "print(roster)\n", + "del roster[1:3]\n", + "print(roster)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The append and extend methods\n", + " \n", + "* The `append` method adds individual items to a list:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Meghan', 'Kerri', 'Tayla', 'Jen', 'Tonya']\n" + ] + } + ], + "source": [ + "roster.append('Tonya')\n", + "print(roster)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The `extend` method adds a list to the end of an existing list:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Meghan', 'Kerri', 'Tayla', 'Jen', 'Tonya', 'Ian', 'Stacie']\n" + ] + } + ], + "source": [ + "adds = ['Ian', 'Stacie'] \n", + "roster.extend(adds)\n", + "print(roster)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Extending a list with operators\n", + "\n", + "* Can also use `+=` operator" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Meghan', 'Kerri', 'Tayla', 'Jen', 'Tonya', 'Ian', 'Stacie', 'Anya']\n" + ] + } + ], + "source": [ + "roster += ['Anya']\n", + "print(roster)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "* Or simply the `+` operator" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3] [4, 5, 6] [1, 2, 3, 4, 5, 6]\n" + ] + } + ], + "source": [ + "a = [1, 2, 3]\n", + "b = [4, 5, 6]\n", + "c = a + b\n", + "print(a, b, c)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "* The `+` operator returns a new list that is a concatenation of two lists" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## List assignment and aliasing\n", + "\n", + "* The slice operator returns a copy of a list" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 9, 4] [1, 2, 9, 4] [1, 2, 3, 4]\n" + ] + } + ], + "source": [ + "a = [1, 2, 3, 4]\n", + "b = a\n", + "c = a[:]\n", + "a[2] = 9\n", + "print(a, b, c)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Traversing a list\n", + "------------------\n", + "\n", + "* There are many ways to loop over a list:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meghan Kerri Tayla Jen Tonya Ian Stacie Anya \n", + "Meghan Kerri Tayla Jen Tonya Ian Stacie Anya \n", + "0 : Meghan 1 : Kerri 2 : Tayla 3 : Jen 4 : Tonya 5 : Ian 6 : Stacie 7 : Anya \n" + ] + } + ], + "source": [ + "for index in range(len(roster)):\n", + " print(roster[index], end=' ')\n", + "print()\n", + "\n", + "for student in roster:\n", + " print(student, end=' ')\n", + "print()\n", + "\n", + "for index, student in enumerate(roster):\n", + " print(index, ':', student, end=' ')\n", + "print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Note how ``enumerate`` keeps track of the index and the item, which\n", + " can come in handy." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Nested lists\n", + "\n", + "* You can nest lists of lists of lists..." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n", + "[1, 2, 3]\n", + "2\n" + ] + } + ], + "source": [ + "nested = [[1,2,3],[4,5,6],[7,8,9]]\n", + "print(nested)\n", + "print(nested[0])\n", + "print(nested[0][1])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Traversing nested lists\n", + "\n", + "* Each nested list can be traversed in the same way as an individual\n", + " list:\n", + "\n", + "* By index:\n", + "\n", + "```python\n", + "for i in range(len(nested)):\n", + " for j in range(len(nested[i])):\n", + " print(nested[i][j])\n", + "```\n", + "\n", + "* Or item:\n", + "\n", + "```python\n", + "for nest in nested:\n", + " for item in nest:\n", + " print(item)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Using lists: cumulate.py\n", + "\n", + "* You can pass lists as arguments to functions:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "204\n" + ] + } + ], + "source": [ + "def cumulate(seq):\n", + " c_sum = 0\n", + " for item in seq:\n", + " c_sum += item\n", + " return c_sum\n", + "\n", + "a = [12, 78, 32, 82]\n", + "s = cumulate(a)\n", + "print(s)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Returning lists from functions\n", + "\n", + "* You can return lists from functions:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['B', 'O', 'O', 'M', 'E', 'R']\n" + ] + } + ], + "source": [ + "def only_upper(t):\n", + " res = []\n", + " for s in t:\n", + " if s.isupper():\n", + " res.append(s)\n", + " return res\n", + "\n", + "text = 'Bold cOlOrs Make for Easy Reading'\n", + "secret = only_upper(text)\n", + "print(secret)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Modifying lists in functions\n", + "\n", + "* In Python, arguments are passed *by reference*\n", + "* The parameter in the function is an alias for the argument that was passed in\n", + "* If a mutable object is changed inside the function, it is also changed \n", + " outside the function!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Example: By reference\n", + "\n", + "* Here we illustrate modifying a list in a function:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3]\n", + "Passed in: [1, 2, 3]\n", + "Changed to: [1, 2, 3, 'new item']\n", + "[1, 2, 3, 'new item']\n" + ] + } + ], + "source": [ + "def change(seq):\n", + " print('Passed in: ' + str(seq))\n", + " seq.append('new item')\n", + " print('Changed to: ' + str(seq))\n", + "\n", + "original = [1, 2, 3]\n", + "print(original)\n", + "change(original)\n", + "print(original)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## OOP and Methods\n", + "\n", + "* Let's say we've defined a list, ``r``::\n", + "\n", + "```python\n", + " r = [1,2,3,4]\n", + "```\n", + "\n", + "* The notation ``r.method()`` (e.g., ``r.sort(), r.append(3), r.pop()``) is our first example of object-oriented programming (OOP).\n", + "\n", + "* Being a ``list``, the object ``r`` owns the *method* ``function`` that\n", + " is called using the notation **.**. \n", + "\n", + "* No further knowledge of OOP than understanding the notation **.** is\n", + " necessary for most of this class.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Discovering methods\n", + "\n", + "* You can always look up methods in books/docs, but...\n", + "\n", + "* One easy way is to use IPython tab-completion (press tab):\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 2, 3, 4]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's try it out\n", + "r = [1, 2, 3, 4]\n", + "r" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The `tuple` data structure\n", + "\n", + "* In Python, a ``tuple`` is an immutable sequence of values\n", + "* Each value in the tuple is an element or item\n", + "* Elements can be any Python data type\n", + "* Tuples can mix data types\n", + "* Elements can be nested tuples\n", + "* Essentially tuples are immutable lists\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Creating tuples\n", + "\n", + "* You create tuples like you would lists, but with parentheses instead\n", + " of brackets" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 2, 3, 4)\n" + ] + } + ], + "source": [ + "numbers = (1, 2, 3, 4)\n", + "print(numbers)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Note one difference, that tuples with only a single item reduce to\n", + " that item" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a \n", + "('a',) \n", + "('a',) \n" + ] + } + ], + "source": [ + "t1 = ('a')\n", + "print(t1, type(t1))\n", + "t2 = ('a',)\n", + "print(t2, type(t2))\n", + "t3 = tuple('a')\n", + "print(t3, type(t3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* And you can turn a `list` into a `tuple`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 2, 3, 4)\n", + "('p', 'a', 'r', 'r', 'o', 't')\n" + ] + } + ], + "source": [ + "alist = [1,2,3,4]\n", + "atuple = tuple(alist)\n", + "print(atuple)\n", + "\n", + "atuple = tuple('parrot')\n", + "print(atuple)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Tuples are *immutable*\n", + "\n", + "Meaning you can't change them!" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'tuple' object does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0matuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'R'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" + ] + } + ], + "source": [ + "atuple[3] = 'R'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Operations on tuples\n", + "\n", + "* Tuples support all the standard sequence operations, including:\n", + "* Membership tests (using the `in` keyword)\n", + "* Comparison (element-wise)\n", + "* Iteration (e.g., in a `for` loop)\n", + "* Concatenation and repetition\n", + "* The `len` function" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Tuples and functions\n", + "\n", + "* Many Python functions return tuples\n", + "* Remember that a function can only return one value\n", + "* However, if multiple objects are packaged together into a tuple, then the \n", + " function can return the objects inside a single tuple" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Min Max example" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 98)\n", + "(' ', 'w')\n" + ] + } + ], + "source": [ + "def min_max(t):\n", + " \"\"\"Returns the smallest and largest \n", + " elements of a sequence as a tuple\"\"\"\n", + " return (min(t), max(t))\n", + "\n", + "seq = [12, 98, 23, 74, 3, 54]\n", + "print(min_max(seq))\n", + "\n", + "string = 'She turned me into a newt!'\n", + "print(min_max(string))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Passing tuples as arguments\n", + "\n", + "* A parameter name that begins with `*` gathers all the arguments into a `tuple`\n", + "* This allows functions to take a variable number of arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 2.0, 'three')\n" + ] + } + ], + "source": [ + "def printall(*args):\n", + " print(args)\n", + "\n", + "printall(1, 2.0, 'three')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The zip function\n", + "\n", + "* Built-in function that takes two or more sequences and \"zips\" them into a \n", + " list of tuples, where each tuple contains one element from each sequence\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('a', 0)\n", + "('b', 1)\n", + "('c', 2)\n" + ] + } + ], + "source": [ + "s = 'abc'\n", + "t = [0, 1, 2]\n", + "z = zip(s, t)\n", + "for i in z:\n", + " print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The dictionary data structure\n", + "\n", + "* In Python, a dictionary (or ``dict``) is mapping between a set of\n", + " indices (keys) and a set of values\n", + "* The items in a dictionary are key-value pairs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The dictionary data structure\n", + "\n", + "* Keys can be any Python data type\n", + "* Because keys are used for indexing, they should be immutable\n", + "* Values can be any Python data type\n", + "* Values can be mutable or immutable" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Creating a dictionary\n", + "\n", + "* There are a number of ways to create and fill a dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{}\n", + "{'one': 'uno'}\n", + "{'one': 'uno', 'two': 'dos'}\n", + "{'one': 'uno', 'two': 'dos', 'three': 'tres'}\n" + ] + } + ], + "source": [ + "eng2sp = dict()\n", + "print(eng2sp)\n", + "\n", + "eng2sp['one'] = 'uno'\n", + "print(eng2sp)\n", + "\n", + "eng2sp['two'] = 'dos'\n", + "print(eng2sp)\n", + "\n", + "eng2sp = {'one': 'uno', 'two': 'dos', 'three': 'tres'}\n", + "print(eng2sp)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Dictionary indexing\n", + "\n", + "* Dictionaries are indexed by keys, not integers\n", + "* You can use `in` to check for the existance of a key\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tres\n", + "I can't translate the word 'five'.\n" + ] + } + ], + "source": [ + "print(eng2sp['three'])\n", + "if 'five' in eng2sp:\n", + " print(eng2sp['five'])\n", + "else:\n", + " print(\"I can't translate the word 'five'.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Histogram example" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'b': 1, 'r': 2, 'o': 2, 'n': 1, 't': 1, 's': 2, 'a': 1, 'u': 2}\n" + ] + } + ], + "source": [ + "def histogram(seq):\n", + " d = dict()\n", + " for element in seq:\n", + " if element not in d:\n", + " d[element] = 1\n", + " else:\n", + " d[element] += 1\n", + " return d\n", + "\n", + "h = histogram('brontosaurus')\n", + "print(h)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment before next class\n", + "\n", + "- We posted a function-writing assignment on UVACollab\n", + "- Look for at least one more assignment to prepare you for writing a behavioral experiment!\n", + "\n", + "You will receive ***points*** for the participation/homework grade by finishing these tasks!\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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/04_Python_Forever.ipynb b/CS4500_CompMethods/lessons/04_Python_Forever.ipynb new file mode 100644 index 0000000..994b9be --- /dev/null +++ b/CS4500_CompMethods/lessons/04_Python_Forever.ipynb @@ -0,0 +1,2163 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Python Forever!!!\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. State the purpose of modules in Python and identify the common modules\n", + " used in scientific programming\n", + "\n", + "2. Discuss the concepts of *namespace* and variable *scope*\n", + "\n", + "3. Import modules and libraries and use imported functions\n", + "\n", + "4. Create N-dimensional arrays\n", + "\n", + "5. Index values in those arrays\n", + "\n", + "6. Perform operations on those arrays\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Modules\n", + "\n", + "* A **Module** is a file that contains a collection of related functions\n", + "* Python has *hundreds* of standard modules\n", + "* These are known as the Python Standard Library (http://docs.python.org/library/)\n", + "* You can also create and use add-on modules\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Using import\n", + "\n", + "* To use a module, you first have to import it into your namespace\n", + "* To import the entire module:\n", + "\n", + "```python\n", + " import module_name\n", + "```\n", + "\n", + "* To import specific functions:\n", + "\n", + "```python\n", + " from module_name import function_name\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The `math` module\n", + "\n", + "The standard math module includes:\n", + "\n", + "* Number-theoretic and representation functions\n", + "* Power and logarithmic functions\n", + "* Trigonometric functions\n", + "* Hyperbolic functions\n", + "* Angular conversion\n", + "* Constants\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Using the math module\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7071067811865475\n" + ] + } + ], + "source": [ + "import math\n", + "degrees = 45\n", + "radians = (degrees / 360) * 2 * math.pi\n", + "print(math.sin(radians))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Dot notation\n", + "\n", + "* Why did we use ``math.sin()`` instead of just ``sin()``?\n", + "* Try this:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'sin' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mradians\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[0;31mNameError\u001b[0m: name 'sin' is not defined" + ] + } + ], + "source": [ + "print(sin(radians))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Dot notation allows the Python interpreter to organize and divide the **namespace**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What not to do...\n", + "\n", + "* Why not just do:\n", + "```python\n", + " from math import * \n", + "```\n", + "\n", + "### **Check yourself before you wreck yourself!!!**\n", + "\n", + "* Makes the code harder to read and understand: where do symbols come \n", + " from?\n", + "\n", + "* Makes it impossible to guess the functionality by the context and\n", + " the name (hint: `os.name` is the name of the OS), and to profit\n", + " usefully from tab completion.\n", + "\n", + "* Restricts the variable names you can use: `os.name` might override \n", + " `name`, or vise-versa.\n", + "\n", + "* Creates possible name clashes between modules.\n", + "\n", + "* Makes the code impossible to statically check for undefined\n", + " symbols.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Libraries\n", + "\n", + "* Libraries are simply larger collections of modules.\n", + "\n", + "* We will be exploring a number of libraries, such as ``numpy``, ``scipy``,\n", + " and ``matplotlib`` in this class.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# NumPy (https://numpy.org)\n", + "\n", + "Numerical Analysis in Python\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What is Numpy\n", + "\n", + "**Python** has:\n", + "\n", + " - built-in: lists, integers, floating point\n", + "\n", + " - for numerics --- more is needed (efficiency, convenience)\n", + "\n", + "**Numpy** is:\n", + "\n", + " - extension package to Python for multidimensional arrays\n", + "\n", + " - closer to hardware (efficiency)\n", + "\n", + " - designed for scientific computation (convenience)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## For example\n", + "\n", + "An array containing ---\n", + "\n", + "* discretized time of an experiment/simulation\n", + "\n", + "* signal recorded by a measurement device\n", + "\n", + "* pixels of an image\n", + "\n", + "* ...\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "a = np.array([0, 1, 2, 3, 4])\n", + "a" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The Basics\n", + "\n", + "* NumPy's main object is the homogeneous multidimensional array. \n", + "\n", + "* It is a table of elements (usually numbers), all of the same type,\n", + " indexed by a tuple of positive integers. \n", + "\n", + "* In Numpy dimensions are called ''axes''. The number of axes is ''rank''.\n", + "\n", + "### **For example**\n", + "\n", + "* Coordinates of a point in 3D space `[1, 2, 1]`:\n", + "\n", + " * Is an array of rank 1, because it has one axis.\n", + " * That axis has a length of 3.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The ndarray\n", + "\n", + "* Numpy's array class is called `ndarray`. (It is also known by the\n", + " alias `array`.) \n", + "\n", + " * **ndarray.ndim**: the number of axes (dimensions) of the array. In\n", + " the Python world, the number of dimensions is referred to as\n", + " ''rank''.\n", + "\n", + " * **ndarray.shape**: the dimensions of the array. This is a tuple of\n", + " integers indicating the size of the array in each\n", + " dimension.\n", + "\n", + " * **ndarray.size**: the total number of elements of the array. This is\n", + " equal to the product of the elements of `shape`.\n", + "\n", + " * **ndarray.dtype**: an object describing the type of the elements in the\n", + " array.\n", + "\n", + " * **ndarray.itemsize**: the size in bytes of each element of the array. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Creating arrays\n", + "### 1D arrays" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.array([0, 1, 2, 3])\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.ndim" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4,)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Creating arrays\n", + "\n", + "### 2D, 3D, ...." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 2],\n", + " [3, 4, 5]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b = np.array([[0, 1, 2], [3, 4, 5]]) # 2 x 3 array\n", + "b" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 3)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(b) # returns the size of the first dimension" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[1],\n", + " [2]],\n", + "\n", + " [[3],\n", + " [4]]])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# a 3D array\n", + "c = np.array([[[1], [2]], [[3], [4]]])\n", + "c" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 2, 1)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## In practice\n", + "\n", + "We rarely enter items one by one...\n", + "\n", + "* Evenly spaced:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 2 3 4 5 6 7 8 9]\n", + "[1 3 5 7]\n" + ] + } + ], + "source": [ + "a = np.arange(10) # 0 .. n-1 (!)\n", + "print(a)\n", + "b = np.arange(1, 9, 2) # start, end (exlusive), step\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- or by number of points:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0.2 0.4 0.6 0.8 1. ]\n", + "[0. 0.2 0.4 0.6 0.8]\n" + ] + } + ], + "source": [ + "c = np.linspace(0, 1, 6) # start, end, num-points\n", + "print(c)\n", + "d = np.linspace(0, 1, 5, endpoint=False)\n", + "print(d)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Common arrays\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.]])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.ones((3, 3)) # reminder: (3, 3) is a tuple\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0., 0.],\n", + " [0., 0.]])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b = np.zeros((2, 2))\n", + "b" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = np.eye(3)\n", + "c" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Random numbers\n", + "\n", + "Uses the Mersenne Twister pseudo random number generator (PRNG):" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.41639869, 0.00134999, 0.33074725, 0.83089662])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.random.rand(4) # uniform in [0, 1]\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.19730317, 0.27733292, -1.29812388, -1.76774009])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b = np.random.randn(4) # Gaussian/normal\n", + "b" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.27365563, 0.7121848 , 0.17829841],\n", + " [0.79088453, 0.87553374, 0.93704718],\n", + " [0.26257238, 0.98724751, 0.21704664]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = np.random.rand(3, 3) # multiple dimensions\n", + "c" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Set the seed\n", + "\n", + "It is possible to set the random number generator seed to get the same\n", + "series of numbers generated:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.19151945 0.62210877 0.43772774]\n", + "[0.19151945 0.62210877 0.43772774 0.78535858 0.77997581]\n" + ] + } + ], + "source": [ + "np.random.seed(1234)\n", + "print(np.random.rand(3))\n", + "np.random.seed(1234)\n", + "print(np.random.rand(5))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Basic data types\n", + "\n", + "You probably noted the ``1`` and ``1.`` above. These are different\n", + "data types:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('int64')" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.array([1, 2, 3])\n", + "a.dtype\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b = np.array([1., 2., 3.])\n", + "b.dtype" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Much of the time you don't necessarily need to care, but remember they\n", + "are there.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Choose your own dtype adventure\n", + "\n", + "You can control your data type destiny:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = np.array([1, 2, 3], dtype=np.float)\n", + "c.dtype" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The **default** data type is floating point:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.ones((3, 3))\n", + "a.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b = np.linspace(0, 1, 6)\n", + "b.dtype" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The many choices...\n", + "\n", + "There are also other types:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('complex128')" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = np.array([1+2j, 3+4j, 5+6*1j])\n", + "d.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('bool')" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "e = np.array([True, False, False, True])\n", + "e.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype(' b" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Logical operations\n", + "\n", + "And perform fast logical operations:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, True, True, False])" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.array([1, 1, 0, 0], dtype=bool)\n", + "b = np.array([1, 0, 1, 0], dtype=bool)\n", + "\n", + "a | b" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, False, False, False])" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a & b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**: For arrays: \"``&``\" and \"``|``\" for logical operations, not\n", + " \"``and``\" and \"``or``\".\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Shape mismatches\n", + "\n", + "What if things don't line up?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "operands could not be broadcast together with shapes (4,) (2,) ", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\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 1\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\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----> 2\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\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[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (4,) (2,) " + ] + } + ], + "source": [ + "a = np.array([1, 2, 3, 4])\n", + "a + np.array([1, 2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**'Broadcast'?** We'll return to that later." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Basic linear algebra\n", + "\n", + "Matrix multiplication:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "a = np.triu(np.ones((3, 3)), 1) # see help(np.triu) or np.triu?\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0., 2., 3.],\n", + " [0., 0., 3.],\n", + " [0., 0., 0.]])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b = np.diag([1, 2, 3])\n", + "a.dot(b) # same as `a @ b`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Basic linear algebra\n", + "\n", + "Transpose:" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 1., 0.]])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.T" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Basic linear algebra\n", + "\n", + "Inverses and linear equation systems:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 1., 1.],\n", + " [0., 2., 1.],\n", + " [0., 0., 3.]])" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = a + b\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.00000000e+00, 0.00000000e+00, 2.77555756e-17],\n", + " [0.00000000e+00, 1.00000000e+00, 2.77555756e-17],\n", + " [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "B = np.linalg.inv(A)\n", + "B @ A" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Eigenvalues:" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 2., 3.])" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.eigvals(A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "... and so on, see ``np.linalg?``" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Basic reductions\n", + "\n", + "Computing sums:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.array([1, 2, 3, 4])\n", + "\n", + "# called as an object method\n", + "x.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# or as a module method\n", + "np.sum(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Sum by rows and by columns\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 1],\n", + " [2, 2]])" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.array([[1, 1], [2, 2]])\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3, 3])" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.sum(axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 4])" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.sum(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Other reductions\n", + "\n", + "Work the same way (and take ``axis=``)\n", + "\n", + "- Statistics:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3., 4., 2.])" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = np.array([[1, 2, 3], [5, 6, 1]])\n", + "np.mean(y, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2., 5.])" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(y, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.9148542155126762" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y.std()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Other reductions\n", + "\n", + "- Extrema:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.array([1, 3, 2])\n", + "x.min()" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.argmax() # index of maximum" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Other reductions\n", + "- Logical operations" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([True, True, False])" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.any([True, True, False])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Further reading\n", + "\n", + "- ... and many more (best to learn as you go).\n", + "\n", + "- If you'd like a much more thorough introduction to NumPy, please read the SciPy Lecture Notes: https://scipy-lectures.org/intro/numpy/index.html" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment before next class\n", + "\n", + "- We posted a few assignments on UVACollab\n", + "- We'll work on those now for the rest of class, though they are due next week.\n", + "\n", + "You will receive ***points*** for the participation/homework grade by finishing these tasks!\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": "3.7.6" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/05_FileIO_ExpDesign.ipynb b/CS4500_CompMethods/lessons/05_FileIO_ExpDesign.ipynb new file mode 100644 index 0000000..1580049 --- /dev/null +++ b/CS4500_CompMethods/lessons/05_FileIO_ExpDesign.ipynb @@ -0,0 +1,1248 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# File I/O and Experimental Design\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 and write basic text files\n", + "\n", + "2. Read and write CSV files\n", + "\n", + "3. Know how to pickle objects\n", + "\n", + "then:\n", + "\n", + "4. Fundamentals of experiment design\n", + "\n", + "5. The link between science and coding\n", + "\n", + "6. Dependent vs. Independent variables\n", + "\n", + "7. Constraints on list structure\n", + "\n", + "8. How to make a simple list of dictionaries to define trials\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Sorting data in files\n", + "\n", + "- Say we have some numbers in a file:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29 34 7 1 32 14 57 54 75 92 81 48 28 76 70 19 97 45 87 52 \n" + ] + } + ], + "source": [ + "!more spaced_numbers.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Let's read them in, sort them, and write them back out sorted!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Reading from files\n", + "\n", + "* Since these numbers are all on one line, we just have to read one\n", + " line in:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29 34 7 1 32 14 57 54 75 92 81 48 28 76 70 19 97 45 87 52 \n", + "\n" + ] + } + ], + "source": [ + "# you can open a file for reading, writing, or appending\n", + "f = open('spaced_numbers.txt', 'r')\n", + "\n", + "# Read one line in\n", + "line = f.readline()\n", + "\n", + "# print what we read in\n", + "print(line)\n", + "\n", + "# close the file\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Files are objects, too!\n", + "\n", + "* You can see that `f` is a file object with methods:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['buffer', 'close', 'closed', 'detach', 'encoding', 'errors', 'fileno', 'flush', 'isatty', 'line_buffering', 'mode', 'name', 'newlines', 'read', 'readable', 'readline', 'readlines', 'reconfigure', 'seek', 'seekable', 'tell', 'truncate', 'writable', 'write', 'write_through', 'writelines']\n" + ] + } + ], + "source": [ + "# print out all non-hidden attributes and methods\n", + "print([x for x in dir(f) if x[0]!='_'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Parsing the numbers\n", + "\n", + "* We need to turn our big string into a list of numbers:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'29 34 7 1 32 14 57 54 75 92 81 48 28 76 70 19 97 45 87 52 \\n'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "line" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* First we can use ``strip`` to pull off the trailing ``newline``." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'29 34 7 1 32 14 57 54 75 92 81 48 28 76 70 19 97 45 87 52'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "line.strip()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* We can combine that with split to make a list of numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['29', '34', '7', '1', '32', '14', '57', '54', '75', '92', '81', '48', '28', '76', '70', '19', '97', '45', '87', '52']\n" + ] + } + ], + "source": [ + "# note how you can apply the strip and split right after one another\n", + "# that's because strip returns a string\n", + "print(line.strip().split(' '))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Convert to numbers\n", + "\n", + "* Now we have a list of strings, but we want numbers.\n", + "\n", + "* We could loop over each item in that list with a for loop, creating a new list." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "ints = []\n", + "for s in line.strip().split(' '):\n", + " ints.append(int(s))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Or, we can use a list comprehension to convert it in one line :)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[29, 34, 7, 1, 32, 14, 57, 54, 75, 92, 81, 48, 28, 76, 70, 19, 97, 45, 87, 52]\n" + ] + } + ], + "source": [ + "ints = [int(s) for s in line.strip().split(' ')]\n", + "print(ints)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Sorting things out\n", + "\n", + "* Now that we have a list, sorting is easy :)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 7, 14, 19, 28, 29, 32, 34, 45, 48, 52, 54, 57, 70, 75, 76, 81, 87, 92, 97]\n" + ] + } + ], + "source": [ + "ints.sort()\n", + "print(ints)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[97, 92, 87, 81, 76, 75, 70, 57, 54, 52, 48, 45, 34, 32, 29, 28, 19, 14, 7, 1]\n" + ] + } + ], + "source": [ + "# you can reverse it, too!\n", + "ints.sort(reverse=True)\n", + "print(ints)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Write it back out\n", + "\n", + "* Now we have our sorted list, let's save it back to file\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "with open('spaced_numbers_sorted.txt', 'w') as f:\n", + " for i in ints:\n", + " f.write('%d ' % i)\n", + " f.write('\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "97 92 87 81 76 75 70 57 54 52 48 45 34 32 29 28 19 14 7 1 \n" + ] + } + ], + "source": [ + "!more spaced_numbers_sorted.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Random Numbers\n", + "\n", + "How did I generate those random numbers in the first place?" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# import the random module\n", + "import random\n", + "\n", + "# open a file for writing\n", + "with open('spaced_numbers.txt', 'w') as f:\n", + " # loop some number of times\n", + " for i in range(20):\n", + " # write out a random integer, followed by a space\n", + " f.write('%d ' % random.randint(0, 100))\n", + " f.write('\\n')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Random Numbers\n", + "\n", + "* You have loads of random operations at your fingertips:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['BPF', 'LOG4', 'NV_MAGICCONST', 'RECIP_BPF', 'Random', 'SG_MAGICCONST', 'SystemRandom', 'TWOPI', 'betavariate', 'choice', 'choices', 'expovariate', 'gammavariate', 'gauss', 'getrandbits', 'getstate', 'lognormvariate', 'normalvariate', 'paretovariate', 'randint', 'random', 'randrange', 'sample', 'seed', 'setstate', 'shuffle', 'triangular', 'uniform', 'vonmisesvariate', 'weibullvariate']\n" + ] + } + ], + "source": [ + "print([m for m in dir(random) if m[0] != '_'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* `random.shuffle` is particularly useful in our work to randomize a list:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[34, 57, 54, 81, 29, 1, 52, 28, 45, 75, 7, 70, 48, 87, 92, 76, 97, 14, 32, 19]\n" + ] + } + ], + "source": [ + "random.shuffle(ints)\n", + "print(ints)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What about CSV files?\n", + "\n", + "* Most often our data are in formatted files, such as comma-separated values (CSV) files, not just lists of numbers:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Subject,Performance\n", + "0,0.19839211032002024\n", + "1,0.41428489112125344\n", + "2,0.027715898314496612\n", + "3,0.05627103270567213\n", + "4,0.27079871696692148\n", + "5,0.93739232241039394\n", + "6,0.49069767020105493\n", + "7,0.24287893232441449\n", + "8,0.97942327679701313\n", + "9,0.3229346781148571\n" + ] + } + ], + "source": [ + "!more exp_res.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Using the csv module\n", + "\n", + "* We could parse the file with strip and split like before\n", + "\n", + "* or we can use the builtin ``csv`` module to read and write them:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'Subject': '0', 'Performance': '0.19839211032002024'},\n", + " {'Subject': '1', 'Performance': '0.41428489112125344'},\n", + " {'Subject': '2', 'Performance': '0.027715898314496612'},\n", + " {'Subject': '3', 'Performance': '0.05627103270567213'},\n", + " {'Subject': '4', 'Performance': '0.27079871696692148'},\n", + " {'Subject': '5', 'Performance': '0.93739232241039394'},\n", + " {'Subject': '6', 'Performance': '0.49069767020105493'},\n", + " {'Subject': '7', 'Performance': '0.24287893232441449'},\n", + " {'Subject': '8', 'Performance': '0.97942327679701313'},\n", + " {'Subject': '9', 'Performance': '0.3229346781148571'}]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import csv\n", + "\n", + "# create a dictionary reader\n", + "dr = csv.DictReader(open('exp_res.csv','r'))\n", + "\n", + "# read in all the lines into a list of dicts\n", + "d = [l for l in dr]\n", + "\n", + "# note it creates OrderedDict instances!!!\n", + "d" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Pickling!\n", + "\n", + "* Often we want to dump and object to file for future use.\n", + "\n", + "* Pickling allows us to *serialize* Python objects (i.e., turn them into a byte stream that can be saved to file):\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "'cat' is not recognized as an internal or external command,\n", + "operable program or batch file.\n" + ] + } + ], + "source": [ + "import pickle\n", + "\n", + "# dump the list of ordered dicts to a file \n", + "# (note the 'b' in the 'wb', which means a \n", + "# binary stream instead of a ascii text stream)\n", + "pickle.dump(d, open('my_dict.pickle', 'wb'))\n", + "\n", + "!cat my_dict.pickle" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Unpickling\n", + "\n", + "* As you can see, the serialization process is not usually human-readable\n", + "* Once pickled, it's easy to load it back:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'Subject': '0', 'Performance': '0.19839211032002024'},\n", + " {'Subject': '1', 'Performance': '0.41428489112125344'},\n", + " {'Subject': '2', 'Performance': '0.027715898314496612'},\n", + " {'Subject': '3', 'Performance': '0.05627103270567213'},\n", + " {'Subject': '4', 'Performance': '0.27079871696692148'},\n", + " {'Subject': '5', 'Performance': '0.93739232241039394'},\n", + " {'Subject': '6', 'Performance': '0.49069767020105493'},\n", + " {'Subject': '7', 'Performance': '0.24287893232441449'},\n", + " {'Subject': '8', 'Performance': '0.97942327679701313'},\n", + " {'Subject': '9', 'Performance': '0.3229346781148571'}]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# open the file back for reading\n", + "d2 = pickle.load(open('my_dict.pickle','rb'))\n", + "d2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Notes on Pickles\n", + "\n", + "* Delicious, but...\n", + "\n", + "* Note that pickles are *NOT* portable across languages\n", + "\n", + "* If you require interoperability, then you'll want to use a different\n", + " file format\n", + "\n", + "* Raw text is about as portable as they get, but is not always the\n", + " most efficient\n", + "\n", + "* My favorite data storage format is Hierarchical Data Format v. 5 (HDF5), which is widely used (even adopted by Matlab) and has I/O libraries for almost every programming language.\n", + "\n", + " * e.g., [h5py](https://www.h5py.org/)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Experimental Design\n", + "\n", + "## Science is hard\n", + "\n", + "![](https://imgs.xkcd.com/comics/purity.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## It all starts with a question\n", + "\n", + "### What are we trying to do, anyway?\n", + "\n", + "![](./figs/brain_quest.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The Scientific method as a computer program" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Science basically involves figuring out how a function works by passing in variables and observing the output." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "slideshow": { + "slide_type": "" + } + }, + "outputs": [], + "source": [ + "def human_brain(*args, **kwargs):\n", + " # stuff happens\n", + " \n", + " return output" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Independent vs. dependent variables\n", + "\n", + "- The inputs are the ***independent*** variables\n", + " - e.g., items, conditions, etc...\n", + "- The outputs are the ***dependent*** variables\n", + " - e.g., choices, reaction times, etc...\n", + "- There are also ***controlled*** variables that you keep the same. \n", + " - The goal is to prevent their influence the effect of independent on dependent variables.\n", + " - e.g., if you changed items when you changed conditions, you wouldn't know if it was the items or the conditions that affected the output." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The Hypothesis\n", + "\n", + "- The scientist makes a conjecture about how change in independent variables will give rise to change in dependent variables.\n", + "\n", + "- The hypothesis is an instantiation of your ***model*** of the world, even if it's a poorly specified model.\n", + "\n", + "- It could be that the independent variables have no relation to the dependent variables, in which case we need a new hypothesis." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Experiments test hypotheses\n", + "\n", + "- The goal is to design an experiment that can reliably ***disprove*** your hypothesis.\n", + "- Ideally, your hypothesis is a *generative* model and you can run simulations to help you design a powerful experiment." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Generative model?\n", + "\n", + "- A ***generative*** model is like a function you've written to mimic the behavior of the function you're trying to understand.\n", + "- The alternative is a ***descriptive/discriminative*** model, which tests whether a change in the input to a function gives rise to a significant change in the output.\n", + "\n", + "(Details in another course, Quantified Cognition, which I typically teach in the Spring.)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Learning by example: Flanker Task\n", + "\n", + "Which of these is harder to indicate the direction the middle arrow is pointing?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# <<<<<<<" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# <<<><<<" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# ===<===" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The Flanker task\n", + "\n", + "Tests the role of attention and cognitive control in decision-making.\n", + "\n", + "### Hypothesis\n", + "\n", + "The items that flank a target item will affect processing of that item, requiring exertion of cognitive control to overcome the interference." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## How should we test this hypothesis?\n", + "\n", + "- How many trials do we need?\n", + "- Should we do a between- or within-subject manipulation?\n", + "- What conditions should we include?\n", + "- What proportion of each condition should we include?\n", + "- Does the order of the items matter?\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## List generation vs. Stimulus Presentation\n", + "\n", + "- Most experiments can separate the generation of random lists that govern what we will present to participants and the code necessary to handle the presentation of stimuli and collect the responses.\n", + "\n", + " - The primary exception would be adaptive experiments that depend on the behavior (or neural activity) of the participant to determine subsequent trials.\n", + "\n", + "- We'll focus here on the list generation portion of the experiment." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Define the trial types\n", + "\n", + "We have the following variables:\n", + "\n", + "- Condition: Incongruent, Congruent, Neutral\n", + "- Direction: Left, Right" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# 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", + " },]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Turning conditions into trials\n", + "\n", + "- As long as we want to keep the conditions balanced, we can just specify the number of repetitions." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_reps = 4\n", + "trials = conds * num_reps\n", + "trials" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Randomizing the order\n", + "\n", + "- We don't want the participant to know what trials will come next\n", + "- We can use the random module to help us here:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random.shuffle(trials)\n", + "trials" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Multiple trial blocks\n", + "\n", + "- We often want to give participants a break during a task.\n", + "- One way to do this is to split the trials into blocks" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[{'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}],\n", + " [{'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}],\n", + " [{'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'},\n", + " {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'},\n", + " {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='},\n", + " {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'},\n", + " {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}]]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# turn the trial list generation into a function\n", + "def gen_trials(conds, num_reps):\n", + " # warning, even though this give you a new list\n", + " # each dictionary in the list is the same one, repeated\n", + " # see the `deepcopy` in the `copy` module \n", + " trials = conds[:] * num_reps\n", + " random.shuffle(trials)\n", + " \n", + " return trials\n", + "\n", + "# Specify the number of blocks\n", + "num_blocks = 3\n", + "blocks = [gen_trials(conds, num_reps) for b in range(num_blocks)]\n", + "blocks" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## General tips\n", + "\n", + "- Give your future self a gift!\n", + " - Try to include as much information as possible in your trials to facilitate subsequent analyses (e.g., don't just have a stimulus column.)\n", + "- Try as much as possible to avoid hard-coded values.\n", + " - Make use of a configuration section in your code to set all the variables that would determine the lists that are generated." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## First bigger project!\n", + "\n", + "- We're going to be generating lists for an experiment we'll run in class.\n", + "- We'll work on this now for the rest of class, though they are due next week.\n", + "\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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/06_SMILE.ipynb b/CS4500_CompMethods/lessons/06_SMILE.ipynb new file mode 100644 index 0000000..02f0482 --- /dev/null +++ b/CS4500_CompMethods/lessons/06_SMILE.ipynb @@ -0,0 +1,763 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# SMILE!!!\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": [ + "# List Generation Sample\n", + "```python\n", + "[{'study': [{'stimulus': 'bliss',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'spirit',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'nature',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'luxury',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'kindness',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'restaurant',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'}],\n", + " 'test': [{'stimulus': 'brave',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'tender',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'spouse',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'spirit',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'kindness',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'father',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'restaurant',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'nature',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'outstanding',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'},\n", + " {'stimulus': 'bliss',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'luxury',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'TARGET'},\n", + " {'stimulus': 'acceptance',\n", + " 'pool_type': 'POS',\n", + " 'cond': 'PURE',\n", + " 'novelty': 'LURE'}]},\n", + " ...\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. To define a hierarchical state machine\n", + "2. The difference between build-time and run-time in SMILE\n", + "3. The difference between Action and Flow states in SMILE\n", + "4. How to build simple experiments in SMILE\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# What is SMILE?\n", + "\n", + "- ***State Machine Interface Library for Experiments***\n", + "- Goals in developing SMILE:\n", + " - Have millisecond accuracy in timing without difficult code\n", + " - Write experiments that run cross-platforms\n", + " - Make easy tasks easy and hard tasks possible\n", + " - Log everything, so you can recreate any experiment\n", + "\n", + "Instead of *coding* you're *smiling*!!!\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Installing SMILE\n", + "\n", + "- First you need Kivy, which is the primary dependency of SMILE:\n", + "\n", + "```bash\n", + "conda install -c conda-forge kivy\n", + "```\n", + "\n", + "- Then you can install SMILE right from the GitHub repository:\n", + "\n", + "```bash\n", + "pip install git+https://github.com/compmem/smile\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Kivy \n", + "\n", + "- [Kivy](https://kivy.org) is a cross-platform python application development library\n", + "- All core libraries are compiled to C code, so it's very fast\n", + "- It's built on OpenGL, so it can have powerful graphics\n", + "- Possible to deploy your apps on Android, iOS, Windows, OSX, and Linux from one Python code-base.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# What is a State Machine?\n", + "\n", + "- We're really talking about a *finite* state machine, because it does not have unlimited states.\n", + "- Are a common way of modeling systems in many fields/areas\n", + "- Often represented by a directed graph with nodes as states and edges as transitions\n", + " - Like a stoplight! Here's an example of what I wish stoplights were like in the US:\n", + " \n", + "![Stoplight](http://www.computing.northampton.ac.uk/~anastas/images/GamesProgramming/FiniteStateMachines/Figure%20(1).png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Hierarchical State Machines\n", + "\n", + "- A very powerful extension of a base state machine is to make it hierarchical\n", + "- This just means that states can be entire finite state machines!\n", + "- HSMs can represent almost any computer program\n", + " - e.g., most computer games are just really big and complex HSMs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# SMILE helps you build state machines\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO ] [Logger ] Record log in C:\\Users\\Carlos Rodriguez\\.kivy\\logs\\kivy_20-10-10_1.txt\n", + "[INFO ] [Kivy ] v1.11.1\n", + "[INFO ] [Kivy ] Installed at \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\__init__.py\"\n", + "[INFO ] [Python ] v3.8.3 (default, Jul 2 2020, 17:30:36) [MSC v.1916 64 bit (AMD64)]\n", + "[INFO ] [Python ] Interpreter at \"C:\\ProgramData\\Anaconda3\\python.exe\"\n", + "[INFO ] [Factory ] 184 symbols loaded\n", + "[INFO ] [Image ] Providers: img_tex, img_dds, img_sdl2, img_pil, img_gif (img_ffpyplayer ignored)\n", + "[INFO ] [Text ] Provider: sdl2\n", + "[CRITICAL] [Camera ] Unable to find any valuable Camera provider. Please enable debug logging (e.g. add -d if running from the command line, or change the log level in the config) and re-run your app to identify potential causes\n", + "picamera - ModuleNotFoundError: No module named 'picamera'\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\__init__.py\", line 59, in core_select_lib\n", + " mod = __import__(name='{2}.{0}.{1}'.format(\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\camera\\camera_picamera.py\", line 18, in \n", + " from picamera import PiCamera\n", + "\n", + "gi - ModuleNotFoundError: No module named 'gi'\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\__init__.py\", line 59, in core_select_lib\n", + " mod = __import__(name='{2}.{0}.{1}'.format(\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\camera\\camera_gi.py\", line 10, in \n", + " from gi.repository import Gst\n", + "\n", + "opencv - ModuleNotFoundError: No module named 'cv2'\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\__init__.py\", line 59, in core_select_lib\n", + " mod = __import__(name='{2}.{0}.{1}'.format(\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\camera\\camera_opencv.py\", line 48, in \n", + " import cv2\n", + "\n", + "[INFO ] [Video ] Provider: null(['video_ffmpeg', 'video_ffpyplayer'] ignored)\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\" graphics system\n", + "[INFO ] [GL ] GLEW initialization succeeded\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: 4, 6\n", + "[INFO ] [GL ] Shading version \n", + "[INFO ] [GL ] Texture max size <16384>\n", + "[INFO ] [GL ] Texture max units <32>\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", + "[INFO ] [Window ] Provider: sdl2\n", + "[INFO ] [GL ] Using the \"OpenGL\" graphics system\n", + "[INFO ] [GL ] GLEW initialization succeeded\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: 4, 6\n", + "[INFO ] [GL ] Shading version \n", + "[INFO ] [GL ] Texture max size <16384>\n", + "[INFO ] [GL ] Texture max units <32>\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", + "[INFO ] [WindowSDL ] exiting mainloop and closing.\n", + "[INFO ] [Window ] Provider: sdl2\n", + "[INFO ] [GL ] Using the \"OpenGL\" graphics system\n", + "[INFO ] [GL ] GLEW initialization succeeded\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: 4, 6\n", + "[INFO ] [GL ] Shading version \n", + "[INFO ] [GL ] Texture max size <16384>\n", + "[INFO ] [GL ] Texture max units <32>\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", + "[INFO ] [WindowSDL ] exiting mainloop and closing.\n" + ] + } + ], + "source": [ + "# load in smile states\n", + "from smile.common import *\n", + "\n", + "# create an experiment instance\n", + "exp = Experiment(show_splash=False)\n", + "\n", + "# show some text for 3 seconds\n", + "Label(text=\"Hello, World!\", duration=3)\n", + "\n", + "# run the experiment\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "![](./hello_world0001.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Running a SMILE experiment\n", + "\n", + "- While it's possible to start an experiment inside a notebook, typically you'll starte experiments from the command line:\n", + "\n", + "```bash\n", + "python exp_name.py -s subj001\n", + "```\n", + "\n", + "- The `-s` option allows you to specify a subject id, which will determine where the data are saved.\n", + "- There are other command line options, such as `-f` to turn off fullscreen mode:\n", + "\n", + "```bash\n", + "python exp_name.py -s subj001 -f\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Build-time vs. Run-time\n", + "\n", + "- The most important concept to learn with SMILE is the distinction between *building* a state machine and *running* a state machine.\n", + "- During build-time:\n", + " - Calls to the SMILE states construct the state machine\n", + " - Actual values in Python variables will not be available, yet\n", + "- During run-time:\n", + " - The state machine is initialized at the first state and runs to completion\n", + " - ***Python code in your script is not run, just the state machine you have constructed.***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# References\n", + "\n", + "- Since you can't evaluate python variables during build time, you need delay evaluations until later.\n", + "- References help make that happen:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ref(, Ref(, 3), Ref(, 4))\n", + "7\n" + ] + } + ], + "source": [ + "a = Ref.object(3)\n", + "b = Ref.object(4)\n", + "c = a + b\n", + "print(c.__repr__())\n", + "print(c.eval())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- All state attributes in SMILE are references\n", + " - Meaning you can refer to them at build time and evaluate them at run time" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Action vs. Flow\n", + "\n", + "- Another key concept in SMILE is the distinction between `Action` states and `Flow` states.\n", + "- Action states carry out some specific input or output operation and often have a `duration`.\n", + "- Flow states control the order of operations for the action states and rarely have a `duration`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Action state examples\n", + "\n", + "- `Image`: Presents an image on the screen\n", + "- `Label`: Places text on the screen\n", + "- `KeyPress`: Accepts specific user input\n", + "- `MovingDot`: Present a moving dot stimulus on the screen" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Flow state examples\n", + "\n", + "Most Flow states are parents to other states:\n", + "\n", + "- `Parallel` and `Serial`: Control sequences of states\n", + "- `If`, `Elif`, `Else`: Condition branching\n", + "- `Loop`: Provide looping over states (optionally with conditionals)\n", + "- `Meanwhile`, `UntilDone`: Run some states while others while other states are running\n", + " - These are basically convenience methods for common uses of Parallel." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Let's build a stop light!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from smile.common import * \n", + "\n", + "# set up times for each light\n", + "red_time = 2.0\n", + "yellow_time = 1.0\n", + "green_time = 2.0\n", + "\n", + "# define the colors (RGBA)\n", + "green_on = [0,1,0,1]\n", + "green_off = [0,1,0,.1]\n", + "red_on = [1,0,0,1]\n", + "red_off = [1,0,0,.1]\n", + "yellow_on = [1,1,0,1]\n", + "yellow_off = [1,1,0,.1]\n", + "\n", + "radius_prop = 1/6.\n", + "\n", + "num_loops = 5\n", + "\n", + "# make a stoplight exp\n", + "exp = Experiment(show_splash=False, debug=True)\n", + "\n", + "#Wait(1.0)\n", + "\n", + "# put up rectangle with three colored circles (low alpha for off)\n", + "with Parallel():\n", + " box = Rectangle(height=300, width=100, color='gray')\n", + " yellow_light = Ellipse(color=yellow_off,\n", + " radius=box.height*radius_prop)\n", + " red_light = Ellipse(color=red_off, \n", + " radius=box.height*radius_prop,\n", + " bottom=yellow_light.top)\n", + " green_light = Ellipse(color=green_off, \n", + " radius=box.height*radius_prop,\n", + " top=yellow_light.bottom)\n", + " \n", + " # add some labels for the lights\n", + " Label(text='GO', color='black', center=green_light.center)\n", + " Label(text='Wait', color='black', center=yellow_light.center)\n", + " Label(text='STOP', color='black', center=red_light.center)\n", + "with UntilDone():\n", + " Wait(until=box.appear_time)\n", + " with Loop(num_loops):\n", + " # make green light active\n", + " UpdateWidget(green_light, color=green_on)\n", + " Wait(green_time)\n", + " UpdateWidget(green_light, color=green_off)\n", + " \n", + " # make yellow light active\n", + " UpdateWidget(yellow_light, color=yellow_on)\n", + " Wait(yellow_time)\n", + " UpdateWidget(yellow_light, color=yellow_off)\n", + " \n", + " # make red light active\n", + " UpdateWidget(red_light, color=red_on)\n", + " Wait(red_time)\n", + " UpdateWidget(red_light, color=red_off)\n", + "\n", + "Wait(1.0)\n", + "\n", + "# run the experiment\n", + "exp.run()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Let's learn by building together!\n", + "\n", + "- Last class we wrote a list generation for a Flanker task.\n", + "- Let's write the frontend experiment to loop over those trials." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# List Gen Function" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}]\n" + ] + } + ], + "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", + "\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", + "print(trials)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Goal for each trial\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": 5, + "metadata": { + "scrolled": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "from smile.common import *\n", + "\n", + "font_size = 75\n", + "resp_keys = ['F', 'J']\n", + "ISI_dur = 0.5\n", + "ISI_jitter = 0.5\n", + "\n", + "# create the experiment\n", + "exp = Experiment(show_splash=False, fullscreen=False)\n", + "\n", + "# show the stimulus (will default to center of the screen)\n", + "with Loop(trials) as trial:\n", + " stim = Label(text=trial.current['stimulus'],\n", + " font_size=font_size)\n", + " with UntilDone():\n", + " kp = KeyPress(keys=resp_keys)\n", + " \n", + " Wait(ISI_dur, jitter=ISI_jitter)\n", + " \n", + " Log(trial.current, name='flanker',\n", + " stim_on=stim.appear_time,\n", + " resp=kp.pressed,\n", + " resp_time=kp.press_time\n", + " )\n", + " \n", + "# run the experiment\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Tips\n", + "\n", + "- When in doubt, draw it out!\n", + " - Since SMILE just creates state machines, you can draw out exactly the flow of actions and that can help you translate it into code.\n", + "- Debugging is hard in a state machine, so make use of the `Debug` state to help you by printing out various values at run time.\n", + "\n", + "- The SMILE docs need updating, but go into more detail: https://smile-docs.readthedocs.io/en/latest/index.html" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment before next class\n", + "\n", + "- Your list generation code is due next Thursday!\n", + "- Start familiarizing yourself with SMILE, since that will be front and center for the next assignment.\n", + "\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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/07_More_SMILE.ipynb b/CS4500_CompMethods/lessons/07_More_SMILE.ipynb new file mode 100644 index 0000000..6b99758 --- /dev/null +++ b/CS4500_CompMethods/lessons/07_More_SMILE.ipynb @@ -0,0 +1,718 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# More SMILE!!!\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 update SMILE to the latest version\n", + "2. How to present images\n", + "3. To visualize the DAG for an experiment\n", + "4. How to lay out visual states on the screen\n", + "5. To log information for easy analysis\n", + "6. How to write subroutines to organize your code\n", + "7. How to include mouse interaction\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\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": [ + "# Understanding Flow\n", + "\n", + "- By default, everything in SMILE proceeds sequentially (i.e., in *serial*) in the order the states are added.\n", + " - The `Serial` parent state manages starting the next child state when the previous child finishes.\n", + "- The `Parallel` state allows for multiple states to run *at the same time*.\n", + " - By default the `Parallel` state is done with all its child states are done.\n", + " - The `blocking` attribute allows ending a `Parallel` state when all non-blocking states are done.\n", + " - The `UntilDone` and `Meanwhile` parent states simply create Serial/Parallel states with specific blocking values." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## These are the same" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# load in smile states\n", + "from smile.common import *\n", + "\n", + "# create an experiment instance\n", + "exp = Experiment(show_splash=False, resolution=(1024,768))\n", + "\n", + "Label(text='Press Any Key')\n", + "with UntilDone():\n", + " KeyPress()\n", + "Wait(.5)\n", + "\n", + "with Parallel():\n", + " Label(text='Press Any Key', blocking=False)\n", + " KeyPress()\n", + "Wait(.5)\n", + "\n", + "KeyPress()\n", + "with Meanwhile():\n", + " exp.current_value = 0\n", + " with Loop():\n", + " exp.current_value = exp.current_value + 1\n", + " Label(text=Ref(str, exp.current_value), duration=1.0, \n", + " font_size=50)\n", + "# Label(text='Press Any Key')\n", + "Wait(.5)\n", + "\n", + "exp.run()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Showing Images\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# load in smile states\n", + "from smile.common import *\n", + "\n", + "# create an experiment instance\n", + "exp = Experiment(show_splash=False, resolution=(1024,768))\n", + "\n", + "# show an image until keypress\n", + "Image(source=\"../assignments/outdoor/out0099_new.jpg\")\n", + "with UntilDone():\n", + " KeyPress()\n", + "\n", + "# show a larger image until keypress (note allow_stretch)\n", + "Image(source=\"../assignments/outdoor/out0099_new.jpg\", \n", + " width=200, height=400, allow_stretch=True, keep_ratio=False)\n", + "with UntilDone():\n", + " KeyPress()\n", + "\n", + "# run the experiment\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Visualizing State Machines\n", + "\n", + "- A SMILE experiment is a directed acyclic graph (DAG)\n", + "- It's possible to visualize the full hierarchy of states" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from smile.dag import DAG\n", + "d = DAG(exp)\n", + "d.view_png()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Advanced Placement\n", + "\n", + "- All visual states have a coordinate system based on the screen.\n", + "- You can place any visual state relative to either the screen or other visual states:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# load in smile states\n", + "from smile.common import *\n", + "\n", + "# create an experiment instance\n", + "exp = Experiment(show_splash=False, resolution=(1024,768))\n", + "\n", + "# show images and labels until keypress\n", + "with Parallel():\n", + " # place images based on the screen coords\n", + " out_im = Image(source=\"../assignments/outdoor/out0091_new.jpg\",\n", + " left=exp.screen.center_x + 50)\n", + " in_im = Image(source=\"../assignments/indoor/in0021.jpg\",\n", + " right=exp.screen.center_x - 50)\n", + " \n", + " # place labels based on the images\n", + " out_txt = Label(text='Outdoor', font_size=50, \n", + " center_bottom=out_im.center_top)\n", + " in_txt = Label(text='Indoor', font_size=50, \n", + " center_bottom=in_im.center_top)\n", + "with UntilDone():\n", + " KeyPress()\n", + "\n", + "# run the experiment\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Mouse States\n", + "\n", + "- The mouse is hidden by default\n", + "- You can add in a mouse cursor with the `MouseCursor` state\n", + "- It's possible to trigger events based on mouse location" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DEBUG (file: '', line: 31) - lag=0.005011s\n", + " choice: 'B'\n", + " rt: 2.8309236710192636\n" + ] + } + ], + "source": [ + "from smile.common import *\n", + "\n", + "# set up an experiment\n", + "exp = Experiment(show_splash=False, debug=True)\n", + "\n", + "Wait(0.5)\n", + "\n", + "# display a rectangle and the mouse cursor\n", + "with Parallel():\n", + " rect = Rectangle(center_bottom=exp.screen.center_bottom,\n", + " color='white')\n", + " MouseCursor()\n", + "with UntilDone():\n", + " Wait(until=MouseWithin(rect))\n", + "\n", + "# put up two new rectangles\n", + "with Parallel():\n", + " choice_A = Rectangle(left_top=(exp.screen.left + 100, exp.screen.top - 100))\n", + " choice_B = Rectangle(right_top=(exp.screen.right - 100, exp.screen.top - 100))\n", + " mrec = Record(mouse_pos=MousePos())\n", + " MouseCursor()\n", + "with UntilDone():\n", + " mwa = MouseWithin(choice_A)\n", + " mwb = MouseWithin(choice_B)\n", + " w = Wait(until= mwa | mwb)\n", + " with If(mwa):\n", + " Debug(choice='A',\n", + " rt=w.event_time['time'] - choice_A.appear_time['time'])\n", + " with Else():\n", + " Debug(choice='B',\n", + " rt=w.event_time['time'] - choice_B.appear_time['time'])\n", + "\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Animations\n", + "\n", + "- It's possible to link the attributes of a visual state to a function.\n", + "- Here we move rectangles based on the mouse position:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from smile.common import *\n", + "\n", + "# set up an experiment\n", + "exp = Experiment(show_splash=False, debug=True)\n", + "\n", + "Wait(1.0)\n", + "\n", + "with Parallel():\n", + " rect = Rectangle(center_bottom=exp.screen.center_bottom,\n", + " color='white')\n", + " r2 = Rectangle(bottom=rect.bottom, color='purple')\n", + " r3 = Rectangle(center_top=exp.screen.center_top, color='green')\n", + " MouseCursor()\n", + "with UntilDone():\n", + " Wait(until=MouseWithin(rect))\n", + " with Meanwhile():\n", + " with Parallel():\n", + " r2.animate(center_x=lambda t, initial: MousePos()[0])\n", + " r3.animate(center_y=lambda t, initial: MousePos()[1])\n", + "\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Sliding\n", + "\n", + "- Visual States have a slide method to set new parameter values over a duration." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from smile.common import *\n", + "\n", + "# set up the experiment\n", + "exp = Experiment(show_splash=False, debug=True, resolution=(1024, 768))\n", + "\n", + "# initial wait\n", + "Wait(.25)\n", + "\n", + "# Put up a circle\n", + "circ = Ellipse(color=(jitter(0, 1),\n", + " jitter(0, 1),\n", + " jitter(0, 1)))\n", + "with UntilDone():\n", + " Wait(until=circ.appear_time)\n", + " with Loop(5):\n", + " # slide to new loc and color\n", + " exp.new_col = (jitter(0, 1),\n", + " jitter(0, 1),\n", + " jitter(0, 1))\n", + " exp.new_loc = (jitter(0, exp.screen.width),\n", + " jitter(0, exp.screen.height))\n", + " cu = circ.slide(duration=2.5,\n", + " color=exp.new_col,\n", + " center=exp.new_loc)\n", + "\n", + "Wait(.25)\n", + "\n", + "exp.run()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Animate and Slide together!" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from smile.common import *\n", + "from math import cos\n", + "\n", + "exp = Experiment(show_splash=False)\n", + "\n", + "# add a circle off the screen\n", + "ellipse = Ellipse(right=exp.screen.left,\n", + " center_y=exp.screen.center_y, width=50, height=50,\n", + " angle_start=90.0, angle_end=460.0,\n", + " color=(1.0, 1.0, 0.0), name=\"Pacman\")\n", + "\n", + "with UntilDone():\n", + " with Parallel(name=\"Pacman motion\"):\n", + " ellipse.slide(left=exp.screen.right, duration=8.0, name=\"Pacman travel\")\n", + " ellipse.animate(\n", + " angle_start=lambda t, initial: initial + (cos(t * 8) + 1) * 22.5,\n", + " angle_end=lambda t, initial: initial - (cos(t * 8) + 1) * 22.5,\n", + " duration=8.0, name=\"Pacman gobble\")\n", + "\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Logging\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." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Subroutines\n", + "\n", + "- Functions are a great way to make clean programs when you have chunks of code that are called more than once.\n", + "- The `Subroutine` decorator allows you to take chunks of state machine and call them like a function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# when defining the subroutine, you must include self as the first arg\n", + "@Subroutine\n", + "def MyTrial(self, text):\n", + " self.val = 33\n", + " Label(text=text)\n", + " with UntilDone():\n", + " KeyPress()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Let's continue learning by building together!\n", + "\n", + "- Last class we wrote a list generation and initial Flanker task.\n", + "- Let's update the frontend experiment to loop over those trials with a subroutine." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# List Gen Function" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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" + ] + } + ], + "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", + "\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", + "print(trials)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Goal for each trial\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": 21, + "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" + ] + } + ], + "source": [ + "from smile.common import *\n", + "\n", + "font_size = 75\n", + "resp_keys = ['F', 'J']\n", + "ISI_dur = 0.5\n", + "ISI_jitter = 0.5\n", + "\n", + "# create the experiment\n", + "exp = Experiment(show_splash=False, fullscreen=False,\n", + " resolution=(1024, 768))\n", + "\n", + "@Subroutine\n", + "def Trial(self, cur_trial):\n", + " self.location = (jitter(self.exp.screen.center_x-200,\n", + " 400),\n", + " jitter(self.exp.screen.center_y-100,\n", + " 200))\n", + " Debug(loc=self.location)\n", + " stim = Label(text=cur_trial['stimulus'],\n", + " font_size=font_size,\n", + " center=self.location)\n", + " with UntilDone():\n", + " Wait(until=stim.appear_time)\n", + " kp = KeyPress(keys=resp_keys)\n", + " \n", + " Wait(ISI_dur, jitter=ISI_jitter)\n", + " \n", + " Log(cur_trial, name='flanker',\n", + " stim_on=stim.appear_time,\n", + " resp=kp.pressed,\n", + " resp_time=kp.press_time,\n", + " location=self.location\n", + " )\n", + " \n", + "# show the stimulus (will default to center of the screen)\n", + "with Loop(trials) as trial:\n", + " with If(trial.i % 3 == 0):\n", + " # give someone a break\n", + " Label(text='Press any key to continue.')\n", + " with UntilDone():\n", + " KeyPress()\n", + " Trial(trial.current)\n", + "\n", + "# run the experiment\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment before next class\n", + "\n", + "- We'll be posting the SMILE assignment soon.\n", + "- Continue familiarizing yourself with SMILE, since that will be front and center for the next assignment.\n", + "\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": "3.7.6" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/07_More_SMILE_withEdits.ipynb b/CS4500_CompMethods/lessons/07_More_SMILE_withEdits.ipynb new file mode 100644 index 0000000..f990179 --- /dev/null +++ b/CS4500_CompMethods/lessons/07_More_SMILE_withEdits.ipynb @@ -0,0 +1,787 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# More SMILE!!!\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 update SMILE to the latest version\n", + "2. How to present images\n", + "3. To visualize the DAG for an experiment\n", + "4. How to lay out visual states on the screen\n", + "5. To log information for easy analysis\n", + "6. How to write subroutines to organize your code\n", + "7. How to include mouse interaction\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\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": [ + "# Understanding Flow\n", + "\n", + "- By default, everything in SMILE proceeds sequentially (i.e., in *serial*) in the order the states are added.\n", + " - The `Serial` parent state manages starting the next child state when the previous child finishes.\n", + "- The `Parallel` state allows for multiple states to run *at the same time*.\n", + " - By default the `Parallel` state is done with all its child states are done.\n", + " - The `blocking` attribute allows ending a `Parallel` state when all non-blocking states are done.\n", + " - The `UntilDone` and `Meanwhile` parent states simply create Serial/Parallel states with specific blocking values." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## These are the same" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO ] [Text ] Provider: sdl2\n", + "[CRITICAL] [Camera ] Unable to find any valuable Camera provider. Please enable debug logging (e.g. add -d if running from the command line, or change the log level in the config) and re-run your app to identify potential causes\n", + "picamera - ModuleNotFoundError: No module named 'picamera'\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\__init__.py\", line 59, in core_select_lib\n", + " mod = __import__(name='{2}.{0}.{1}'.format(\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\camera\\camera_picamera.py\", line 18, in \n", + " from picamera import PiCamera\n", + "\n", + "gi - ModuleNotFoundError: No module named 'gi'\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\__init__.py\", line 59, in core_select_lib\n", + " mod = __import__(name='{2}.{0}.{1}'.format(\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\camera\\camera_gi.py\", line 10, in \n", + " from gi.repository import Gst\n", + "\n", + "opencv - ModuleNotFoundError: No module named 'cv2'\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\__init__.py\", line 59, in core_select_lib\n", + " mod = __import__(name='{2}.{0}.{1}'.format(\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\camera\\camera_opencv.py\", line 48, in \n", + " import cv2\n", + "\n", + "[INFO ] [Video ] Provider: null(['video_ffmpeg', 'video_ffpyplayer'] ignored)\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\" graphics system\n", + "[INFO ] [GL ] GLEW initialization succeeded\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: 4, 6\n", + "[INFO ] [GL ] Shading version \n", + "[INFO ] [GL ] Texture max size <16384>\n", + "[INFO ] [GL ] Texture max units <32>\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", + "[INFO ] [Window ] Provider: sdl2\n", + "[INFO ] [GL ] Using the \"OpenGL\" graphics system\n", + "[INFO ] [GL ] GLEW initialization succeeded\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: 4, 6\n", + "[INFO ] [GL ] Shading version \n", + "[INFO ] [GL ] Texture max size <16384>\n", + "[INFO ] [GL ] Texture max units <32>\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", + "[INFO ] [WindowSDL ] exiting mainloop and closing.\n", + "[INFO ] [Window ] Provider: sdl2\n", + "[INFO ] [GL ] Using the \"OpenGL\" graphics system\n", + "[INFO ] [GL ] GLEW initialization succeeded\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: 4, 6\n", + "[INFO ] [GL ] Shading version \n", + "[INFO ] [GL ] Texture max size <16384>\n", + "[INFO ] [GL ] Texture max units <32>\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", + "[INFO ] [WindowSDL ] exiting mainloop and closing.\n" + ] + } + ], + "source": [ + "# load in smile states\n", + "from smile.common import *\n", + "\n", + "# create an experiment instance\n", + "exp = Experiment(show_splash=False, resolution=(1024,768))\n", + "\n", + "Label(text='Press Any Key')\n", + "with UntilDone():\n", + " KeyPress()\n", + "Wait(.5)\n", + "\n", + "with Parallel():\n", + " Label(text='Press Any Key', blocking=False)\n", + " KeyPress()\n", + "Wait(.5)\n", + "\n", + "KeyPress()\n", + "with Meanwhile():\n", + " Label(text='Press Any Key')\n", + "Wait(.5)\n", + "\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Showing Images\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# load in smile states\n", + "from smile.common import *\n", + "\n", + "# create an experiment instance\n", + "exp = Experiment(show_splash=False, resolution=(1024,768))\n", + "\n", + "# show an image until keypress\n", + "Image(source=\"C:/WINDOWS/system32/compsy/lessons/outdoor/out0099_new.jpg\")\n", + "with UntilDone():\n", + " KeyPress()\n", + "\n", + "# show a larger image until keypress (note allow_stretch)\n", + "Image(source=\"C:/WINDOWS/system32/compsy/lessons/outdoor/out0099_new.jpg\", \n", + " width=400, height=400, allow_stretch=True)\n", + "with UntilDone():\n", + " KeyPress()\n", + "\n", + "# run the experiment\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Visualizing State Machines\n", + "\n", + "- A SMILE experiment is a directed acyclic graph (DAG)\n", + "- It's possible to visualize the full hierarchy of states" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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wfPhwPPXUU23Od+XKFZw7dw4qlQpKpRKnTp3Cd999hytXrkAgEGD06NGYOnUqZs6cCV9fX3h6eprwVVi+Hht4VVVV2LVrF2QyGc6ePYsxY8YgMDAQ27dvx9SpU9G7d29O6xsyZAjmzp2LuXPn4u2338a9e/eQl5eH77//Hnv37kV8fDyeeOIJLFu2DCtXrsTw4cM5rddcVFZW4rvvvkNmZiaOHj2K2tpaDBkyBDNmzMDKlSsxfvx4eHt7cxZs+nJycoKTkxNmzZql8/yVK1egUChw8uRJ5OfnY8OGDbh+/TocHBzg6+sLf39/PPvssxgxYgRHlVuGHhV4DMMgMzMTiYmJSE9Ph4ODA1566SWkpqbiT3/6E9fltat3796YPXs2Zs+ejQ8++ADFxcXYt28fEhMTERcXh4CAAKxevRpz5szhulSTamxsREFBAb799lscPHgQp06dwpAhQ+Dv74/Y2FjMmjULTz75JKysevYBB05OTpg/fz7mz58P4OH7cubMGeTk5CA3Nxd/+9vfEBkZCaFQiIULF0IkEmHy5Mk9/n3pqh7zbnz77beYNGkS5s2bB7VaDalUioqKCvz97383+7BrjY+PD7Zt24bKykrs2bMH165dg7+/PyZNmoTvvvuO6/KMrqysDBs3bsTIkSMxffp0fPvtt1iwYAFyc3NRW1uLffv2Yc2aNfDy8uLlj9rKygpeXl5Ys2YN0tLSUFtbi5ycHMybNw9yuRzTpk3DqFGjsHHjRpw+fZrrcs2GxX9Tjh07hsmTJyMgIADDhg1DSUkJsrOzERgYyI6iWbJevXph2bJlOHLkCE6cOAEnJycsWrQIU6ZMwbFjx7guz6Bu3LiB7du3Y/z48Rg3bhy+/PJLrFy5EuXl5Th16hQ++OAD+Pr6wtramutSzY61tTVmzpyJDz/8EGVlZSgvL8err76KL7/8El5eXhg/fjy2b9+OGzducF0qpyw28Orq6vDqq6/C19cXdnZ2KCwsxDfffANvb2+uSzOaP/3pT0hPT0dhYSEGDRoEX19frFy5UueQCEv066+/4o033oCzszPefvttTJ06FXl5eVCpVHjnnXfg6urKdYkWx9XVFe+++y5UKhXy8vIwdepUvP3223B2dsa6devw66+/cl0iJywy8L7++mt4enoiMzMTaWlpOHToEHx8fLguy2R8fHxw+PBh7Nu3D4cOHYKnpyfS09O5LqvLlEolli1bBnd3d6Snp+Odd95BVVUVdu7cialTp3JdXo8xdepU7Ny5E5cuXcKmTZvw9ddfw93dHcHBwexhWXxhUYF39+5dvPHGG1iyZAmWLl2KX375BUuXLuW6LM4EBgbi7NmzWLJkCRYvXox169bh/v37XJfVoerqaqxatQpPPvkkTp8+jX379kGlUmHdunWwtbXlurwey87ODlFRUSgvL4dMJsOpU6cwduxYRERE4PLly1yXZxIWE3hqtRrz5s3D559/DplMhn//+98GPzjUEg0YMADJycnYs2cPdu3ahXnz5rEH2ZqbxsZGfPLJJ/Dw8MD3338PiUSCsrIyLF26lJcDD1yxtrZGYGAgSktLkZycjIyMDHh4eGD79u1obGzkujyjsohv2W+//YZZs2ahoqIChYWFeOGFF7guyeyEhISgoKAA58+fx9NPP42amhquS9Lxyy+/YMaMGdiwYQO7lfHKK69Q0HHIxsYGr776KpRKJdatW4eoqCj4+vri7NmzXJdmNGb/bdNoNJg3bx4AIC8vDx4eHhxXZL7Gjh2LY8eO4e7du3jmmWeg0Wi4LgkAsG/fPkyYMAEPHjxAcXExNm/ezPmB3+R/+vTpg82bN6O4uBj37t3DhAkTkJaWxnVZRmHWgXfv3j0sWbIE9+/fR05ODh577DGuSzJ7w4cPx5EjR3D79m0sWbIE9+7d46wWhmEQExOD4OBgvP7668jLy6PT5czYuHHjkJ+fj9WrVyMoKAixsbFgGIbrsgzKrAMvNjYWZWVlkMvldFWRLrC3t0dGRgbKysqwefNmTmpgGAZhYWFISEjAF198gQ8//JCOn7MANjY22LZtG3bv3g2xWIxVq1b1qNAz21PLCgsL8fHHHyMzM5OOw+oGV1dXpKWlYe7cuViyZAkmTJhg0vY3bNgAqVSKH374ATNmzDBp20R/f/nLXzBq1CjMnz8fjz76KLZu3cp1SQZhllt4DQ0NCA0NxerVqzFz5kyuy7FYs2fPRnh4OEJDQ9mLVZpCcnIy/vGPf2D//v0UdhbM19cXMpkMH330ET799FOuyzEIswy8/fv349KlS4iLizPI+gQCQasPPtiyZQsuXryIr776yiTtVVVVITo6GomJiVi4cKHB19/eZ1lZWdni+ezsbKO2GxAQAIlEgtraWoO0Y25EIhF27tyJ6OhoVFVVcV2O3swy8LZt24bVq1frXGFWHwzD6FzBWK1W96h+ifbY2tpi1apV+PDDD03SXmRkJHx9fbFixQqjrL+9z9LFxYWdlpWVBbVa3eI6hPq02/RQH4ZhwDAMduzYgcrKSjg6OkKpVBqkLXOzcuVKTJs2Da+99hrXpejN7AKvsLAQJ0+eRFhYmEHX2/QIfr4dzR8WFoaSkhKcOHHCqO3k5+fju+++w44dO4zaTnuf5aeffgqFQgE/Pz+Df86tXY3axcUFr7/+OgDg448/Nmh75mT79u349ttvUVBQwHUpejG7wDt8+DA8PDwwcuRIo7dVW1sLmUyGgIAAAIBcLodAIEBkZCQqKysBADKZrMVzwMPjAyUSCbtrExsb22K3Jjs7GwEBARAIBEhISGh1t6e2thYJCQns7pGhdsGaGjNmDFxdXXHo0CGDr7upXbt24ZlnnsGoUaOM2k5ramtrIZFIsHz5cgiFwjbnaf5ea/+tfSQkJLDzN53W9LNvThuESUlJbDtyuRwBAQHQaDSIjIxEbGxsu3U0pZ2m3VVu3v3S0XRjGDNmDObMmYNdu3YZvS2jYvQQEhLChISEdGpeAExqamqH8/n5+TERERH6lNVuDU1fskgkYp9TKBQMwzBMfn4+A4CJiIhg8vPzGYZhmIqKCvY5rYiICAYAU1NT0+r09PR0BgC7DqlUyralraGmpoYRiUSMVCplGIZhsrKydGoxpPDwcGbOnDldXi41NZXpzNfkzp07zIABAzr1GRtC0/fx3LlzjFgsbnf+9t7rpp95cyKRiKmpqWm1XS21Wq2zfNPvVX5+PqNQKNhpHX3mYrGYqaioYNcbExOj015H040pJSWFGThwIHP37t0O5+3s772z3y+G6VretFmXPgsbI/Dc3NyY+Ph4fcpqt4bmb253n4uJidH5gTSf3tY6mv4wtSHYfJ6YmJguvrKOxcXFMW5ubl1errNfyJKSEgYAo1KpulNel2nf3/T0dEYkEnU4f0fvtVgsZgCwYcIwDKNQKNhgat6uNqCaho72j1vT+dRqdZfq0P4R1aqpqWnxvWpvujGdO3eu03+QzTXwzG6X9rfffrOIMyri4uKQmJiIyspKnd0grYiIiFaXi46OZv9/7969ANBitDE+Pt7g9T722GO4cuWKwderpb3ahqnvJeHi4gK5XN5ql0JTHb3X/v7+AKCz25+ZmYlp06a1uj5vb28IBAL2gHiFQoEpU6a0mK95P2JHdURERMDR0REymQwajQYODg46A2wdTTcm7X1VqqurTdKeMZhd4AGwmBFUiUSCNWvWQCQStZimDTyZTAbg4Z3TAEAsFrPzyOVyAP8b8Wv6IJ0jFApRUVGB0tJShIaGthl6Hb3XQqEQERERCA8PZ+8lq1Kp4OLi0ur6mi4fFxfXZr9hV+tYu3YtRCIRgoODYWdn1+KPaUfTTcGSD+kyu8Az9paIochkMoSHh2PHjh1wd3dvMV0oFCI9PR3V1dXsoIZUKkVUVFSLeU1xOMNvv/1m1K0v7V9/Lj47FxcXpKSkQCgUIjQ0lP3j0pr23mvtH6mMjAzk5ubi5ZdfNnitHdWhvRiqQqFAREQEoqOjdUKto+nGpN2yGzZsmEnaMwazC7wRI0ZYxAGOwcHBANDmFoBcLsfMmTMRFRUFhmGQnp6OoKAgnXmSk5MBACkpKeyVTbQjeIZ26dIlPP744wZfr9bYsWMxcOBA5OfnG62N9tja2iIuLg4ikQje3t4tAqUz77V2Ky84OBgSiaTVXVR9dVSHQCCARqOBUChEYmIiFAqFTjdIR9ONqaCgAIMGDcITTzxhkvaMwewCz8/PzyiHZjS9VFLTL1p7z2n/v7XntLuxlZWVOj8u7fSAgADY2dm1ODI/MjKSnee5554D8LD/Rjuvo6MjAgMDDfSq/yc7O9tgB+G2pk+fPli2bBlSUlKM1oZWa5+llvb4zejoaJ0tvc6+19qtuta6KZq21V5/YXvTOlOHWCxmD4MZPHiwTjdIZ6YbS0pKCpYtW2bZN8fSZ8TDGKO0RUVFDADm/Pnz+pTWou3OPFqbt63nFAoFO7pWU1PDjtpqR/kUCoXO4QlNH01HdysqKthRvqbLG5JKpWIAMCdOnOjysl0ZRcvPz2esrKyYX3/9tcvtdFZ7n11b07U6+16LRCLm3LlzXWq3rXlbG0Furw78dxRWO2rc/HCbjqYbi0qlYqysrJiCgoJOzd/Z3zvvD0thGIYZP348s3HjRn1K49y5c+da/UFph/ZN6c0332QmTJjQrWW78oVkGIYJCAhgFixY0K22zIFarTbacaCWbO7cuUxAQECn5zfXwDO7XVrg4aWFkpKSzPbeDB2RyWRwd3dvtX/P0dERUqnUZLWo1WokJydjw4YNJmnvX//6F/Ly8iz2iPy0tDSjdClYsk8//RSFhYVITEzkuhS9mWXgLV26FC4uLnj77be5LqVb9u7dC4lE0uJ0JKVSibS0tBaDF8YUExODUaNG4fnnnzdJe87OzkhISMDq1avZQzDMXWxsrM4pZMbs67Q06enpiIiIwCeffGLRo7NaZhl41tbW+Oyzz/Dvf/8bP/74I9fldFlKSgoGDhyIrVu36pxrW1VVZfCLIrTnxx9/RHJyMj799FOT3ixn5cqVWLduHYKCgnD06FGTtdtd2i3x5ORkg12SrCfIyclBcHAw3nzzTaMeomNS+uwPG6sPT2v9+vWMvb09U15e3p3yeK28vJyxt7dn/va3v+m1nq724Wk1NjYy4eHhTJ8+fZgvvvhCrxqI6X3++edMnz59mIiICKaxsbHLy3f29059eE3ExcXBy8sLCxYsQF1dHdflWIy6ujosWLAAXl5enN3TQiAQICkpCevXr8crr7yC6OhoNDQ0cFIL6byGhgasW7cOK1aswIYNG7Bz506LPrOiObMOvN69e+PAgQN45JFH4OfnZ9Hn8JnKpUuXMHv2bDzyyCM4cOAApzfOEQgEeO+99yCTyZCUlIQpU6bg1KlTnNVD2qdQKDB58mQkJycjLS0Nmzdv7lFhB5h54AEPj6A/fPgwbGxsMH36dPzyyy9cl2S2zpw5g+nTp+ORRx5BZmam2Vzo9IUXXsCJEyfQt29f+Pj4IDY2Fnfu3OG6LPJft2/fRmxsLCZNmoR+/fqhpKQES5cu5bosozD7wAMeXoEjJycHo0ePxpQpU9grTpD/SU1NxZQpU+Du7o4jR460enVeLnl4eCA3NxcJCQn45JNP4OHhgV27dtFuLocaGhrw6aefwsPDA5988gk+/vhj5OTktHpueE9hEYEHAIMGDcL333+PlStX4qWXXkJYWBhu3LjBdVmcq6+vx4oVK7B8+XKsWrUKGRkZBrsXiKFZWVnh9ddfh1KpxMKFCxEZGQkvLy+kpaWZ9K5qfPfgwQPs27cPTz31FF577TUsWrQISqUSr732mklH87lgUa+ud+/e+Oijj/DNN9/g66+/hqenJ9LS0rguizP79u2Dp6cnvv32W8jlcojFYos4z/Gxxx5DYmIifv75Z4wfPx7BwcFwdXXFtm3bdG7QQwxLrVZj27ZtGDNmDF588UX4+Pjg559/xs6dOy3iGpSGYFGBpyUSifDLL0e/i1wAACAASURBVL9g/vz5CAoKwjPPPIOioiKuyzKZoqIi+Pv7Izg4GM8++yx+/vlnPPvss1yX1WWurq7Yu3cvVCoVlixZgi1btsDZ2RmrVq1CXl4eXRfQABiGQV5eHlatWgVnZ2ds2bIFzz//PMrLy7Fnzx6MGTOG6xJNyiIDDwCGDh2KXbt24aeffsKNGzcwadIkiEQiFBcXc12a0RQXF0MkEmHSpEm4desWfvrpJ0gkEgwdOpTr0vQyatQoJCQk4NKlS/jggw9QVFSE6dOnw83NDZs2bcK5c+e4LtHinD17Fps2bYKrqyumT5+OoqIifPDBB7h06RISEhI4udGSObDYwNOaNm0aCgoKcPDgQdTU1GDixImYNWsWZDIZ7t+/z3V5ert37x6kUil8fX0xceJE1NXVISMjA3l5eW1eftxSDRw4EGvWrEFJSQnOnDmDF154AV988QU8PT3xxBNPYP369cjJyaGBjlY0NDTgxx9/xPr16zF27FiMHTsW//nPfxAUFIQzZ86gpKQEa9asMdv+XVOx+MDTWrhwIQoLC5GVlQV7e3v85S9/gYuLC958800UFhZyXV6XMAyD48ePIzo6Gi4uLnj55Zfx2GOPITs7GwUFBZg/fz7XJRrdE088gffffx8XLlxAQUEBlixZgszMTMyePRsODg4IDAzE9u3boVAoeDng8eDBA5w8eRL//Oc/ERgYCHt7e/j5+SEzMxNLly7F8ePHceHCBWzZssWiL9hpaDZcF2Bofn5+7EHKn332GWQyGcRiMUaOHIkXXngB8+bNw7Rp09C3b1+uS9Vx584dHDt2DBkZGfjqq69w8eJFPPXUU1izZg1WrlzJm07l5qysrDB58mRMnjwZ8fHxqKqqwsGDB5GVlYX3338fV65cga2tLWbMmIFJkyZhwoQJ8Pb2Zi8531NUV1ejpKQEJ0+exPHjx3Hs2DFoNBo4OTlh5syZ+PDDD7Fw4UI4OztzXapZ63GBpzV8+HDExsYiNjYWZ86cQVpaGjuS2bdvX8yYMQOzZs3ChAkT4OPjg0cffdSk9V29ehUnTpxAUVERjh49iqNHj+Lu3bvw9vbGK6+8gsDAQPrL3ArtoMaqVasAPLwCzU8//YTc3FykpqZi8+bNaGxshL29Pby9veHt7Q03Nzf2Ye5BWF1dDaVSCZVKhfLycigUCigUCtTV1cHKygru7u6YPHkyPv74Y8yYMQNubm5cl2xRemzgNfXkk09i8+bN2Lx5M+rq6pCVlYXMzEzs3r0bMTExYBgGI0eOxLhx4+Dq6sr+OB5//HE4OTlhwIAB3Wr3jz/+wJUrV3Dx4kWUl5ezX+KysjJcvHgRAoEArq6umDVrFj7//HP4+fnB3t7ewK++Z3N3d4e7uztWrFgB4OF7furUKSgUCpSUlODIkSNITk5mL8/er18/uLu7Y8SIERg+fDgee+wxODs7w8nJCSNGjICdnR1sbW0xaNAgg9ZZX18PjUYDtVqNS5cu4cqVK6iqqsJvv/2G6upqVFRUQKVS4datWwAenmHk5uYGb29vLF68GN7e3hg3bly3v4vkIV4EXlP29vYICgpir0mnVqtRUlKC4uJilJaW4qeffsLu3btx7do1dpm+ffvC3t4e9vb26NevH/r27QsrKyv21C2NRoPGxkbcvn0bt27dwtWrV1FXV6dz+tSQIUPg7u4ONzc3rF69GhMmTMCECRPM5vSvnmLAgAGYNm1aiwGd2tpaqFQqnDt3DiqVCpWVlTh79ix+/PFHVFdXtziIXXvP2cGDB8PW1pb9vLUH5vbr1w99+vQBANy9e5cNqsbGRvb7oNFocP36dajV6haH2AwcOJAN2uHDh2PRokVwc3ODu7s7XF1dze5MmZ6Cd4HXnJ2dHdvv19S1a9dw8eJF1NTUoK6ujg2xmzdv4u7du2hoaGB/JM7OzrCxsUGfPn3Qv39/2NvbY+jQobC3t4ejoyNGjhyJIUOGcPHyyH85ODjAwcGhzZHtmzdv4tKlS7h+/Tp7X9qmgdXY2KhzUPQff/zBHgUwYMAAnVtgDh48mA1IbWBqH4MHD8aIESPQv39/475g0ireB15bhgwZQiHFI/3794enpyfXZRAj6zGHpRBCSEco8AghvEGBRwjhDQo8QghvUOARQniDAo8QwhsUeIQQ3qDAI4TwBh14TNrVr18/AOhxt+sjxqf97pgTCjzSrkWLFuGrr77i5TXnSPdZW1tj0aJFXJfRAgUeaZeNjQ2WLFnCdRmEGAT14RFCeIMCjxDCGxR4hBDeoMAjhPAGBR4hhDco8Ein9IR7/BJCgUc6dOLECfTu3RubNm3iuhRC9GLS4/D279+PXr16mbJJYgBbt24FAMTFxeHy5cuYN28eAKCmpgaOjo5clkYs3P79+03anskCr3fv3vj666/x9ddfm6pJYgS7du3Crl27uC6D9CC9e/c2WVsmC7y7d++aqiliQDNmzMDx48fR0NDAPmdtbY0hQ4Zg586dWLp0KYfVEdI1dGoZadP333+PY8eOtXi+sbER9fX1ePTRRzmoipDuo0EL0iqGYbBhwwZYW1u3Ou3+/fsQiUQoLi7moDpCuocCj7Tqq6++QllZWZtXSWlsbMSdO3cwceJE5Ofnm7g6QrqHAo+00NDQgI0bN3Z4DTzt9NzcXFOURYjeqA+PtPDFF1/gwoULaGxsbHV6r1690NDQgAULFiAqKgqzZs0ycYWEdA8FHtFx9+5dbNq0CQzDtJhmbW2NPn36ICwsDP/v//0/jB49moMKCek+CjyiIykpCTU1NWzgWVtbo7GxEc7Ozli3bh1WrFiBQYMGcVwlId1DgUdYN27cwN/+9jc8ePAAvXr1wv379zFlyhRERUXhueeeg5UVdfkSy2aywHv77behUqlM1RzphtLSUty8eRMAMGzYMLi5uWHw4MGQSqWQSqUcV0d6KldXV7z//vsmactkgac9HzMwMNBUTZIuGjZsGG7fvg1vb2/07duX63IID2jPpe1xgQcAqampCAkJMWWThBAztnfvXrz44osma486ZQghvEGBRwjhDQo8QghvUOARsyQQCFo9ta2t5w2xbtLzUeDxkPYHr30EBARAIpGgtraW69IIMSoKPAK5XI7w8HCEhoZS6JEejQKPxxiGAcMwUKvVEIvFkMvl+Oabb7guixCjocAjsLW1RWhoKAAgPDxcZ5pEIkFkZCS7+xsZGYns7OxW19O0b0wul7foK+vKurqjoKAAsbGx7PppN520wOghJCSECQkJ6dS8AJjU1FR9miMGAoBp7aNv7Xntc80fWVlZbS4fExOjM68+6+rs81KptNV1i0QiRq1Wd2odxPRSU1M7/Vl0JW/aQlt4BADYLaGIiAid55n/7vZqH1lZWQA6vr1eRUUFu4y+6+pIaWkpgoODERMTw17phWEYSKVSyOVyZGRk6LV+0nNQ4BHU1tZi+/btANDhxTz9/PwAPLyMVFvi4uLg4uLSYbudWVdnFBYWAgCio6Ph4ODAPh8UFAQAyMnJ0Wv9pOegy0PxWPNj0UQiERsSWhqNBrm5uVAqlVCpVHqFkyHX1ZS239HOzq7V6UlJSUhMTDRIW8SyUeARiEQihISEYMGCBTrPK5VKeHh4GKQNQ66LkO6iwOMxppXLuDeVkpICAEhOTsa8efPY3dTunKVgyHW1paPXQwj14ZE2xcfHAwDCwsI61SdnqnU1JxaLATwcvCCkPRR4pE0ikQjAw91R4GEfnFwu53xdzfn7+wMAYmNjUVBQoDOttLQUCQkJBmmHWD4KPNKmsLAwAICHhwcEAgHs7OwgkUg4X1dzQqGQPQRl6tSpOucJe3t7Izo62iDtEMtHgUfaJBKJkJ6ezv5bKpWyfXFcrqs1QUFBUCgU7O6ttk2pVIqKigqDtUMsGw1a8FBXOvdFIlGL+dtavqP1GmJd7bUhFAohFAoRFRXVbh00uMFftIVHCOENCjxCCG9Q4BFCeIMCjxDCGxR4hBDeoMAjhPAGBR4hhDco8AghvEGBRwjhDQo8QghvUOARQniDAo8QwhsUeIQQ3qDAI4TwBgUeIYQ3KPAIIbxBgUcI4Q0KPEIIb1DgEUJ4g+5pQdrV0NCA9PR0PHjwgOtSiAWxtrZGQEAAbGzMK2LMqxpidr799ls8//zzXJdBLND//d//YfHixVyXoYMCj7Tr1q1bAOhOX6RrBAIB+90xJ9SHRwjhDQo8QghvUOARQniDAo8QwhsUeISYKYFAAIFAwHUZPQoFHumxsrOzERsbywaHQCBAQkIC5HI5lEqlUdumsDJPdFgK6XE0Gg02btyIpKSkFtOio6PZ/6dDbfiHAo/0OGlpaUhKSkJycjKee+45ODg4sNMqKytx+fJlHDx40Kg1UJiaJ9qlJT1OeHg4ACAsLEwn7ADAxcUFU6ZMQVxcXKvLFhQU6OwGSyQS1NbWtpiv6S6rXC5vsQvb1i6tRCJBZGQkOz0yMhLZ2dndfq2kayjwSI+l0Wi6NL9MJsPUqVMRHx/PPhceHo7Q0NA21xUbG4uAgIBOtxEeHq6zq52UlIQ5c+ZQ6JkIBR7pcaRSKQBg48aNKC0tbXULrbnS0lIEBwcjJiYGNTU1YBgGDMNAKpVCLpcjIyOjzWUrKirY+TuinU/7yMrKAgDs37+/k6+O6IMCj/Q4QUFBEIvFSEpKgre3NxwdHSEQCBAbGwuZTIbS0tIWyxQWFgJ4OKjRdDc4KCgIAJCTk9NqW3FxcXBxcel2rX5+fgDQ6gALMTwatCA9UlRUFJYvX47Tp0/j5MmTUKlUOruqERER2Lx5Mxtu2n4/Ozu7VteXlJSExMREvevSaDTIzc2FUqmESqWioDMxCjzSYzk4OMDPz4/dikpMTIRSqURKSgri4+Ph6uqKqKgok9WjVCrh4eFhsvZIS7RLS3jF3d2dHaFtekyeVvM+tqYPfaWkpAAAkpOTu9TvRwyHAo/wlkgkYv9fLBYDQKv9e4ai3aUOCwvTq9+PdB8FHulxIiMjIZfLUVlZ2eJwEqVSidjYWAAPg0fL398fwMPDTAoKCnSWKS0tRUJCgt51aQNWe1qbRqOBXC7Xe72k86gPj/Q4SUlJHQ4GxMTE6GzhCYVCSKVSBAcHtxlC+vb3hYWFQS6X6/TjNa2BGB9t4ZEep6amBllZWRCLxS0CRSwWQ6FQtHqmRVBQEBQKBbt7CzwMJKlUioqKCr3rEolESE9PZ/8tlUrZfj1iGrSFR3qcpqOzXd0qEwqFEAqFHS7X0WBDW9NFIlGLaW3NSwMahkdbeIQQ3qDAI4TwBgUeIYQ3KPAIIbxBgUcI4Q0KPEIIb1DgEUJ4gwKPEMIbFHiEEN6gwCOE8AYFHiGENyjwCCG8QYFHCOENCjxCCG9Q4BFCeIMCjxDCGxR4hBDeoMAjhPAGBR4hhDfonhZNPHjwAJWVlVCpVFCpVLh48SJqampw9epV1NXV4erVq7hx4wYaGhrQ0NCAGzduAAAGDhwIGxsb2NjYYODAgRg6dCjs7e1hb28PBwcHjBw5Eq6urnB1dcXjjz8OKyv6O2MOampqcOXKFVRVVaGmpgZVVVW4du0aNBqNzuP69eu4f/8+/vjjD3ZZ7fcAAPu5aw0YMAC9evXC4MGDYWtryz7s7OwwePBgODs7w9HREc7OznBycoKjo6PJXztf8TbwHjx4gFOnTqG4uBjFxcUoKirCmTNncO/ePQDA0KFDMXLkSDg4OMDe3h6enp6wt7dnv8zW1tYYNGgQAKC+vh4PHjzAvXv3cPPmTdTV1aGurg61tbU4deoULl68iGvXrgEA+vTpg6eeegoTJkzAxIkT4ePjAy8vL1hbW3P2XvRUDMPgwoULKC8vR3l5OZRKJZRKJVQqFSorK3H//n123oEDB8LZ2VknpNzd3WFra4vBgwfrfN4A0L9/f/Tu3RsA2M9dS/t9uH79OtRqNTQaDWpqaqBUKnH9+nVUVVWxfywBoHfv3hgxYgTc3Nzg5uYGDw8PuLq6ws3NDaNGjYJAIDDBu8UPvAq8s2fPIisrC5mZmfjxxx+hVqthZ2cHHx8fzJ07F9HR0ewXbfDgwQZt+9q1a+yW46lTp1BUVIS0tDS2hqeffhr+/v7w9/eHu7u7Qdvmg4aGBpw6dQoKhYJ9lJaWor6+HsDDO5lpg2T27NkYMWIERowYwW5p9e/f36T13rx5E5cuXUJtbS0uXbqEyspKlJeX4+TJk9i3bx9qa2sBAIMGDYJQKIS3tze8vb0xfvx4eHl5wcaGVz9dg+nx71pZWRnS0tKQlpYGpVIJBwcHzJkzBwkJCfD19YWrq6tJ/oIOGTIEkyZNwqRJkxASEgLg4RaISqVCbm4uMjMz8e677+K1116Dp6cnAgMDsWzZMjz55JNGr80S3b59G4WFhcjNzcXRo0eRl5eHmzdv6gTEK6+8gnHjxsHd3V1n68wc9O/fH56envD09Gx1en19PZRKpU6I7969Gzdu3MCAAQMwdepU+Pr6YtasWZg4cSIeeeQRE78Cy9QjA0+j0eCLL75AcnIyzpw5g9GjR+OFF15AYGAgxo8fbza7CAKBgN2NWblyJRiGQUlJCdLS0rBnzx7ExcXBy8sL4eHhWL58OWxtbbkumVPnzp3DwYMHcfDgQRw7dgx3796Fp6cnfH19sXz5ckydOhVjxowxm89XH4MGDYKPjw98fHzY5xobG6FSqXD8+HHk5uYiNTUVmzZtQp8+fTB9+nQsWrQIzz77LO0htIfRQ0hICBMSEtKpeQEwqamp+jTXoXPnzjHh4eFM//79mYEDBzKRkZFMYWGhUds0puPHjzMRERHMgAEDmAEDBjARERFMeXm5SWtITU1l9PyadFtjYyNz7Ngx5o033mBcXV0ZAMxjjz3GhIaGMmlpacyVK1c4qcucXLlyhUlLS2NCQ0MZJycnBgDj6urKrF27lsnLy2MaGxs5qauzv/eufL+6kjdt6RHDhefPn8fLL7+MJ598Ejk5Odi2bRuqq6uxc+dOTJw4kevyum3SpElITExEdXU1tm7diuzsbIwdOxavvvoqzp8/z3V5RlNeXo533nkHbm5umD59OnJycrB8+XIUFxejuroaEokEgYGBNLoJwNHREYGBgZBIJLh8+TKKiorw0ksv4ciRI5g2bRrc3NzwzjvvoLy8nOtSzYJFB159fT3Wrl0LDw8PFBQU4LPPPsPPP/+MyMhIncMELN2gQYOwZs0a/Pzzz/jss8+Ql5cHDw8PrF27Vme0z5I9ePAAX375JWbOnAl3d3fs3r0bgYGBOH36NEpKSrBp0yZMmDChR+yuGotAIICPjw/eeecdnDx5EmVlZVi6dCl2794Nd3d3zJw5E19++SUePHjAdamcsdjA27dvHzw9PbFnzx4kJibizJkzWL58eY8+xs3a2hrLly/HmTNnsHPnTqSkpMDT0xP79+/nurRuU6vV+Pvf/47Ro0cjKCgIQ4cORVZWFi5cuICtW7fSoI0ennrqKXzwwQe4cOECsrKyMHToUAQFBWHMmDHYtm0bNBoN1yWanMWlwx9//IGXXnoJISEhWLRoEX755ReEhobyapjexsYGYWFhOHv2LBYuXIhly5bh5Zdf1jkw1tzdvn2bDbqtW7ciMDAQKpUKBw4cgJ+fX4/+w2VqVlZW8PPzw4EDB1BeXo7nn38e77//Pht8t2/f5rpEk7Gob1VZWRkmTJiAH374ARkZGUhOTsbQoUO5LoszQ4cOhUQiwffff4+MjAxMnDgRP//8M9dltauxsRESiQSurq6Ii4vDa6+9hosXL0IsFmPkyJFcl9fjjRo1CgkJCbhw4QJWrVqF9957D25ubti1axcejjX0bBYTeDk5OZg5cyaGDx+O0tJSzJ07l+uSzMbcuXNx8uRJODk5YcaMGcjLy+O6pFYplUrMmjULq1evxpIlS6BSqRAXF8f7w224YGdnhy1btqC8vByLFy9GREQEZs+eDaVSyXVpRmURgffdd99hwYIFEIlEOHToEJycnLguyewMHz4chw4dwoIFC/DMM88gIyOD65JYDMNg27Zt8Pb2xq1bt1BUVITt27fTKKsZcHJywo4dO1BYWIj6+np4e3vjo48+6rFbe2YfeMePH0dgYCBWr16NL774Ar169eK6JLPVu3dv7NmzB6+++iqWLl2K48ePc10Sbt68iWXLliE2NhabNm3C8ePH4e3tzXVZpJnx48ejqKgIMTExeOuttxAUFIRbt25xXZbBmXVP/4ULFyASibB06VKIxWKuy7EIAoEA27dvh1qthkgkQmFhIWd9Y5cvX8aiRYtQXV2NrKwsTJ8+nZM6SOfY2Njg7bffxsyZM/H888/D19cXcrkcw4YN47o0gzHbLTyGYfDqq6/Cy8sLEomE63IsikAgwGeffQYvLy+EhoZysntSV1eHOXPmgGEYFBUVUdhZkBkzZqCwsBANDQ145plnUFdXx3VJBmO2gbdz506cOXMGUqmUvQwP6bzevXsjNTUVJ0+eRHJysknb1mg0ePbZZ9GrVy/8+OOPcHFxMWn7RH+PP/44cnJyYG1tDZFIxF51xtKZZeBdvXoVb7/9Nv75z3/CwcGB63IslpOTEz7++GNs3LgRv//+u8nafeONN3D9+nUcPnyYRmAtmJ2dHTIyMvD7779j7dq1XJdjEGYZeDt27MCIESMQFBRkkPUJBIJWH3ywfPlyDBs2DDt37jRJe4cPH4ZMJsPXX39tlNH09j7LysrKFs9nZ2cbtd2AgABIJBL2+nU9zfDhw3HgwAGkpqbihx9+4LocvZld4N26dQv/+te/sH79eoOFEsMwUKvV7L/VanWPHXZvTiAQIDo6Gv/85z+NfkT9nTt3EBkZibfeestop4S191m6uLiw07KysqBWq+Hn52ewdmtqanT+zTAMduzYgcrKSjg6OvbYY9i8vLzw1ltvISIiAnfv3uW6HL2YXeBlZWVBo9Fg8eLFBl1v010rvu1mLVmyBGq12mBbO205cOAAfv/9d0RHRxu1nfY+y08//RQKhQJ+fn4G/5xb615xcXHB66+/DgD4+OOPDdqeOYmOjsbVq1dx4MABrkvRi9kF3qFDhzBlyhSTXKG2trYWMpkMAQEBAAC5XA6BQIDIyEhUVlYCAGQyWYvngIcd8xKJhN21iY2NbbFbk52djYCAAAgEAiQkJLS621NbW4uEhAR298gYoWRra4spU6bg0KFDBl93U7t27UJwcDD69etn1HZaU1tbC4lEguXLl0MoFLY5T/P3Wvtv7SMhIYGdv+m0pp99c9ogTEpKYtuRy+UICAiARqNBZGQkYmNj262jKe007a5y8z2djqYbQ//+/REUFITPPvvM6G0ZlT4X0zPGBUAnTpzIvPnmm/qU1W4NTV+ySCRin1MoFAzDMEx+fj4DgImIiGDy8/MZhmGYiooK9jmtiIgIBgBTU1PT6vT09HQGALsOqVTKtqWtoaamhhGJRIxUKmUYhmGysrJ0ajGkdevWMZMmTerycp29QOPVq1cZgUDA/PDDD90pr8uavo/nzp1jxGJxu/O39143/cybE4lETE1NTavtaqnVap3lm36v8vPzGYVCwU7r6DMXi8VMRUUFu96YmBid9jqabkyHDh1irKysmGvXrnU4b2d/76a+AKjZBZ6zszOTkJCgT1nt1tD8ze3uczExMTo/kObT21pH0x+mNgSbzxMTE9PFV9axhIQEZsSIEV1errNfyJycHAaAya5CrH1/09PTGZFI1OH8Hb3XYrGYAcCGCcMwjEKhYIOpebvagGoaOto/bk3nU6vVXapD+0dUq6ampsX3qr3pxnT58mUGAHP06NEO5zXXwDO7Xdq6ujqLuAJKXFwcEhMTUVlZqbMbpBUREdHqck37t/bu3QsALUYb4+PjDV7v0KFDjTqSqL0QqZ2dndHaaI2LiwvkcnmrXQpNdfRe+/v7A4DObn9mZiamTZvW6vq8vb0hEAjY16tQKDBlypQW8zXvR+yojoiICDg6OkImk0Gj0cDBwUFngK2j6cY0ZMgQALDo6+iZXeANGjTIYq7iK5FIsGbNGohEohbTtIEnk8kAAKWlpQCgc4qcXC4H8L8Rv6YPQ6uvrzfqYI32HGdTX1tNKBSioqICpaWlCA0NbTP0OnqvhUIhIiIiEB4ezt6AW6VStXnQdNPl4+Li2uw37Goda9euhUgkQnBwMOzs7Fr8Me1oujFp773bp08fk7VpaGYXeI6OjhZxTJNMJkN4eDh27NjR6l2ihEIh0tPTUV1dzQ5qSKVSREVFtZjXFIcz1NbWGvXqJGPGjAEAqFQqo7XRFhcXF6SkpEAoFCI0NJT949Ka9t5r7R+pjIwM5Obm4uWXXzZ4rR3V4e7ujvT0dCgUCkRERCA6Olon1Dqabky//vorAMDV1dUk7RmD2QXeE088YfYXsQSA4OBgAGhzC0Aul2PmzJmIiooCwzBIT09vcSC19pSvlJQUdjdBO4JnaGfOnMETTzxh8PVqjR49GqNHj0ZmZqbR2miPra0t4uLiIBKJ4O3t3SJQOvNea7fygoODIZFIWt1F1VdHdQgEAmg0GgiFQiQmJkKhUOh0g3Q03Zh++OEHuLm5WfaFWvXpADTGoIVEImGGDBnCNDQ06FNaC9qRNDTpSNZ2+Lb1nLZzuLXntCNxFRUVzLlz51pM1/67+SMiIqLV9TZ9NO04N4SGhgbGzs6O2bVrV5eX7Uqn8pYtWxh3d3ej3xqwtc+yKQCMSCTSGe3u7HutHbFNTk5ut92mAwfNNW2rvWmt1YH/DmBo/11RUaEz0NXRdGNpbGxkXF1dmffff79T83f29877UdqqqirGxsaGyc3N1ae0Fm135tHavG09p1Ao2C9fTU0NO2qr/SIqFAqdwxOah55WRUUFO8rXdHlDOnLkCGNjY8Ncvny5y8t25QtZXV3N9O3bl/n222+73E5ntffZtTVdq7PvtUgkYs6dO9eldtuat7UR5PbqcL1ujAAAC79JREFU0IapdtS4eZh1NN1Y0tPTmb59+zLV1dWdmp8Cr5NvAMMwTFBQELN48WJ9SuPcuXPnWv1BabcGTem5555jXnzxxW4t29UbccfHxzMuLi5MfX19t9rjmlqtbvV4PD6rr69nnJ2dmS1btnR6GXMNPLPrwwOA9evXQy6Xo6ysjOtSukUmk8Hd3b3V/j1HR0dIpVKT1VJaWgq5XI7169ebpL3169djyJAhiIiIQGNjo0naNKS0tDQEBgZyXYbZePDgAUJDQzF06FCTfYeMySwDb/z48XjxxRcRGhpqkTcN3rt3LyQSSYvTkZRKJdLS0gx2FZiOPHjwAOHh4XjllVcwbtw4k7TZq1cvfP755zh48CDWrFljkjb1FRsbq3MKmaEuONATvP766zh06BD27NnTI26FapaBBzw8EbuyshJbtmzhupQuS0lJwcCBA7F161adc22rqqoQFhZmsjq2bNmCqqoqk18e39vbGwcOHMCuXbuwfv16s9/S026JJycnIy4ujuNqzENjYyOio6Oxe/duHDhwoOfcEF2f/WFj9eFpff3114yVlRWzZ8+e7pTHa3v27GGsrKyYgwcP6rWervbhNSWXy5m+ffsyAQEBFtunx0f19fWMSCRi+vbty8jl8m6to7O/d+rDa+K5557Dli1bsGLFih5x8UFTOXz4MFasWIEtW7Zg4cKFnNWxaNEi5OTkoLi4GNOmTcOZM2c4q4V0TllZGaZMmYKSkhIcPXoUixYt4rokgzLrwAOAjRs3IiwsDCKRCF9++SXX5Zi9ffv2QSQSYdWqVdi4cSPX5WDSpEkoKirCoEGD8Kc//Qnvvfce7t27x3VZpJl79+7h3XffhY+PD4YMGYKioiL4+PhwXZbBmX3gAcD27dvxxhtvICgoCJ988gnX5ZglhmHw0UcfISQkBG+++Sb+8Y9/cF0Sa9iwYTh69Ci2bt2KDz/8EBMnTkROTg7XZZH/OnLkCHx8fCAWi/Hhhx8iJycHjz32GNdlGYVFBJ5AIMAHH3yAhIQEvPnmm/jzn/+sc5lvvrt27RqWLFmCDRs24B//+Afi4+PN7p4dVlZWWLduHcrKyjBs2DDMnj0b8+fPx8mTJ7kujbdOnDiBefPmwc/PD8OHD0dZWRneeOMNWFlZRCx0i0W9sr/+9a/Izs5GcXExxo8fb/Qr+FqC7777DuPHj0dJSQmOHDli9oeCjB49GhkZGcjMzMT169cxYcIEBAYGoqCggOvSeCM/Px9Lly7FxIkTUV9fj+zsbGRkZGDUqFFcl2Z0FhV4AODr6wuFQoGJEydi/vz5CAwMRFVVFddlmVxVVRUCAwPx7LPPsp3MM2bM4LqsTpszZw4KCgrw1VdfoaKiAlOnTsWUKVMglUpx//59rsvrce7fv4+9e/di0qRJmDZtGqqqqnDgwAHk5eXh6aef5ro8k7G4wAOARx99FGlpacjIyIBCocDYsWOxceNGXL16levSjK6urg7r16+Hp6cnFAoFDh06hH379uHRRx/lurQuEwgE+POf/4zCwkLk5eVh5MiRePnllzFixAisXbsWJ06c4LpEi1dcXIy1a9dixIgReOWVVzB69Gjk5+ejoKAAixcvNruuD2OzyMDTmj9/Pk6dOoV3330Xn3/+OUaNGoW33noL1dXVXJdmcFVVVdiwYQNGjx6N//znP3jvvfdQVlaGuXPncl2aQUydOhUymQznz5/HX//6Vxw+fBg+Pj4YO3YstmzZgtOnT3NdosUoKytDfHw8xo4di4kTJ+KHH37AX//6V5w/fx4ymcwol72yFBYdeADwyCOPICoqCufPn8emTZuwe/dujBw5EkuXLkV2drZF33+WYRhkZmbi+eefx8iRI5GSkoJ3330X58+fx7p169C3b1+uSzQ4Z2dnvPXWWzhz5gxKSkqwYMEC/Pvf/4aXlxdGjRqFNWvW4NChQ7hz5w7XpZqNO3fu4Pvvv8eaNWswatQojBs3DhKJBAsXLkRJSQlOnz6Nt956C87OzlyXyjnLPznuv/r3748333wTb7zxBg4cOICdO3dizpw5bPgtW7bMIo4rYhgGRUVFSEtLw5dffomKigrMnj0bUqkUixcvZi+lzgfjx4/H+PHj8dFHH6G0tBTfffcd5HI5kpKSYGNjg0mTJsHX1xe+vr6YPn06Bg4cyHXJJlFfX49jx47hp59+Qm5uLoqKitDQ0IDJkycjPDwczz77rMnOnbY0PSbwtHr16oVly5Zh2bJlOHv2LGQyGfbv3w+xWIxRo0Zh3rx58Pf3h5+fHwYPHsx1uQAeHlZy5MgR/PDDD/j+++9RUVGBJ598Eq+++iqCgoLg4eHBdYmcEwqFEP7/9u7npek/gOP4c01N12wfzdXCtgw+Dgld0ipC7BAESkPokpdO3Tp1+N528yQi/Q/eunRICE8FQT+owxYWg2B9QGyjhmu0aKiUtu9B/KBQ4ferzbb36wEfxA+Oz9sxnn722eft+8wZkskkxWKRx48f8+zZM+bm5piamsLj8XD69GnOnj3L4OCgu9V6UaG9Vi6XmZ+fd7d0Os3bt2+pVqvEYjEuXbrE7du3uXz5cl0sfrXfGi54W/X19TExMcHExASZTIYHDx7w8OFDZmZmWFtbY2BggHg8zrlz54jH4wwMDPzxt4krKytkMhlSqRTpdJpUKkUmk6G5uZmhoSFu3brF2NhY40zW/gOCwSDj4+OMj48DG1F4/vw5L168YH5+njt37rhrifT09GDbNtFo1P3a29tLJBL5axajWV1dJZfLkc1meffunbs5jsPCwgIA3d3dDA4Ocu3aNaamphgeHv6jizI1qoYO3lb9/f309/eTTCZZXl7m6dOnPHnyhFQqxezsLKVSCY/HQzgcpre3F9u2OXnyJKFQiGAwSFdXF8FgEJ/PR2trK16vl8OHDwMbbzHW19dZXV1leXmZYrFIsVjk06dPFAoFFhcXyWazOI5DPp+nWq1y5MgR4vE4iUSC6elphoeH8fl8+/ws1SfLskgkEiQSCXff0tISr1694s2bN2SzWTKZDPfv3+fDhw/uz3R1dREKhThx4gShUIhwOIxlWViWRSAQIBAI0NHRQSAQ4MCBA1iW5X6qeejQIVpaWoCNaVmbK3pVq1XK5TI/fvzgy5cvfP782V0FrVwuUy6XyeVyFAoF8vk8Hz9+pFQquWPq7u7Gtm1s2+bKlSvEYjHi8TjBYLAWT2XDMyZ4W/l8PkZGRhgZGXH3LSws8Pr1axzHcbdHjx5RKBTcF/N/5ff7OXbsGD09PUSjUa5evUo0GiUWi9X3Qih14OjRo4yOjjI6Orptf6VSwXEc3r9/Tz6fp1AokMvlWFpaIp1Ou4Ha66VC29vb3YCGw2GOHz/O+fPn3eBGIhFs28bv9+/pcWU7I4P3M6dOnfrlneYrKyvuGVulUuHbt298//6dSqUCbIStubmZlpYW/H6/ezbY1tZWy19BdsDv97vX935nfX1921nZ5vebvn79ytraGgBNTU3bPjAJBAJ4vV73bNGyrIaerlVPFLwdaGtrIxKJ/HJJRmk8Xq+Xzs5OOjs793sosof0Z0dEjKHgiYgxFDwRMYaCJyLGUPBExBgKnogYQ8ETEWPoPjzZkXv37u33EER2TcGT37JtG8CdqC+yU5uvnb+Jgie/deHChbr+J6oiW+kanogYQ8ETEWMoeCJiDAVPRIyh4ImIMRQ8ETGGgicixlDwRMQYNb3x+MaNG8zOztbykCLyF6v1lMWaBS+ZTOI4Tq0OJyJ14Pr16zWdglaz4E1OTtbqUCIiP6VreCJiDAVPRIyh4ImIMRQ8ETGGgicixlDwRMQYCp6IGEPBExFj7OrG44MHDzIzM8Pdu3f3ajwiIr908+bNXT3eU93FCi25XI6XL1/uagAiIjt18eJFwuHw/378roInIlJPdA1PRIyh4ImIMRQ8ETGGgicixlDwRMQYCp6IGEPBExFjKHgiYgwFT0SMoeCJiDEUPBExhoInIsZQ8ETEGE3AP/s9CBGRWvgX67VqviWD7/oAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from smile.dag import DAG\n", + "d = DAG(exp)\n", + "d.view_png()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Advanced Placement\n", + "\n", + "- All visual states have a coordinate system based on the screen.\n", + "- You can place any visual state relative to either the screen or other visual states:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# load in smile states\n", + "from smile.common import *\n", + "\n", + "# create an experiment instance\n", + "exp = Experiment(show_splash=False, resolution=(1024,768))\n", + "\n", + "# show images and labels until keypress\n", + "with Parallel():\n", + " # place images based on the screen coords\n", + " out_im = Image(source=\"C:/WINDOWS/system32/compsy/lessons/outdoor/out0091_new.jpg\",\n", + " left=exp.screen.center_x + 150)\n", + " in_im = Image(source=\"C:/WINDOWS/system32/compsy/lessons/outdoor/out0021_new.jpg\",\n", + " right=exp.screen.center_x - 150)\n", + " # place labels based on the images\n", + " out_txt = Label(text='Outdoor', font_size=50, \n", + " center_bottom=out_im.center_top)\n", + " in_txt = Label(text='Indoor', font_size=50, \n", + " center_bottom=in_im.center_top)\n", + "with UntilDone():\n", + " KeyPress()\n", + "\n", + "# run the experiment\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Mouse States\n", + "\n", + "- The mouse is hidden by default\n", + "- You can add in a mouse cursor with the `MouseCursor` state\n", + "- It's possible to trigger events based on mouse location" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO ] [Logger ] Record log in C:\\Users\\Carlos Rodriguez\\.kivy\\logs\\kivy_20-10-20_45.txt\n", + "[INFO ] [Kivy ] v1.11.1\n", + "[INFO ] [Kivy ] Installed at \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\__init__.py\"\n", + "[INFO ] [Python ] v3.8.3 (default, Jul 2 2020, 17:30:36) [MSC v.1916 64 bit (AMD64)]\n", + "[INFO ] [Python ] Interpreter at \"C:\\ProgramData\\Anaconda3\\python.exe\"\n", + "[INFO ] [Factory ] 184 symbols loaded\n", + "[INFO ] [Image ] Providers: img_tex, img_dds, img_sdl2, img_pil, img_gif (img_ffpyplayer ignored)\n", + "[INFO ] [Text ] Provider: sdl2\n", + "[CRITICAL] [Camera ] Unable to find any valuable Camera provider. Please enable debug logging (e.g. add -d if running from the command line, or change the log level in the config) and re-run your app to identify potential causes\n", + "picamera - ModuleNotFoundError: No module named 'picamera'\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\__init__.py\", line 59, in core_select_lib\n", + " mod = __import__(name='{2}.{0}.{1}'.format(\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\camera\\camera_picamera.py\", line 18, in \n", + " from picamera import PiCamera\n", + "\n", + "gi - ModuleNotFoundError: No module named 'gi'\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\__init__.py\", line 59, in core_select_lib\n", + " mod = __import__(name='{2}.{0}.{1}'.format(\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\camera\\camera_gi.py\", line 10, in \n", + " from gi.repository import Gst\n", + "\n", + "opencv - ModuleNotFoundError: No module named 'cv2'\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\__init__.py\", line 59, in core_select_lib\n", + " mod = __import__(name='{2}.{0}.{1}'.format(\n", + " File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\kivy\\core\\camera\\camera_opencv.py\", line 48, in \n", + " import cv2\n", + "\n", + "[INFO ] [Video ] Provider: null(['video_ffmpeg', 'video_ffpyplayer'] ignored)\n", + "[WARNING] [SMILE ] Unable to import PYO!\n", + "[WARNING] [SMILE ] Durations will be maintained, unless none are specified\n" + ] + }, + { + "ename": "OSError", + "evalue": "[Errno 22] Invalid argument: '.\\\\data\\\\SMILE\\\\test000\\\\20201020_145759\\\\record__20_0.slog'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mOSError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 31\u001b[0m rt=w.event_time['time'] - choice_B.appear_time['time'])\n\u001b[0;32m 32\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m \u001b[0mexp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\smile\\experiment.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, trace)\u001b[0m\n\u001b[0;32m 616\u001b[0m \u001b[1;31m# open all the logs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 617\u001b[0m \u001b[1;31m# (this will call begin_log for entire state machine)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 618\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_root_state\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbegin_log\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 619\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 620\u001b[0m \u001b[1;31m# clone the root state in prep for starting the state machine\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\smile\\state.py\u001b[0m in \u001b[0;36mbegin_log\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 945\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParentState\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbegin_log\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 946\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_children\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 947\u001b[1;33m \u001b[0mchild\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbegin_log\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 948\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 949\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mend_log\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mto_csv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\smile\\state.py\u001b[0m in \u001b[0;36mbegin_log\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 945\u001b[0m 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\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mOSError\u001b[0m: [Errno 22] Invalid argument: '.\\\\data\\\\SMILE\\\\test000\\\\20201020_145759\\\\record__20_0.slog'" + ] + } + ], + "source": [ + "from smile.common import *\n", + "\n", + "# set up an experiment\n", + "exp = Experiment(show_splash=False, debug=True)\n", + "\n", + "Wait(0.5)\n", + "\n", + "# display a rectangle and the mouse cursor\n", + "with Parallel():\n", + " rect = Rectangle(center_bottom=exp.screen.center_bottom,\n", + " color='white')\n", + " MouseCursor()\n", + "with UntilDone():\n", + " Wait(until=MouseWithin(rect))\n", + "\n", + "# put up two new rectangles\n", + "with Parallel():\n", + " choice_A = Rectangle(left_top=(exp.screen.left + 100, exp.screen.top - 100))\n", + " choice_B = Rectangle(right_top=(exp.screen.right - 100, exp.screen.top - 100))\n", + " mrec = Record(mouse_pos=MousePos())\n", + " MouseCursor()\n", + "with UntilDone():\n", + " mwa = MouseWithin(choice_A)\n", + " mwb = MouseWithin(choice_B)\n", + " w = Wait(until= mwa | mwb)\n", + " with If(mwa):\n", + " Debug(choice='A',\n", + " rt=w.event_time['time'] - choice_A.appear_time['time'])\n", + " with Else():\n", + " Debug(choice='B',\n", + " rt=w.event_time['time'] - choice_B.appear_time['time'])\n", + "\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Animations\n", + "\n", + "- It's possible to link the attributes of a visual state to a function.\n", + "- Here we move rectangles based on the mouse position:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from smile.common import *\n", + "\n", + "# set up an experiment\n", + "exp = Experiment(show_splash=False, debug=True)\n", + "\n", + "Wait(1.0)\n", + "\n", + "with Parallel():\n", + " rect = Rectangle(center_bottom=exp.screen.center_bottom,\n", + " color='white')\n", + " r2 = Rectangle(bottom=rect.bottom, color='purple')\n", + " r3 = Rectangle(center_top=exp.screen.center_top, color='green')\n", + " MouseCursor()\n", + "with UntilDone():\n", + " Wait(until=MouseWithin(rect))\n", + " with Meanwhile():\n", + " with Parallel():\n", + " r2.animate(center_x=lambda t, initial: MousePos()[0])\n", + " r3.animate(center_y=lambda t, initial: MousePos()[1])\n", + "\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Sliding\n", + "\n", + "- Visual States have a slide method to set new parameter values over a duration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from smile.common import *\n", + "\n", + "# set up the experiment\n", + "exp = Experiment(show_splash=False, debug=True)\n", + "\n", + "# initial wait\n", + "Wait(.25)\n", + "\n", + "# Put up a circle\n", + "circ = Ellipse(color=(jitter(0, 1),\n", + " jitter(0, 1),\n", + " jitter(0, 1)))\n", + "with UntilDone():\n", + " Wait(until=circ.appeared)\n", + " with Loop(5):\n", + " # slide to new loc and color\n", + " exp.new_col = (jitter(0, 1),\n", + " jitter(0, 1),\n", + " jitter(0, 1))\n", + " exp.new_loc = (jitter(0, exp.screen.width),\n", + " jitter(0, exp.screen.height))\n", + " cu = circ.slide(duration=1.5,\n", + " color=exp.new_col,\n", + " center=exp.new_loc)\n", + "\n", + "Wait(.25)\n", + "\n", + "exp.run()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Animate and Slide together!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from smile.common import *\n", + "from math import cos\n", + "\n", + "exp = Experiment(show_splash=False)\n", + "\n", + "# add a circle off the screen\n", + "ellipse = Ellipse(right=exp.screen.left,\n", + " center_y=exp.screen.center_y, width=50, height=50,\n", + " angle_start=90.0, angle_end=460.0,\n", + " color=(1.0, 1.0, 0.0), name=\"Pacman\")\n", + "\n", + "with UntilDone():\n", + " with Parallel(name=\"Pacman motion\"):\n", + " ellipse.slide(left=exp.screen.right, duration=2.0, name=\"Pacman travel\")\n", + " ellipse.animate(\n", + " angle_start=lambda t, initial: initial + (cos(t * 8) + 1) * 22.5,\n", + " angle_end=lambda t, initial: initial - (cos(t * 8) + 1) * 22.5,\n", + " duration=4.0, name=\"Pacman gobble\")\n", + "\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Logging\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." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Subroutines\n", + "\n", + "- Functions are a great way to make clean programs when you have chunks of code that are called more than once.\n", + "- The `Subroutine` decorator allows you to take chunks of state machine and call them like a function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# when defining the subroutine, you must include self as the first arg\n", + "@Subroutine\n", + "def MyTrial(self, text):\n", + " Label(text=text)\n", + " with UntilDone():\n", + " KeyPress()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Let's continue learning by building together!\n", + "\n", + "- Last class we wrote a list generation and initial Flanker task.\n", + "- Let's update the frontend experiment to loop over those trials with a subroutine." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# List Gen Function" + ] + }, + { + "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", + "\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", + "print(trials)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Goal for each trial\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", + "\n", + "font_size = 75\n", + "resp_keys = ['F', 'J']\n", + "ISI_dur = 0.3\n", + "ISI_jitter = 0.2\n", + "\n", + "# create the experiment\n", + "exp = Experiment(show_splash=False, fullscreen=False)\n", + "\n", + "var = 40\n", + "\n", + "@Subroutine\n", + "def Trial(self, cur_trial):\n", + " stim = Label(text=cur_trial['stimulus'], center=exp.screen.center - var,\n", + " font_size=font_size)\n", + " with UntilDone():\n", + " kp = KeyPress(keys=resp_keys)\n", + " \n", + " Wait(ISI_dur, jitter=ISI_jitter)\n", + " \n", + " Log(cur_trial, name='flanker',\n", + " stim_on=stim.appear_time,\n", + " resp=kp.pressed,\n", + " resp_time=kp.press_time\n", + " )\n", + " \n", + "# show the stimulus (will default to center of the screen)\n", + "with Loop(trials) as trial:\n", + " Trial(trial.current)\n", + "\n", + "# run the experiment\n", + "exp.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assignment before next class\n", + "\n", + "- We'll be posting the SMILE assignment soon.\n", + "- Continue familiarizing yourself with SMILE, since that will be front and center for the next assignment.\n", + "\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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/08_Exp_to_Data.ipynb b/CS4500_CompMethods/lessons/08_Exp_to_Data.ipynb new file mode 100644 index 0000000..22413b4 --- /dev/null +++ b/CS4500_CompMethods/lessons/08_Exp_to_Data.ipynb @@ -0,0 +1,1353 @@ +{ + "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==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": [ + "# 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": 2, + "metadata": {}, + "outputs": [], + "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": 3, + "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": 6, + "metadata": {}, + "outputs": [], + "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": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.9M\tdata\r\n" + ] + } + ], + "source": [ + "!du -sh data" + ] + }, + { + "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" + ] + } + ], + "source": [ + "!ls data/SMILE/test000/20201015_153059" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'instantiation_filename': 'Turn on Debug Mode for this information',\n", + " 'instantiation_lineno': 0,\n", + " 'name': None,\n", + " 'start_time': 952398.971597069,\n", + " 'end_time': 952424.8647644764,\n", + " 'enter_time': 952398.472749577,\n", + " 'leave_time': 952424.875065739,\n", + " 'finalize_time': 952424.875176885,\n", + " 'base_time': 952398.971597069,\n", + " 'pressed': 'SPACEBAR',\n", + " 'press_time_time': 952424.8647644764,\n", + " 'press_time_error': 0.009731228521559387,\n", + " 'correct': False,\n", + " 'rt': 25.893167407484725,\n", + " 'log_num': 0}]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from smile.log import log2dl\n", + "dl = log2dl('data/SMILE/test000/20201015_153059/state_KeyPress_0.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": 14, + "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" + ] + } + ], + "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": 19, + "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 = 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 = 200\n", + "LOC_Y_jitter = 100\n", + "inst_font_size = 25\n", + "inst_text = \"\"\"[u][size=40]FLANKER INSTRUCTIONS[/size][/u]\n", + "\n", + "In this task, you will see stimuli one at at time on the screen. \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), scale_box=(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", + "# Get the subj id information\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", + "# 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')\n", + "with UntilDone():\n", + " KeyPress(keys=['ENTER'])\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": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'block_num': 0,\n", + " 'trial_num': 0,\n", + " 'stim_on_time': 954870.289099855,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'J',\n", + " 'resp_time_time': 954871.0955087265,\n", + " 'resp_time_error': 0.00016560248332098126,\n", + " 'rt': 0.806408871547319,\n", + " 'correct': True,\n", + " 'location_0': 360.8089525252066,\n", + " 'location_1': 383.30362645275306,\n", + " 'log_time': 954871.6452580952,\n", + " 'condition': 'congruent',\n", + " 'direction': 'right',\n", + " 'stimulus': '>>>>>>>',\n", + " 'log_num': 0},\n", + " {'block_num': 0,\n", + " 'trial_num': 1,\n", + " 'stim_on_time': 954871.655407118,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'F',\n", + " 'resp_time_time': 954872.3834008861,\n", + " 'resp_time_error': 0.0001642329734750092,\n", + " 'rt': 0.7279937680577859,\n", + " 'correct': True,\n", + " 'location_0': 523.7262231937616,\n", + " 'location_1': 477.31968840778643,\n", + " 'log_time': 954873.0225743013,\n", + " 'condition': 'congruent',\n", + " 'direction': 'left',\n", + " 'stimulus': '<<<<<<<',\n", + " 'log_num': 0},\n", + " {'block_num': 0,\n", + " 'trial_num': 2,\n", + " 'stim_on_time': 954873.038298427,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'J',\n", + " 'resp_time_time': 954873.7354019149,\n", + " 'resp_time_error': 0.00018212903523817658,\n", + " 'rt': 0.6971034879097715,\n", + " 'correct': True,\n", + " 'location_0': 518.400284447627,\n", + " 'location_1': 334.80361618012347,\n", + " 'log_time': 954874.7098478917,\n", + " 'condition': 'congruent',\n", + " 'direction': 'right',\n", + " 'stimulus': '>>>>>>>',\n", + " 'log_num': 0},\n", + " {'block_num': 0,\n", + " 'trial_num': 3,\n", + " 'stim_on_time': 954874.72097419,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'J',\n", + " 'resp_time_time': 954875.319064312,\n", + " 'resp_time_error': 0.00017657398711889982,\n", + " 'rt': 0.5980901219882071,\n", + " 'correct': True,\n", + " 'location_0': 481.5854658715342,\n", + " 'location_1': 300.5602553695706,\n", + " 'log_time': 954876.0103888777,\n", + " 'condition': 'neutral',\n", + " 'direction': 'right',\n", + " 'stimulus': '===>===',\n", + " 'log_num': 0},\n", + " {'block_num': 0,\n", + " 'trial_num': 4,\n", + " 'stim_on_time': 954876.020861595,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'F',\n", + " 'resp_time_time': 954876.7270706014,\n", + " 'resp_time_error': 0.00016534148016944528,\n", + " 'rt': 0.7062090063700452,\n", + " 'correct': True,\n", + " 'location_0': 353.95290941556425,\n", + " 'location_1': 449.833555376235,\n", + " 'log_time': 954877.6206667155,\n", + " 'condition': 'neutral',\n", + " 'direction': 'left',\n", + " 'stimulus': '===<===',\n", + " 'log_num': 0},\n", + " {'block_num': 0,\n", + " 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'===>===',\n", + " 'log_num': 0},\n", + " {'block_num': 0,\n", + " 'trial_num': 7,\n", + " 'stim_on_time': 954880.236257618,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'J',\n", + " 'resp_time_time': 954882.343831201,\n", + " 'resp_time_error': 0.009121523005887866,\n", + " 'rt': 2.1075735830236226,\n", + " 'correct': True,\n", + " 'location_0': 491.5092137386937,\n", + " 'location_1': 410.8337717200968,\n", + " 'log_time': 954883.2585443532,\n", + " 'condition': 'incongruent',\n", + " 'direction': 'right',\n", + " 'stimulus': '<<<><<<',\n", + " 'log_num': 0},\n", + " {'block_num': 0,\n", + " 'trial_num': 8,\n", + " 'stim_on_time': 954883.286224209,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'F',\n", + " 'resp_time_time': 954883.904225479,\n", + " 'resp_time_error': 0.00018427299801260233,\n", + " 'rt': 0.6180012699915096,\n", + " 'correct': True,\n", + " 'location_0': 523.4753054808052,\n", + " 'location_1': 426.7656130497411,\n", + " 'log_time': 954884.5665998099,\n", + " 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" 'resp_time_time': 954903.3389387125,\n", + " 'resp_time_error': 0.009492939454503357,\n", + " 'rt': 0.6626885285368189,\n", + " 'correct': True,\n", + " 'location_0': 579.6885418109841,\n", + " 'location_1': 468.32516362039416,\n", + " 'log_time': 954904.0882496499,\n", + " 'condition': 'congruent',\n", + " 'direction': 'right',\n", + " 'stimulus': '>>>>>>>',\n", + " 'log_num': 0},\n", + " {'block_num': 1,\n", + " 'trial_num': 5,\n", + " 'stim_on_time': 954904.131896587,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'F',\n", + " 'resp_time_time': 954904.9707146101,\n", + " 'resp_time_error': 0.009472176025155932,\n", + " 'rt': 0.8388180230977014,\n", + " 'correct': True,\n", + " 'location_0': 638.7134195411566,\n", + " 'location_1': 467.2312656382712,\n", + " 'log_time': 954905.4725534584,\n", + " 'condition': 'congruent',\n", + " 'direction': 'left',\n", + " 'stimulus': '<<<<<<<',\n", + " 'log_num': 0},\n", + " {'block_num': 1,\n", + " 'trial_num': 6,\n", + " 'stim_on_time': 954905.498099823,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'J',\n", + " 'resp_time_time': 954906.287039916,\n", + " 'resp_time_error': 0.009410488011781126,\n", + " 'rt': 0.7889400930143893,\n", + " 'correct': True,\n", + " 'location_0': 596.9378027614156,\n", + " 'location_1': 311.5525865659883,\n", + " 'log_time': 954907.1235673903,\n", + " 'condition': 'incongruent',\n", + " 'direction': 'right',\n", + " 'stimulus': '<<<><<<',\n", + " 'log_num': 0},\n", + " {'block_num': 1,\n", + " 'trial_num': 7,\n", + " 'stim_on_time': 954907.149002917,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'J',\n", + " 'resp_time_time': 954907.7589961896,\n", + " 'resp_time_error': 0.002389204513747245,\n", + " 'rt': 0.6099932725774124,\n", + " 'correct': True,\n", + " 'location_0': 537.4981840468671,\n", + " 'location_1': 482.5181344477846,\n", + " 'log_time': 954908.4057255693,\n", + " 'condition': 'neutral',\n", + " 'direction': 'right',\n", + " 'stimulus': '===>===',\n", + " 'log_num': 0},\n", + " {'block_num': 1,\n", + " 'trial_num': 8,\n", + " 'stim_on_time': 954908.430531045,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'F',\n", + " 'resp_time_time': 954909.137428819,\n", + " 'resp_time_error': 0.008356418984476477,\n", + " 'rt': 0.7068977740127593,\n", + " 'correct': True,\n", + " 'location_0': 427.61999116595973,\n", + " 'location_1': 435.4956387488511,\n", + " 'log_time': 954909.8565501624,\n", + " 'condition': 'congruent',\n", + " 'direction': 'left',\n", + " 'stimulus': '<<<<<<<',\n", + " 'log_num': 0},\n", + " {'block_num': 1,\n", + " 'trial_num': 9,\n", + " 'stim_on_time': 954909.897096403,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'J',\n", + " 'resp_time_time': 954910.436795317,\n", + " 'resp_time_error': 0.00957260001450777,\n", + " 'rt': 0.539698914042674,\n", + " 'correct': True,\n", + " 'location_0': 467.65428261055234,\n", + " 'location_1': 329.6555283678654,\n", + " 'log_time': 954911.3518943688,\n", + " 'condition': 'neutral',\n", + " 'direction': 'right',\n", + " 'stimulus': '===>===',\n", + " 'log_num': 0},\n", + " {'block_num': 1,\n", + " 'trial_num': 10,\n", + " 'stim_on_time': 954911.380208996,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'F',\n", + " 'resp_time_time': 954912.0687173165,\n", + " 'resp_time_error': 0.009421262540854514,\n", + " 'rt': 0.6885083204833791,\n", + " 'correct': True,\n", + " 'location_0': 482.0600674553221,\n", + " 'location_1': 327.31024907860115,\n", + " 'log_time': 954912.8537654872,\n", + " 'condition': 'incongruent',\n", + " 'direction': 'left',\n", + " 'stimulus': '>>><>>>',\n", + " 'log_num': 0},\n", + " {'block_num': 1,\n", + " 'trial_num': 11,\n", + " 'stim_on_time': 954912.89799192,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'J',\n", + " 'resp_time_time': 954913.5362527535,\n", + " 'resp_time_error': 0.009343342506326735,\n", + " 'rt': 0.638260833453387,\n", + " 'correct': True,\n", + " 'location_0': 348.6348106187002,\n", + " 'location_1': 294.8978151477944,\n", + " 'log_time': 954914.4723602355,\n", + " 'condition': 'congruent',\n", + " 'direction': 'right',\n", + " 'stimulus': '>>>>>>>',\n", + " 'log_num': 0},\n", + " {'block_num': 2,\n", + " 'trial_num': 0,\n", + " 'stim_on_time': 954916.84492168,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'F',\n", + " 'resp_time_time': 954917.4679825695,\n", + " 'resp_time_error': 0.008579178480431437,\n", + " 'rt': 0.6230608894256875,\n", + " 'correct': True,\n", + " 'location_0': 597.9383367791996,\n", + " 'location_1': 293.7179503656032,\n", + " 'log_time': 954918.3451234616,\n", + " 'condition': 'neutral',\n", + " 'direction': 'left',\n", + " 'stimulus': '===<===',\n", + " 'log_num': 0},\n", + " {'block_num': 2,\n", + " 'trial_num': 1,\n", + " 'stim_on_time': 954918.395887578,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'J',\n", + " 'resp_time_time': 954919.1166650434,\n", + " 'resp_time_error': 0.00940229051047936,\n", + " 'rt': 0.7207774653797969,\n", + " 'correct': True,\n", + " 'location_0': 611.5309296366736,\n", + " 'location_1': 401.23070153645443,\n", + " 'log_time': 954919.6430923046,\n", + " 'condition': 'congruent',\n", + " 'direction': 'right',\n", + " 'stimulus': '>>>>>>>',\n", + " 'log_num': 0},\n", + " {'block_num': 2,\n", + " 'trial_num': 2,\n", + " 'stim_on_time': 954919.677470153,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'F',\n", + " 'resp_time_time': 954920.2507915329,\n", + " 'resp_time_error': 0.009429041005205363,\n", + " 'rt': 0.5733213799539953,\n", + " 'correct': True,\n", + " 'location_0': 701.7094220435131,\n", + " 'location_1': 342.8130129643691,\n", + " 'log_time': 954920.8302064759,\n", + " 'condition': 'incongruent',\n", + " 'direction': 'left',\n", + " 'stimulus': '>>><>>>',\n", + " 'log_num': 0},\n", + " {'block_num': 2,\n", + " 'trial_num': 3,\n", + " 'stim_on_time': 954920.87701435,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'J',\n", + " 'resp_time_time': 954921.4329561766,\n", + " 'resp_time_error': 0.009179625485558063,\n", + " 'rt': 0.555941826547496,\n", + " 'correct': True,\n", + " 'location_0': 577.8356518989913,\n", + " 'location_1': 473.0309247201444,\n", + " 'log_time': 954922.2535121355,\n", + " 'condition': 'neutral',\n", + " 'direction': 'right',\n", + " 'stimulus': '===>===',\n", + " 'log_num': 0},\n", + " {'block_num': 2,\n", + " 'trial_num': 4,\n", + " 'stim_on_time': 954922.294778626,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'F',\n", + " 'resp_time_time': 954922.8034653595,\n", + " 'resp_time_error': 0.0001656354870647192,\n", + " 'rt': 0.5086867335485294,\n", + " 'correct': True,\n", + " 'location_0': 397.13496042361186,\n", + " 'location_1': 457.5990590523711,\n", + " 'log_time': 954923.7187468251,\n", + " 'condition': 'congruent',\n", + " 'direction': 'left',\n", + " 'stimulus': '<<<<<<<',\n", + " 'log_num': 0},\n", + " {'block_num': 2,\n", + " 'trial_num': 5,\n", + " 'stim_on_time': 954923.76103776,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'F',\n", + " 'resp_time_time': 954924.26670717,\n", + " 'resp_time_error': 0.009732596052344888,\n", + " 'rt': 0.5056694101076573,\n", + " 'correct': True,\n", + " 'location_0': 327.69676521882076,\n", + " 'location_1': 374.49605238550805,\n", + " 'log_time': 954925.2545395218,\n", + " 'condition': 'neutral',\n", + " 'direction': 'left',\n", + " 'stimulus': '===<===',\n", + " 'log_num': 0},\n", + " {'block_num': 2,\n", + " 'trial_num': 6,\n", + " 'stim_on_time': 954925.2771632,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'J',\n", + " 'resp_time_time': 954925.7986897355,\n", + " 'resp_time_error': 0.008986253524199128,\n", + " 'rt': 0.5215265355072916,\n", + " 'correct': True,\n", + " 'location_0': 632.1871415507924,\n", + " 'location_1': 483.78488386119284,\n", + " 'log_time': 954926.6692699132,\n", + " 'condition': 'congruent',\n", + " 'direction': 'right',\n", + " 'stimulus': '>>>>>>>',\n", + " 'log_num': 0},\n", + " {'block_num': 2,\n", + " 'trial_num': 7,\n", + " 'stim_on_time': 954926.71016468,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'F',\n", + " 'resp_time_time': 954927.215606059,\n", + " 'resp_time_error': 0.00939307699445635,\n", + " 'rt': 0.5054413789184764,\n", + " 'correct': False,\n", + " 'location_0': 514.9860049132062,\n", + " 'location_1': 420.6897451358569,\n", + " 'log_time': 954928.1374766257,\n", + " 'condition': 'incongruent',\n", + " 'direction': 'right',\n", + " 'stimulus': '<<<><<<',\n", + " 'log_num': 0},\n", + " {'block_num': 2,\n", + " 'trial_num': 8,\n", + " 'stim_on_time': 954928.178108396,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'F',\n", + " 'resp_time_time': 954928.799291376,\n", + " 'resp_time_error': 0.0001647140015847981,\n", + " 'rt': 0.6211829800158739,\n", + " 'correct': True,\n", + " 'location_0': 600.2278061844588,\n", + " 'location_1': 435.3755214880001,\n", + " 'log_time': 954929.7257551854,\n", + " 'condition': 'incongruent',\n", + " 'direction': 'left',\n", + " 'stimulus': '>>><>>>',\n", + " 'log_num': 0},\n", + " {'block_num': 2,\n", + " 'trial_num': 9,\n", + " 'stim_on_time': 954929.774995076,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'F',\n", + " 'resp_time_time': 954930.28642564,\n", + " 'resp_time_error': 0.00017161096911877394,\n", + " 'rt': 0.5114305639872327,\n", + " 'correct': False,\n", + " 'location_0': 673.8228293680604,\n", + " 'location_1': 472.15431942728475,\n", + " 'log_time': 954931.1832851917,\n", + " 'condition': 'incongruent',\n", + " 'direction': 'right',\n", + " 'stimulus': '<<<><<<',\n", + " 'log_num': 0},\n", + " {'block_num': 2,\n", + " 'trial_num': 10,\n", + " 'stim_on_time': 954931.209378003,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'J',\n", + " 'resp_time_time': 954931.8308921885,\n", + " 'resp_time_error': 0.009370164480060339,\n", + " 'rt': 0.6215141854481772,\n", + " 'correct': True,\n", + " 'location_0': 487.4141088335443,\n", + " 'location_1': 418.39228160110684,\n", + " 'log_time': 954932.7738914909,\n", + " 'condition': 'neutral',\n", + " 'direction': 'right',\n", + " 'stimulus': '===>===',\n", + " 'log_num': 0},\n", + " {'block_num': 2,\n", + " 'trial_num': 11,\n", + " 'stim_on_time': 954932.807052482,\n", + " 'stim_on_error': 0.0,\n", + " 'resp': 'F',\n", + " 'resp_time_time': 954933.5312273615,\n", + " 'resp_time_error': 0.00017143948934972286,\n", + " 'rt': 0.7241748794913292,\n", + " 'correct': True,\n", + " 'location_0': 557.9610413515001,\n", + " 'location_1': 447.6020706911089,\n", + " 'log_time': 954934.5166633481,\n", + " 'condition': 'congruent',\n", + " 'direction': 'left',\n", + " 'stimulus': '<<<<<<<',\n", + " 'log_num': 0}]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from smile.log import log2dl\n", + "dl = log2dl('data/FLANKER/test000/20201015_161204/log_flanker_0.slog')\n", + "dl" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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block_numtrial_numstim_on_timestim_on_errorrespresp_time_timeresp_time_errorrtcorrectlocation_0location_1log_timeconditiondirectionstimuluslog_num
000954870.2891000.0J954871.0955090.0001660.806409True360.808953383.303626954871.645258congruentright>>>>>>>0
101954871.6554070.0F954872.3834010.0001640.727994True523.726223477.319688954873.022574congruentleft<<<<<<<0
202954873.0382980.0J954873.7354020.0001820.697103True518.400284334.803616954874.709848congruentright>>>>>>>0
303954874.7209740.0J954875.3190640.0001770.598090True481.585466300.560255954876.010389neutralright===>===0
404954876.0208620.0F954876.7270710.0001650.706209True353.952909449.833555954877.620667neutralleft===<===0
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" + ], + "text/plain": [ + " block_num trial_num stim_on_time stim_on_error resp resp_time_time \\\n", + "0 0 0 954870.289100 0.0 J 954871.095509 \n", + "1 0 1 954871.655407 0.0 F 954872.383401 \n", + "2 0 2 954873.038298 0.0 J 954873.735402 \n", + "3 0 3 954874.720974 0.0 J 954875.319064 \n", + "4 0 4 954876.020862 0.0 F 954876.727071 \n", + "\n", + " resp_time_error rt correct location_0 location_1 log_time \\\n", + "0 0.000166 0.806409 True 360.808953 383.303626 954871.645258 \n", + "1 0.000164 0.727994 True 523.726223 477.319688 954873.022574 \n", + "2 0.000182 0.697103 True 518.400284 334.803616 954874.709848 \n", + "3 0.000177 0.598090 True 481.585466 300.560255 954876.010389 \n", + "4 0.000165 0.706209 True 353.952909 449.833555 954877.620667 \n", + "\n", + " condition direction stimulus log_num \n", + "0 congruent right >>>>>>> 0 \n", + "1 congruent left <<<<<<< 0 \n", + "2 congruent right >>>>>>> 0 \n", + "3 neutral right ===>=== 0 \n", + "4 neutral left ===<=== 0 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.DataFrame(dl)\n", + "df.head()" + ] + }, + { + "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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/08_Exp_to_Data_withEdits.ipynb b/CS4500_CompMethods/lessons/08_Exp_to_Data_withEdits.ipynb new file mode 100644 index 0000000..12bc4aa --- /dev/null +++ b/CS4500_CompMethods/lessons/08_Exp_to_Data_withEdits.ipynb @@ -0,0 +1,533 @@ +{ + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "source": [ + "!ls data" + ] + }, + { + "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": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[{'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}], [{'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'neutral', 'direction': 'right', 'stimulus': '===>==='}, {'condition': 'congruent', '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': 'right', 'stimulus': '===>==='}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'congruent', 'direction': 'left', 'stimulus': '<<<<<<<'}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'incongruent', 'direction': 'left', 'stimulus': '>>><>>>'}, {'condition': 'congruent', 'direction': 'right', 'stimulus': '>>>>>>>'}, {'condition': 'incongruent', 'direction': 'right', 'stimulus': '<<<><<<'}, {'condition': 'neutral', 'direction': 'left', 'stimulus': '===<==='}]]\n" + ] + } + ], + "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": 5, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "ename": "RecursionError", + "evalue": "maximum recursion depth exceeded", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mRecursionError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 85\u001b[0m \u001b[1;31m# loop over the trials\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 86\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mLoop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurrent\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtrial\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 87\u001b[1;33m \u001b[0mTrial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrial\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrial\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurrent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 89\u001b[0m \u001b[1;31m# run the experiment\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\smile\\state.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *pargs, **kwargs)\u001b[0m\n\u001b[0;32m 1586\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"name\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1587\u001b[0m blocking=kwargs.pop(\"blocking\", True)) as state:\n\u001b[1;32m-> 1588\u001b[1;33m \u001b[0mretval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mpargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1589\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mretval\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1590\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36mTrial\u001b[1;34m(self, block_num, trial_num, cur_trial)\u001b[0m\n\u001b[0;32m 48\u001b[0m \u001b[0mbase_time\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappear_time\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'time'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 49\u001b[0m correct_resp=Ref.object(resp_map)[cur_trial['direction']])\n\u001b[1;32m---> 50\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcur_trial\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 51\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[1;31m# wait the ISI with jitter\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\smile\\ref.py\u001b[0m in \u001b[0;36m__str__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 125\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 126\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__str__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 127\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'utf-8'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 128\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 129\u001b[0m \u001b[1;31m# Unicode is called when creating the filename\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "... last 1 frames repeated, from the frame below ...\n", + "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\smile\\ref.py\u001b[0m in \u001b[0;36m__str__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 125\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 126\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__str__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 127\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'utf-8'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 128\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 129\u001b[0m \u001b[1;31m# Unicode is called when creating the filename\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mRecursionError\u001b[0m: maximum recursion depth exceeded" + ] + } + ], + "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", + " \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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/09_Data_Processing.ipynb b/CS4500_CompMethods/lessons/09_Data_Processing.ipynb new file mode 100644 index 0000000..bce3acf --- /dev/null +++ b/CS4500_CompMethods/lessons/09_Data_Processing.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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" + ], + "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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" + ], + "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": "3.7.6" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/09_Data_Processing_withEdits.ipynb b/CS4500_CompMethods/lessons/09_Data_Processing_withEdits.ipynb new file mode 100644 index 0000000..78a22e0 --- /dev/null +++ b/CS4500_CompMethods/lessons/09_Data_Processing_withEdits.ipynb @@ -0,0 +1,1873 @@ +{ + "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": 2, + "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": 2, + "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=0.3\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', left=dots.right+40, font_size=28) \n", + " ll = Label(text='Left Coherence:\\n', 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": [ + "
\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", + "
onetwo
a1.04.0
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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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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": [ + "
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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": [ + "
\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
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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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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
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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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/10_Initial_Analyses.ipynb b/CS4500_CompMethods/lessons/10_Initial_Analyses.ipynb new file mode 100644 index 0000000..1c57e07 --- /dev/null +++ b/CS4500_CompMethods/lessons/10_Initial_Analyses.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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/11_Across_Subject_Analyses.ipynb b/CS4500_CompMethods/lessons/11_Across_Subject_Analyses.ipynb new file mode 100644 index 0000000..b7dc254 --- /dev/null +++ b/CS4500_CompMethods/lessons/11_Across_Subject_Analyses.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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keuMb38i3v/3tqpsmaQHeUC0/y0I6zGKm+enVq5WlXlDHU6xnnXUWv/ALv3BMzfW73/3uqpsmaQFDQ0N8+MMf5pxzzuHgwYOceOKJXHjhhZ5RXQGT6w4zO3Fe//Id3PlHzyg9rkm9lEcdZ4MZHR2dc7aBXh6tl3rFxMQEO3bs4IYbbjjq7/fJT36yCfYymVwLqC6pl8pS1Z0h6zgbjBfpSt3L2ULyM7mW1JOak+d2flmsa6LprA5Sd6pjKVvZvKBRqokqb+lbN84GI+XhnQPL52wh+TlyLdVEVSO5krQc3jmwPepYylY2k2tJ0rJVVduu3mctcHvUtZStTCbXkqRl82Lo9qjjjE7WAreP10zkZc31PGbXo1qjKkmqSkrpqMcZl7/vmGW9xlpgdStHrufhaIwkSdUZHR3luc99LmvXruWuu+7ijDPO4MCBA7zhDW+oumnZ1fHMRC/r+OTaN5wkSdWo+jP4W9/6Fvfddx8pJe6++27WrFlTWqwqOaDXWzq+LKSOp8IkSeoEVX4GX3bZZaxdu5adO3dy4403snPnTtauXctll11WWkwph45PriVJUv3s27ePa6+9lsHBQVavXs3g4CDXXnst+/btq7ppUksm15IkSVImHV9zLZWh6jpCSVJrp512Gueffz5ve9vbjtzc5Pzzz+e0006rumlSS45cq5as5Zekzva6172OQ4cOccEFF/C0pz2NCy64gEOHDvG6172u6qZJLTlyrUo5gixJmsvMHQJnbsO9du1a/uAP/sA7B6rjmVyrUnWcfsjbRUvS4njnQHUjy0KkNrMcRepeC929V9LiTUxM0N/fz5YtW+jv72diYqLqJmXhyLUkSYtUx7Nt6l1VlmZOTEwwOjrK+Pg4hw8fZtWqVQwPDwN0femPI9eSJEk1VOXF/WNjY4yPjx81j/n4+PiRGvtuZnItSeo6lmdI3W16epqBgYGjlg0MDDA9PV167LLLUSwLkSR1HcszpO7W19fH1NQUg4ODR5ZNTU3R19dXatx2lKM4ci1JkjrKQmcmPDvR/UZHRxkeHmZycpJDhw4xOTnJ8PAwo6OjpcZtRzmKI9eSJKmjeGai982MEo+MjDA9PU1fXx9jY2OlX8zYjnIUR64lSZLUdkNDQ+zZs4ddu3axZ8+etswSMlOO0ix3OYoj15IkSWqbKqcAnClHmam5nilHsSxEkiRJXanKsp+hoSE+/OEPc84553Dw4EFOPPFELrzwwqyj5ibXkiRJqoWJiQl27NjBDTfccNRsIU9+8pOzJdgm15IkqZYe+5r387X7H2j5mvUv3zHn8pMedDwfe/VTy2iWStQ8W8ju3bvZvHkz4+PjjIyMmFxLkiStxNfuf6BlOcJM8jWX+ZJudTZnC5EkSZIycbYQSdKiLHT1fVlX3ktSN3G2EEldZ6EaxlanUnuhhrGqJNebbkjSwtpx8xqTa0lZtaphbFW/CL1Rw9ic5JrgSlLnGRoaYmhoaMHPpOWy5lqSJEnKxJHrGqv76XtJkqTcTK5rrO6n7yVJknKzLESSJEm1MTIywpo1axgcHGTNmjWMjIxk3X6WkeuIeAvwTOCLKaX+HNuUJEmSchoZGeHqq69m69atnHXWWdxxxx1cfvnlAGzbti1LjFwj138FPD3TtiRJkqTsrrnmGrZu3coll1zCmjVruOSSS9i6dSvXXHNNthhZkuuU0geBe3NsS5IkSSrDwYMHufjii49advHFF3Pw4MFsMdp2QWNEXARcBHDKKaewe/fuZW9rJeuuRC/GnW/b+/fvXzBuWe3qxeNct7i+r6qPW2Vs4xq3m+K22u5CfVYV/WjZej3u8ccfz8te9jKe85znHPn9vuMd7+D444/P1oa2JdcppTcBbwI4++yz07In7f7HHaVM+F3LuC22veDE6mW1qweP80JTHr7wHw/M+1xpUx76vmqPquJWGbvH4i709wvz/w2XOmVpjx3nyuIusN2WfVZF/WipahD3xS9+MVdffTVnnnkmZ511FrfddhvXXHMNF198cbY2OBVfB3C+6d7mlIdS92r19wut/4ZX8ve7mKR+vu37ubB4D+l7OT/81pe3ftFb51sXYHl3YPX3W52ZixZf8YpXcPDgQU488UQuvvjibBczQgcm13V8w5l8qQxVfWlb8MNqng+qxrpQ1oeVX1LzqGMfXYWqkvq6+cb0H1VynP39Vmvbtm1s27attNuf55qKbwLYDJwcEfuAV6eUxpezLd9wUh5VfWlr9WFVZly/pLaHfbSkbhURC74mpbTiOFmS65TSUI7tSJI0F0fM28PjrF42O3Fe//IdLQcLlqvjykIkSZrNEfP28DhLK2dyrdqwJleSJJXN5LrGqrrwrCrW5EpSZ7IcpX0caCqfyXWTur3hqrrwTJKkZpajtI8DTeUzuW7iG06SJEkrYXKttqvbGQJJklQfJtdqO88QSJKkXmVyLZWsbheOSpJUZybXUsm8cFSSpPo4ruoGSJIkSb3CkWtJ0qItWOYE85Y6WeYkNfh31Ns6Lrn2DaeyWPusXlLVTTdalTmB8xHn4mdhb/PvqLd1XHLtG05lsfZZvcSbbvQ2PwvbZ8Hj9Y/zf0mV5tJxyXUdOaKqMvi+ag/nbZe6V6svMND4+13oNdJsJtcdwBFVlcH3VXs4b7u0cpbBqJeYXNdcyw/3eU6FgafD1Hkcqe9tVSVfJn3tYRlM76vTWT6T6xpr1ZF5Kiwvv8SUz5H63lZV8mXSp15T1UBEnc7ymVxLJavjlxi/TEhSZ3Igonwm15KyquOXCUmSZniHRkmSJCkTR64lSRLgBZxSDibXTZxtQJJUZ17AKa2cyXUTi/wldQtHGCWpM5lcS1IXcoRRvcbbkKtXmFyr7Sy/kSQ1q+NtyP0y0bs6Mrn2Ddfbqiy/cf5lSVLVqv4y4WdhuTouua76Dafe5fzLkqS687OwfB2XXNeV3yIldQvPLraHx1lauce+5v187f4H5n2+1d/ZSQ86no+9+qlLjmly3QH8FimpW9Tx7GIVSW4dj7NUhq/d/0DbS1FNriVpBbxAt7eZ5Ep51KmvNLmWepjlRuVzfvz2sUxC6l516itNrqUeVcdyI79M9C5HkCV1C5NrST2hjl8mJEmdx+RakrqUZRKS1HlMriWpC1kmIUmdyeR6Fms2JUmSekMVs5SYXDexZlOSJKl3VDFLicm1JEk6wlp+aWVMriVJEmAtv5SDybUqYW27JEnqRSbXajtr2yVJ+o6IOHbZ1qP/n1JqU2vKU5eBNZNrAfX5w+4Es491rx/nuu2vJC3V7H5woQvtulGdBtaOq7oB6gwppaMek5OTxyxTHnU7znXbX0lSvZlcS5IkSZmYXEuSJEmZWHMtSStUl4t0JEkLM7mWpBWo00U6knqLkxmUw+RakiR1FJO+9qjDLCXQ/rOLWZLriHg68AZgFfDmlNIf5diuJElzWSj5MvHqblUlfSb1vaeKs4srvqAxIlYBfwacA5wFDEXEWSvdriRJ81lo+lBpOZyWVjnkmC3kCcCnU0qfSSl9G7geeFaG7UqSJEldJUdy/Wjgc03/31cskyRJkmolR831sQVKcMx5k4i4CLgI4JRTTmH37t3LDriSdVeiTnH3799fq/2tKm7djnPd9rfKuFXG9r1l3Nyq+v0at316Ku7sWqKlPoAnATub/n8FcEWrdTZt2pSW64zL37fsdVeibnEnJycrietxbg/3t7fjVhnb95Zxy1DV79e47dGN72fgljRPnptj5PrfgO+PiA3A3cDzgPMybFeS1OHqNmtH3fZX0tKtuOY6pXQI+E1gJzANvCOldPtKtyuVKSKOety19ZnHLJO6yULv6bLMHrHp9Vk76ra/kpYuxwWNpJT+IaX0Ayml70spjeXYplSmhT4g/ZBUtzHpk6TO0PF3aHRCd0mSJHWLjk+uZyfOvXprzrryy5MkLcy+UuoeWcpCpOWyPEOSFmZfKXWPjh+5rhtHJyRJkrqXI9cdxtEJSZKk7uXItaSeNPsskGeAJEnt4Mi1pJ7kGSBJUhVMriVJkqRMLAuRJElz8iJ7aekcuZYkSXPyInuVISKOety19ZnHLOtmJteSJElqm6q+tLUrqTe5nkevf6uSJEmqk3Yl9SbX8/BUmCRJkpbK5FqSJEnKxORakiRJysSp+CQpI+8MKUn15si1JGXktRqSVG8m15IkSVImloVINWG5giRJ5XPkWqoJyxUkSSqfybUkSZKUicm1JEmSlInJtSRJkpSJybUkSZKUicm1JEmSlInJtSRJkpSJybUkSZKUicm1JEmSlInJtSRJkpSJybUkSZKUicm1JEmSlInJtSRJkpSJybUkSZKUicm1JEmSlInJtSRJkpSJybUkSZKUicm1JEmSlInJtSRJkpSJybUkSZKUicm1JEmSlInJtSRJkpSJybUkSZKUicm1JEmSlInJtSRJkpSJybUkSZKUicm1JEmSlInJtSRJkpSJybUkSVKTiYkJ+vv72bJlC/39/UxMTFTdJHWR1StZOSJ+GbgS6AOekFK6JUejJEmSqjAxMcHo6Cjj4+McPnyYVatWMTw8DMDQ0FDFrVM3WOnI9R7g2cAHM7RFkiSpUmNjY4yPjzM4OMjq1asZHBxkfHycsbGxqpumLrGikeuU0jRARORpjSRJUoWmp6cZGBg4atnAwADT09MVtUjdxpprSZKkQl9fH1NTU0ctm5qaoq+vr6IWqdtESqn1CyI+ADxqjqdGU0rvKV6zG3hZq5rriLgIuAjglFNO2XT99dcvq8H79+9n3bp1y1p3JYxrXOMat1viVhnbuMbt9ri7du1ifHycSy+9lA0bNrB3716uuuoqhoeH2bJlS1vaUIfj3O1xBwcHb00pnT3nkymlFT+A3cDZi339pk2b0nJNTk4ue92VMK5xjWvcbolbZWzjGrcX4m7fvj1t3LgxHXfccWnjxo1p+/btbY1fl+PczXGBW9I8ee6Kaq4lSZJ6zdDQEENDQ+zevZvNmzdX3Rx1mRXVXEfEL0bEPuBJwI6I2JmnWZIkSVL3WelsIX8H/F2mtkiSJEldzdlCJEmSpExMriVJkqRMTK4lSZKkTEyuJUmSpExMriVJkqRMTK4lSZKkTEyuJUmSpExMriVJkqRMTK4lSZKkTEyuJUmSpExMriVJkqRMTK4lSZKkTEyuJUmSpExMriVJkqRMTK4lSZKkTEyuJUmSpExMriVJkqRMTK4lSZKkTEyuJUmSpExMriVJkqRMTK4lSZKkTEyuJUmSpExMriVJkqRMTK4lSZKkTEyuJUmSpExMrhcwMTFBf38/W7Zsob+/n4mJiaqbJEmSpA61uuoGdLKJiQlGR0cZHx/n8OHDrFq1iuHhYQCGhoYqbp0kSZI6jSPXLYyNjTE+Ps7g4CCrV69mcHCQ8fFxxsbGqm6aJEmSOpDJdQvT09MMDAwctWxgYIDp6emKWiRJkqROZnLdQl9fH1NTU0ctm5qaoq+vr6IWSZIkqZOZXLcwOjrK8PAwk5OTHDp0iMnJSYaHhxkdHa26aZIkSepAXtDYwsxFiyMjI0xPT9PX18fY2JgXM0qSJGlOJtcLGBoaYmhoiN27d7N58+aqmyNJkqQOZlmIJEmSlInJtSRJkpSJybUkSZKUicm1JEmSlInJtSRJkpSJybUkSZKUicm1JEmSlInJtSRJkpSJybUkSZKUicm1JEmSlInJtSRJkpSJybUkSZKUicm1JEmSlInJtSRJkpSJybUkSZKUyYqS64i4KiI+GREfj4i/i4iHZmqXJEmS1HVWOnJ9I9CfUvoR4FPAFStvkiRJktSdVpRcp5Ten1I6VPz3X4HTVt4kSZIkqTvlrLm+ALgh4/aOMjExQX9/P1u2bKG/v5+JiYmyQkmSJEnLEiml1i+I+ADwqDmeGk0pvad4zShwNvDsNM8GI+Ii4CKAU045ZdP111+/6Ebu2rWL8fFxLr30UjZs2MDevXu56qqrGB4eZsuWLYvezkrs37+fdevWtSWWcY1rXON2a2zjGte4xq1D3MHBwVtTSmfP+WRKaUUP4HzgX4AHL3adTZs2paXYuHFjuummm1JKKU1OTqaUUrrpppvSxo0bl7SdlZiJ227GNa5xjdtNsY1rXOMatw5xgVvSPHnu6mUm+wBExNOBy4GnpJS+uZJttTI9Pc3AwMBRywYGBpieni4rpCRJkrRkK625fiPwEODGiPhoRFydoU3H6OvrY2pq6qhlU1NT9PX1lRFOkiRJWpYVjVynlM7M1ZBWRkdHGR4eZnx8nMOHDzM5Ocnw8DBjY2PtCC9JkiQtyoqS63YZGhoCYGRkhOnpafr6+hgbGzuyXJIkSeoEXZFcQyPBHhoaYvfu3WzevLnq5kiSJEnHyDnPtSRJklRrJteSJElSJibXkiRJUiYm15IkSVImJteSJElSJibXkiRJUiYm15IkSVImkVJqf9CILwF3LXP1k4EvZ2yOcY1rXOP2WtwqYxvXuMY1bh3inpFSesRcT1SSXK9ERNySUjrbuMY1rnGN23mxjWtc4xq37nEtC5EkSZIyMbmWJEmSMunG5PpNxjWucY1r3I6NbVzjGte4tY7bdTXXkiRJUqfqxpFrSZIkqSN1ZHIdERsi4uaI+M+IeHtEnFAsj4j404j4dER8PCIe37TOWyLiixGxp11xI+IxETEZEdMRcXtEvLRNcddExEci4mNF3Ne0a5+b1lsVEf8eEe9rV9yIuDMiPhERH42IW9oY96ER8c6I+GTxu35S2XEj4geL/Zx5fD0ifrtN+/s7xftqT0RMRMSaNsV9aRHz9uXs6wJxfygi/iUiDkbEy2at8/SI+I+iTS9vY9wy+6w540b5fdZ8cbP0Wcs5zsXzZfVXrX6/ZfZXreKW2V/N9/stu79qtb+/E+X1V63iltlfPT8a/ePHI+LDEfHYpnXK7K9axS2zv5ozbmTqr5YZe2V9Vkqp4x7AO4DnFT9fDbyk+Plc4AYggCcCNzet81PA44E97YoLfA/w+OLnhwCfAs5qQ9wA1hU/Hw/cDDyxXce6eP4SYDvwvjb+ju8ETq7gvfVW4EXFzycAD23XcS5eswr4Ao05Nct+bz0a2As8qGn9F7Yhbj+wB3gwsBr4APD9GeM+EvgxYAx42axj+1/A9xa/24+R9294zrjFc2X2WfPtb9l91nxxs/RZyznOxfNl9Vetfr93Ul5/1Spumf1Vy+NcvKaM/mq+91XZ/dV8ccvur54MPKz4+Ry+00+W3V/NGbf4f5n91Xz7m6W/WmbsFfVZy/6jz/UA1gI7ijfJHuC5NCb0Xl08/yRgZ/HzXwBDTev+B/A9Tf9fv9hffM64TcvfA/xsO+PS+OO+Dfjxdu0zcBqwC/hpFvFhlTHunSzhwypHXOC7aHTeUcV7ulj2VOCf27S/jwY+BzycxofG+4CntiHuLwNvblr+u8BlueI2rXMlR39IHvUa4ArgirLjNi1fTwl91kJxm57P2mctJi6L7LNyxaXE/mqBuHdSUn/V4v1can+1yN9v9v6qxf6W2l+1iNuW/qpY/jDg7rleQ0n91ey4TcvWU2J/NV/cpucW7K/KiM0S8qyZx2qq93TgnpTSMwAi4gzgvpTSoeL5fTT+gOA7f0jMeu7zVceNiPXA42h8uyk9bkSsAm4FzgT+LKW0UNyc+/x64DIa3yQXI1fcBLw/IhLwFymlha7yzRH3EPAl4C+L00W3Ai9NKR1ow/7OeB4wscC+ZombUrolIv4Y+CxwP/D+lNL7y45LowMci4jvLuKeCyx0Kn0pceczV3t+vA1xlyNr3JL6rFbxltpn5drf11Nef9VKmf3VfL6XcvurxSijv5pTSunukvur+bSzvxqmcbYP2ttfNcddjqxxl9BfZYu9zDwL6Iya608APxMRWyPiJ4FvzvGaVPwbLZ6rLG5ErAP+FvjtlNLX2xE3pXQ4pfSjNEZlnhAR/QvEzRI7Ip4JfDGldOsi4mWLW/z7Eymlx9M4dfMbEfFTbYi7msapsD9PKT0OOAAsVOeW8711AvDzwN8sEDNL3Ih4GPAsYANwKrA2Il5QdtyU0jSwFbgR+EcaIw6H5njtcuPOZzl9So64y5Etbol91ryW0WetOG4b+qtWyuyv5lN2f9VSif3VfPHK7q/m1K7+KiIGaSR8l88sWmpbM8Vdjmxxl9hfZYu9zDwL6IDkOqX0KWATjYPxh8CvAw+NiJlR9dOAe4qf9wGPaVq9+blK4kbE8TR+6W9LKb2rXXGbtncfsJvGN7V2xP4J4Ocj4k7geuCnI+K6NsQlpTTz7xeBvwOe0Ia4+4B9Td9Y30njw6v0/S2cA9yWUvrvVjEzxv0ZYG9K6UsppQeAd9GoSSt9f1NK4ymlx6eUfgq4F/jPjHHns+Q+JVPcJcsVt+Q+azH7cR+L6LMyxS27v2q1nTL7q/mU3V8tpKz+aj5l91ettlNqfxURPwK8GXhWSukrxeLS+6t54i5ZrrhL7a9yxm7a3n0sMs+aUXlyHRGnAt9MKV0H/DGNYf9J4JeKl5xPo84G4O+BX42GJwJfSyktpyQkS9yICGAcmE4p/Ukb4z4iIh5abO9BNDqYT7YjdkrpipTSaSml9TRO/92UUmo5UpBpn9dGxEOK7a2lUdfX8qrlTPv7BeBzEfGDxeu2AHeUHbdpc0Ms7hRrrrifBZ4YEQ8u3t9bgOl27G9EPLL493Tg2Qvt9xLjzuffgO+PxpXkJ9B4T/99G+IuWY64beiz5tvGkvusHHHb0F/Nt42y+6s5taG/WkhZ/dV8yu6vWm2ntP6q2Oa7gF8pEsUZpfZXLeIuWY64y+mvMsZeVp51RFpkcXZZD+BpwMeBj9J445xNo27sI8CnaZxeOrF4bQB/RuNq2U8AZzdtZ4JGneoDNL7dDZcdFxigcWphZjsfBc5tQ9wfAf692M4e4FXtPNZN29vM4i4QyrHP30vj1NvHgNuB0Ta+t36URj3dx4F3U1xZ3Ia4Dwa+ApzU5r+l19DoRPYAfz2zThvifohGIvAxYEvm/X0UjX7h68B9xc/fVTx3Lo2r0P+rhPdVq7hl9llzxqX8Pmu+uEvus3Id55L7q/n2t+z+qtX76kcpr79qFbfM/qpV3DL7q1Zxy+yv3gx8le/8jd7StJ0y+6tWccvsr+aMyzL6q4yxl5VnzTy8Q6MkSZKUSeVlIZIkSVKvMLmWJEmSMjG5liRJkjIxuZYkSZIyMbmWJEmSMjG5lqQOFhHrI+L+iPjoMta9MiJeNsfyUyPincXPPxkRd0REy3mYJUmLY3ItSZ3vv1LjNrxZpJTuSSn9UvHzh2jMnStJysDkWpK6RHHnvx0R8bGI2BMRzy2W3xkRJxc/nx0Ru5tWe2xE3BQR/xkRFxavWe9ItSSVY/XCL5EkdYinA/eklJ4BEBEnLWKdHwGeCKwF/j0idpTYPkmqPUeuJal7fAL4mYjYGhE/mVL62iLWeU9K6f6U0peBSeAJ5TZRkurN5FqSukRK6VPAJhpJ9h9GxKuKpw7xnf58zezVFvi/JCkjk2tJ6hIRcSrwzZTSdcAfA48vnrqTRtIN8L9mrfasiFgTEd8NbAb+rQ1NlaTasuZakrrHDwNXRcT/AA8ALymWvwYYj4hXADfPWucjwA7gdOC1KaV7ImI9jmBLUilMriWpS6SUdgI751j+IeAH5lh+5Tyb+m7g3qyNkyQBloVIUqc7DJy0nJvIzCUizgYmgDcU//9J4L3Al3NsX5LqLlLyzKAkSZKUgyPXkiRJUiYm15IkSVImJteSJElSJibXkiRJUiYm15IkSVImJteSJElSJv8fj7ClFcYFjVcAAAAASUVORK5CYII=\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
\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
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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/11_Across_Subject_Analyses_withEdits.ipynb b/CS4500_CompMethods/lessons/11_Across_Subject_Analyses_withEdits.ipynb new file mode 100644 index 0000000..fe198da --- /dev/null +++ b/CS4500_CompMethods/lessons/11_Across_Subject_Analyses_withEdits.ipynb @@ -0,0 +1,2563 @@ +{ + "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": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0m\u001b[01;34ms001\u001b[0m/ \u001b[01;34ms004\u001b[0m/ \u001b[01;34ms007\u001b[0m/ \u001b[01;34ms010\u001b[0m/ \u001b[01;34ms013\u001b[0m/ \u001b[01;34ms016\u001b[0m/ \u001b[01;34ms019\u001b[0m/ \u001b[01;34ms022\u001b[0m/\r\n", + "\u001b[01;34ms002\u001b[0m/ \u001b[01;34ms005\u001b[0m/ \u001b[01;34ms008\u001b[0m/ \u001b[01;34ms011\u001b[0m/ \u001b[01;34ms014\u001b[0m/ \u001b[01;34ms017\u001b[0m/ \u001b[01;34ms020\u001b[0m/ \u001b[01;34ms023\u001b[0m/\r\n", + "\u001b[01;34ms003\u001b[0m/ \u001b[01;34ms006\u001b[0m/ \u001b[01;34ms009\u001b[0m/ \u001b[01;34ms012\u001b[0m/ \u001b[01;34ms015\u001b[0m/ \u001b[01;34ms018\u001b[0m/ \u001b[01;34ms021\u001b[0m/\r\n" + ] + } + ], + "source": [ + "ls data/Taskapalooza/" + ] + }, + { + "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": 3, + "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": 3, + "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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" + ], + "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": 7, + "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.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')\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": 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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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": 10, + "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": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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nu7cdZvW0/3F6IjMf7/97Crid3kupvdSknxuA2/ovAx8CHqW3R1l5jQb1NItrBA16yswnM/OGzHwV8HP09vofbTJ3D4zTz6yu0TCDeh5tfaa00X8x8AjwMr6zYf/yXcbtp7eXdGnbudO+jNnTpcBl53z9T/R+U+RM9wP8EfBb/a87wAl6v4mt7Bqdp6eZW6MWPV3Od96c/QV6+68z+X9pzH5mco369Swy+I3NQ3z3G5tfGGd9ptnUm4B/pffu66/3b3sv8N5zxrwbWGsydxYuo/ZE793n+/qX+2elp2H9AC8CPk3vJe0x4J3V12hQT7O6Rg17eh3wb8CDwG3A82d5nUbtZ1bXiN6r7q8D/0vv2fV7dvQT9P7QzsP977vuOOvjafeSVJhnbEpSYYa4JBVmiEtSYYa4JBVmiEtSYYa4JBVmiEtSYf8PYeT0IdXB2JcAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# quick plot of performance\n", + "mperf['mean'].hist(bins='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "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
s001congruent94960.9791675.877960e-26
incongruent80960.8333331.037205e-11
neutral95960.9895831.224312e-27
s002congruent94960.9791675.877960e-26
incongruent91960.9479178.152854e-22
..................
s022incongruent89960.9270831.629576e-19
neutral95960.9895831.224312e-27
s023congruent92960.9583334.379121e-23
incongruent36960.3750009.948270e-01
neutral90960.9375001.251628e-20
\n", + "

69 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " sum count mean binom_pval\n", + "subj condition \n", + "s001 congruent 94 96 0.979167 5.877960e-26\n", + " incongruent 80 96 0.833333 1.037205e-11\n", + " neutral 95 96 0.989583 1.224312e-27\n", + "s002 congruent 94 96 0.979167 5.877960e-26\n", + " incongruent 91 96 0.947917 8.152854e-22\n", + "... ... ... ... ...\n", + "s022 incongruent 89 96 0.927083 1.629576e-19\n", + " neutral 95 96 0.989583 1.224312e-27\n", + "s023 congruent 92 96 0.958333 4.379121e-23\n", + " incongruent 36 96 0.375000 9.948270e-01\n", + " neutral 90 96 0.937500 1.251628e-20\n", + "\n", + "[69 rows x 4 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fperf = df_f.groupby(['subj', 'condition'])['correct'].agg(['sum', 'count', 'mean'])\n", + "fperf.head()\n", + "fperf['binom_pval'] = fperf.apply(lambda x: stats.binom_test(x['sum'], n=x['count'], \n", + " p=0.5, alternative='greater'),\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": 64, + "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": 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": 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": 65, + "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": 100, + "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": 100, + "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": 101, + "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: 13:21:22 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: 13:21:22 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": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# build a logistic 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": "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
\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
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
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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": 120, + "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: 13:48:02 Log-Likelihood: 3.4100
No. Observations: 69 AIC: -0.8201
Df Residuals: 66 BIC: 5.882
Df Model: 2
Covariance Type: nonrobust
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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
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
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: 13:48:02 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": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum_df = df_f.loc[df_f['correct']].groupby(['subj', 'condition'])['log_rt'].mean().reset_index()\n", + "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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/12_Recognition_Memory_withEdits.ipynb b/CS4500_CompMethods/lessons/12_Recognition_Memory_withEdits.ipynb new file mode 100644 index 0000000..c0790ba --- /dev/null +++ b/CS4500_CompMethods/lessons/12_Recognition_Memory_withEdits.ipynb @@ -0,0 +1,2441 @@ +{ + "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": 1, + "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": 2, + "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": 3, + "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": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the data from each task\n", + "task_dir = os.path.join('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", + "df_i.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Some data clean-up" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "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": 23, + "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": 23, + "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": 24, + "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": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# must unstack and reset index to plot properly\n", + "res.unstack().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Performance')" + ] + }, + "execution_count": 25, + "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": { + "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": 26, + "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": 26, + "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": 51, + "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "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": 38, + "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "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": 39, + "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "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": 45, + "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "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": 59, + "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 59, + "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": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 74, + "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": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 71, + "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": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 77, + "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": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 81, + "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": 132, + "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": 136, + "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": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# use the agg method to get the counts\n", + "iperf = df_i.groupby(['subj', 'cond', 'in_out', 'novelty'])['old_resp'].agg(['sum', 'count', 'mean'])\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": 137, + "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": 137, + "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": 141, + "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
\n", + "
" + ], + "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": 141, + "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": 142, + "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 142, + "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": 143, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.formula.api as smf" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "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, 12 Nov 2020 Prob (F-statistic): 0.376
Time: 13:45:31 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, 12 Nov 2020 Prob (F-statistic): 0.376\n", + "Time: 13:45:31 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": 144, + "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": 146, + "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": 146, + "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": 147, + "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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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 147, + "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 = \"bias(c)\", fill='Location')\n", + " )\n", + "p" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "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: c R-squared: 0.093
Model: OLS Adj. R-squared: 0.062
Method: Least Squares F-statistic: 3.013
Date: Thu, 12 Nov 2020 Prob (F-statistic): 0.0343
Time: 13:50:18 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
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
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, 12 Nov 2020 Prob (F-statistic): 0.0343\n", + "Time: 13:50:18 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": 149, + "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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/13_Bayesian_LMER_withEdits.ipynb b/CS4500_CompMethods/lessons/13_Bayesian_LMER_withEdits.ipynb new file mode 100644 index 0000000..f8305db --- /dev/null +++ b/CS4500_CompMethods/lessons/13_Bayesian_LMER_withEdits.ipynb @@ -0,0 +1,2102 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Bayesian Linear Mixed-Effects Regression\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": [ + "# Course Evaluations\n", + "\n", + "- Please fill our your ***anonymous*** course evaluations, including custom comments if you can. \n", + "- I really do pay attention to your feedback and will use your suggestions to improve the course.\n", + "- It's good to hear both aspects of the class you liked and thought worked well, as well as those you disliked and think should change (perhaps even with a suggestion for *how* they should change!)\n", + "\n", + "### Thank you!!!\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# A reminder about grading\n", + "\n", + "The class is graded out of a total of 100 points, translated to the standard grading scale:\n", + "\n", + "- Lesson exercises / class participation (30 pts)\n", + " - There have been 16 points worth of exercises\n", + " - Everyone will receive 15 free points for class participation\n", + " - So you'll have one point to help you make up something you missed\n", + "- List generation project (20 pts)\n", + "- Experiment project (20 pts)\n", + "- Data Analysis project (30 pts)\n", + " - See next slide..." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Final Project\n", + "\n", + "The final project is a synthesis of the experiment development, data analysis, and visualization we've covered in class. The goal is to perform an analysis on one of the experiments from the class, producing a notebook with part of what would end up in an actual manuscript submission.\n", + "\n", + "Your notebook will include:\n", + "\n", + "- Methods\n", + " - A write-up of the task design and methods for data collection\n", + "- Results\n", + " - Plots and statistics organized by the question you are asking\n", + " - A short discussion of each plot/statistic stating what you found\n", + "\n", + "We will provide a full description of the final project as a separate notebook, outlining each of these pieces in detail.\n", + "\n", + "Also, even though we don't have class, we will be continuing office hours (except for the Wednesday before Thanksgiving), so feel free to come chat with us or set up a time to meet via Slack.\n", + "\n", + "***Due Date: By 11:59 pm on Tuesday, Dec. 8th, which is the day our final would be scheduled.***" + ] + }, + { + "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. Quick ANOVA example\n", + "2. Introduction to mixed-effects models\n", + "3. Introduction to Bayesian models\n", + "4. Fit and visualize a Bayesian mixed-effects regression to our data\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# New library to install\n", + "\n", + "You're going to need new Bayesian modeling and plotting libraries, so run these lines at your Anaconda Prompt/Terminal:\n", + "\n", + "```bash\n", + "conda install -c conda-forge arviz\n", + "conda install patsy pymc3 pystan\n", + "pip install bambi\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "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", + "import arviz as az\n", + "import bambi as bmb\n", + "import statsmodels.formula.api as smf\n", + "import statsmodels.api as sm\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": 2, + "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": 3, + "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
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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": 3, + "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_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": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" + ] + } + ], + "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": [ + "# Word Recognition\n", + "\n", + "Primary question: Is there an effect of valence (potentially interacting with condition) on recognition accuracy?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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condnoveltyvalencemeanstdsemcilen
0mixedlureneg0.8831520.3360970.0123890.024321736.0
1mixedlureneu0.9021740.3037740.0111970.021982736.0
2mixedlurepos0.8790760.3311840.0122080.023966736.0
3mixedtargetneg0.7826090.4209880.0155180.030465736.0
4mixedtargetneu0.7717390.4291930.0158200.031058736.0
5mixedtargetpos0.7296200.4590920.0169220.033222736.0
6purelureneg0.8659420.3461200.0104170.0204391104.0
7purelureneu0.8740940.3315260.0099780.0195781104.0
8purelurepos0.8641300.3441180.0103570.0203211104.0
9puretargetneg0.7943840.4118450.0123950.0243211104.0
10puretargetneu0.7853260.4189450.0126090.0247401104.0
11puretargetpos0.7735510.4206220.0126590.0248391104.0
\n", + "
" + ], + "text/plain": [ + " cond novelty valence mean std sem ci len\n", + "0 mixed lure neg 0.883152 0.336097 0.012389 0.024321 736.0\n", + "1 mixed lure neu 0.902174 0.303774 0.011197 0.021982 736.0\n", + "2 mixed lure pos 0.879076 0.331184 0.012208 0.023966 736.0\n", + "3 mixed target neg 0.782609 0.420988 0.015518 0.030465 736.0\n", + "4 mixed target neu 0.771739 0.429193 0.015820 0.031058 736.0\n", + "5 mixed target pos 0.729620 0.459092 0.016922 0.033222 736.0\n", + "6 pure lure neg 0.865942 0.346120 0.010417 0.020439 1104.0\n", + "7 pure lure neu 0.874094 0.331526 0.009978 0.019578 1104.0\n", + "8 pure lure pos 0.864130 0.344118 0.010357 0.020321 1104.0\n", + "9 pure target neg 0.794384 0.411845 0.012395 0.024321 1104.0\n", + "10 pure target neu 0.785326 0.418945 0.012609 0.024740 1104.0\n", + "11 pure target pos 0.773551 0.420622 0.012659 0.024839 1104.0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 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', 'novelty', 'valence'], # list of columns for grouping within subject\n", + " measvar='correct') # dependent variable averaging over\n", + "res = res.reset_index()\n", + "res" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "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('~novelty')\n", + " + pn.labs(x=\"Condition\", y = \"P(correct)\", fill='Valence')\n", + " )\n", + "p" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Testing for significance\n", + "\n", + "Using a standard linear model, we must average performance within subject\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjcondvalencenoveltycorrect
0s001mixedneglure0.937500
1s001mixednegtarget0.812500
2s001mixedneulure0.906250
3s001mixedneutarget0.875000
4s001mixedposlure0.968750
..................
271s023purenegtarget0.625000
272s023pureneulure0.687500
273s023pureneutarget0.791667
274s023pureposlure0.895833
275s023purepostarget0.708333
\n", + "

276 rows × 5 columns

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" + ], + "text/plain": [ + " subj cond valence novelty correct\n", + "0 s001 mixed neg lure 0.937500\n", + "1 s001 mixed neg target 0.812500\n", + "2 s001 mixed neu lure 0.906250\n", + "3 s001 mixed neu target 0.875000\n", + "4 s001 mixed pos lure 0.968750\n", + ".. ... ... ... ... ...\n", + "271 s023 pure neg target 0.625000\n", + "272 s023 pure neu lure 0.687500\n", + "273 s023 pure neu target 0.791667\n", + "274 s023 pure pos lure 0.895833\n", + "275 s023 pure pos target 0.708333\n", + "\n", + "[276 rows x 5 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# use the agg method to get the means\n", + "perf = df_w.groupby(['subj', 'cond', 'valence', 'novelty'])['correct'].mean()\n", + "perf = perf.reset_index()\n", + "perf" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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: 0.165
Model: OLS Adj. R-squared: 0.130
Method: Least Squares F-statistic: 4.738
Date: Mon, 07 Dec 2020 Prob (F-statistic): 1.23e-06
Time: 21:22:03 Log-Likelihood: 183.23
No. Observations: 276 AIC: -342.5
Df Residuals: 264 BIC: -299.0
Df Model: 11
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept 0.8832 0.027 33.251 0.000 0.831 0.935
cond[T.pure] -0.0172 0.038 -0.458 0.647 -0.091 0.057
valence[T.neu] 0.0190 0.038 0.506 0.613 -0.055 0.093
valence[T.pos] -0.0041 0.038 -0.109 0.914 -0.078 0.070
novelty[T.target] -0.1005 0.038 -2.677 0.008 -0.175 -0.027
cond[T.pure]:valence[T.neu] -0.0109 0.053 -0.205 0.838 -0.115 0.094
cond[T.pure]:valence[T.pos] 0.0023 0.053 0.043 0.966 -0.102 0.107
cond[T.pure]:novelty[T.target] 0.0290 0.053 0.546 0.586 -0.076 0.134
valence[T.neu]:novelty[T.target] -0.0299 0.053 -0.563 0.574 -0.134 0.075
valence[T.pos]:novelty[T.target] -0.0489 0.053 -0.921 0.358 -0.154 0.056
cond[T.pure]:valence[T.neu]:novelty[T.target] 0.0127 0.075 0.169 0.866 -0.135 0.161
cond[T.pure]:valence[T.pos]:novelty[T.target] 0.0299 0.075 0.398 0.691 -0.118 0.178
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Omnibus: 29.804 Durbin-Watson: 1.533
Prob(Omnibus): 0.000 Jarque-Bera (JB): 36.051
Skew: -0.843 Prob(JB): 1.48e-08
Kurtosis: 3.542 Cond. No. 25.6


Warnings:
[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: 0.165\n", + "Model: OLS Adj. R-squared: 0.130\n", + "Method: Least Squares F-statistic: 4.738\n", + "Date: Mon, 07 Dec 2020 Prob (F-statistic): 1.23e-06\n", + "Time: 21:22:03 Log-Likelihood: 183.23\n", + "No. Observations: 276 AIC: -342.5\n", + "Df Residuals: 264 BIC: -299.0\n", + "Df Model: 11 \n", + "Covariance Type: nonrobust \n", + "=================================================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "-----------------------------------------------------------------------------------------------------------------\n", + "Intercept 0.8832 0.027 33.251 0.000 0.831 0.935\n", + "cond[T.pure] -0.0172 0.038 -0.458 0.647 -0.091 0.057\n", + "valence[T.neu] 0.0190 0.038 0.506 0.613 -0.055 0.093\n", + "valence[T.pos] -0.0041 0.038 -0.109 0.914 -0.078 0.070\n", + "novelty[T.target] -0.1005 0.038 -2.677 0.008 -0.175 -0.027\n", + "cond[T.pure]:valence[T.neu] -0.0109 0.053 -0.205 0.838 -0.115 0.094\n", + "cond[T.pure]:valence[T.pos] 0.0023 0.053 0.043 0.966 -0.102 0.107\n", + "cond[T.pure]:novelty[T.target] 0.0290 0.053 0.546 0.586 -0.076 0.134\n", + "valence[T.neu]:novelty[T.target] -0.0299 0.053 -0.563 0.574 -0.134 0.075\n", + "valence[T.pos]:novelty[T.target] -0.0489 0.053 -0.921 0.358 -0.154 0.056\n", + "cond[T.pure]:valence[T.neu]:novelty[T.target] 0.0127 0.075 0.169 0.866 -0.135 0.161\n", + "cond[T.pure]:valence[T.pos]:novelty[T.target] 0.0299 0.075 0.398 0.691 -0.118 0.178\n", + "==============================================================================\n", + "Omnibus: 29.804 Durbin-Watson: 1.533\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 36.051\n", + "Skew: -0.843 Prob(JB): 1.48e-08\n", + "Kurtosis: 3.542 Cond. No. 25.6\n", + "==============================================================================\n", + "\n", + "Warnings:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# build a linear regression of the full model\n", + "m0 = smf.ols(\"correct ~ cond * valence * novelty\", perf).fit()\n", + "m0.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Running an ANOVA on the linear model results\n", + "\n", + "- Such a complicated linear model is really 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", + "- Statsmodels provides a way to handle that for you." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sum_sqdfFPR(>F)
cond0.0001571.00.0096929.216506e-01
valence0.0267712.00.8249994.393621e-01
novelty0.7639661.047.0855074.826049e-11
cond:valence0.0060512.00.1864788.299863e-01
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cond:valence:novelty0.0025882.00.0797679.233540e-01
Residual4.283420264.0NaNNaN
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" + ], + "text/plain": [ + " sum_sq df F PR(>F)\n", + "cond 0.000157 1.0 0.009692 9.216506e-01\n", + "valence 0.026771 2.0 0.824999 4.393621e-01\n", + "novelty 0.763966 1.0 47.085507 4.826049e-11\n", + "cond:valence 0.006051 2.0 0.186478 8.299863e-01\n", + "cond:novelty 0.032157 1.0 1.981955 1.603610e-01\n", + "valence:novelty 0.013930 2.0 0.429267 6.514397e-01\n", + "cond:valence:novelty 0.002588 2.0 0.079767 9.233540e-01\n", + "Residual 4.283420 264.0 NaN NaN" + ] + }, + "execution_count": 9, + "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": [ + "- Looks like a big effect of novelty (target vs. lure), but not much else.\n", + "- This isn't a huge surprise from that graph" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## So what's the problem?\n", + "\n", + "- If you average data within subject, what happened to the within-subject variability in performance?\n", + " - It's gone!\n", + "- It could be that the entire effect is driven by item outliers (see Clark, 1993)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## An item effect\n", + "\n", + "* Some average subject data\n", + "\n", + "| Subject | Cond 1 | Cond 2 |\n", + "| :- | -: | -: |\n", + "| Pat | 400ms | 500ms\n", + "| Sam | 420ms | 530ms\n", + "| Robin | 380ms | 490ms\n", + "\n", + "\n", + "* Some average item data\n", + "\n", + "| Item | Cond 1 | Cond 2 |\n", + "| :- | -: | -: |\n", + "| chicken | 390ms | 395ms |\n", + "| octopus | 410ms | **1100ms** |\n", + "| horse | 380ms | 400ms |\n", + "\n", + "\n", + "* The effect is driven by a single item, \"octopus\"!\n", + "\n", + "* This is why it's now common practice to include both subject and\n", + " item analyses.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Subjects and Items\n", + "\n", + "- What if you could account for subject and item-level variability\n", + " *at the same time*?\n", + "\n", + "- Mixed effects models allow just that, and you get all that and more!\n", + " You can include:\n", + "\n", + " - Continuous dependent and independent variables.\n", + "\n", + " - Interactions between any combo of continuous and discrete\n", + " variables.\n", + "\n", + "\n", + "- This means we can analyze the actual valence values instead of the groups!\n", + "- You are also working with the raw data instead of summary statistics!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Mixed Effects\n", + "\n", + "* Split your model into **random** and **fixed** effects.\n", + "\n", + "* For **random** effects we expect random variation across that\n", + " variable (e.g., subjects, items, etc...)\n", + "\n", + "* For **fixed** effects you expect systematic changes between\n", + " conditions (i.e. your experimental manipulations).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Bayesian models\n", + "\n", + "- As mentioned last class, it is possible to build models of the world out of probability distributions.\n", + "- The process involves:\n", + " - Determining the correct probability distribution function for your observed data\n", + " - Then finding the parameters of that function to maximize the probability of observing your data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Visualizing the fitting process\n", + "\n", + "Say we have some data, can we figure out what model and parameters could have generated those data?" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.array([3.39317851, 0.92986704, 2.6072234 , 1.63686968, 2.69860129,\n", + " 2.3531901 , 2.67225635, 1.93437793, 2.0751784 , 3.71950222,\n", + " 1.39434613, 2.37011522, 0.81351156, 2.36217543, 3.30390727,\n", + " 2.78832169, 2.11905216, 1.48325417, 0.73556411, 2.36148697])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":11: UserWarning: In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Log Likelihood: -24.298')" + ] + }, + "execution_count": 11, + "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 start by visulizing our data\n", + "plt.hist(data, bins='auto', alpha=.3, lw=2, edgecolor='k', density=True);\n", + "plt.vlines(data, ymin=0, ymax=0.05)\n", + "\n", + "# adjust the mean (mu) and standard deviation (sigma) to maximize the log likelihood\n", + "mu = 2.19\n", + "sigma = 0.8\n", + "xvals = np.linspace(0, 5, 100)\n", + "d = dists.norm(loc=mu, scale=sigma)\n", + "plt.plot(xvals, d.pdf(xvals), lw=2)\n", + "plt.stem(data, d.pdf(data))\n", + "plt.title('Log Likelihood: %.3f' % np.log(d.pdf(data)).sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Maximum Likelihood\n", + "\n", + "We can calculate parameters that maximize the likelihood of observing the data given the model and those parameters:\n", + "\n", + "$$\\hat{L} = P(D \\mid \\hat{\\theta}, M)$$\n", + "\n", + "However, this approach ignores the fact that there are many parameter values that *could* have generated the same data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Bayesian Inference\n", + "\n", + "Instead, what we'd really like to know is the full set of possible parameters given the data:\n", + "\n", + "$$P(\\theta \\mid D)$$\n", + "\n", + "This should look familiar and a probability reminder should give a hint for how to calculate it..." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Basics of Probability\n", + "\n", + "\n", + "\n", + "### A \n", + "$P(A) \\in [0, 1]$\n", + "\n", + "### not A\n", + "$1 - P(A)$\n", + "\n", + "### A or B \n", + "$P(A \\cup B) = P(A) + P(B) - P(A \\cap B)$ or
$P(A \\cup B) = P(A) + P(B)$ if A and B are mutually exclusive\n", + "\n", + "### A and B\n", + "$P(A \\cap B) = P(A \\mid B) P(B) = P(B \\mid A) P(A)$ or
$P(A \\cap B) = P(A) P(B)$ if A and B are independent\n", + "\n", + "### A given B \n", + "$P(A \\mid B) = \\frac{P(A \\cap B)}{P(B)} = \\frac{P(B \\mid A) P(A)}{P(B)}$\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Bayes Rule\n", + "\n", + "$$P(\\theta \\mid D) = \\frac{P(\\theta \\cap D)}{P(D)} = \\frac{P(D \\mid \\theta) P(\\theta)}{P(D)} \\propto P(D \\mid \\theta) P(\\theta)$$\n", + "\n", + "- $P(\\theta \\mid D)$ is the posterior probability AKA the likelihood of observing each of the probability values given the data. Initial belief of what the data is based on prior experience and prior knowledge\n", + "- $P(D \\mid \\theta)$ is the likelihood AKA the probability of observing the data given any set of data\n", + "- $P(\\theta)$ is the prior probability AKA the probabilty of observing any of the specific data values (weird to see 10 second reaction time)\n", + " - Theory of memory relying on past experiences. Can always be updated.\n", + "- $P(D)$ is the marginal likelihood (*does not depend on model or params*) AKA the probability of just observing the data being seen at all, independent of the model (the most impossible to quantify)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What's a prior?\n", + "\n", + "A prior probability represents the initial belief in the potential values of some quantity before any new evidence is taken into account.\n", + "\n", + "The notion of a prior is one of the key differences between Bayesian and Frequentist approaches. Bayesians believe that we should take such prior knowledge into account when making decisions and that this prior knowledge can be updated with each new bit of evidence:\n", + "\n", + "\"Today's posterior is tomorrow's prior\"\n", + "\n", + "That said, priors are also a major critique of Bayesian approaches because they can have a massive effect on the posterior (i.e., the conclusions you draw)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Bayesian Inference\n", + "\n", + "Determining $P(\\theta \\mid D)$ can be difficult for a number of reasons:\n", + "\n", + "- The likelihood $P(D \\mid \\theta)$ is often intractable, requiring simulation\n", + "- Can be unfeasible/impossible to determine the marginal likelihood ($P(D)$)\n", + "\n", + "The standard approach is to use Markov chain Monte Carlo (MCMC) to estimate the posterior.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Bayesian Mixed-effects Regression\n", + "\n", + "- We can use hierarchical Bayesian approaches to build regression models that predict trial-level data taking into account both subject-level and item-level variability.\n", + "\n", + "- The *BAyesian Model-Building Interface ([Bambi](https://bambinos.github.io/bambi/index.html))* package helps us build and perform parameter inference on mixed-effects models.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Treating valence as continuous" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\plotnine\\stats\\stat_bin.py:93: PlotnineWarning: 'stat_bin()' using 'bins = 22'. Pick better value with 'binwidth'.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# Show the distributions of valence values\n", + "p = (pn.ggplot(df_w, pn.aes('valence_mean', fill='valence'))\n", + " + pn.geom_histogram(alpha=.8)\n", + " )\n", + "p" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = (pn.ggplot(df_w.loc[df_w['novelty']=='target'], pn.aes('valence_mean', 'correct'))\n", + " + pn.geom_smooth(method='loess')\n", + " )\n", + "p" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Build and fit the model" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:2963: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\bambi\\models.py:267: UserWarning: Modeling the probability that correct=='1'\n", + "Auto-assigning NUTS sampler...\n", + "INFO:pymc3:Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "INFO:pymc3:Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "INFO:pymc3:Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [1|description_offset, 1|description_sd, 1|subj_offset, 1|subj_sd, cond:valence_mean, valence_mean, cond, Intercept]\n", + "INFO:pymc3:NUTS: [1|description_offset, 1|description_sd, 1|subj_offset, 1|subj_sd, cond:valence_mean, valence_mean, cond, Intercept]\n", + "Sampling 4 chains, 0 divergences: 100%|█████████████████████████| 6000/6000 [03:33<00:00, 28.13draws/s]\n", + "The number of effective samples is smaller than 25% for some parameters.\n", + "INFO:pymc3:The number of effective samples is smaller than 25% for some parameters.\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:87: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n" + ] + } + ], + "source": [ + "# First initialize the model with the data frame we're using\n", + "model = bmb.Model(df_w.loc[df_w['novelty']=='target'])\n", + "\n", + "# next build the regression with both fixed and random effects\n", + "results = model.fit('correct ~ cond * valence_mean', \n", + " random=['1|subj', '1|description'], \n", + " family='bernoulli')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the inference traces for the fit\n", + "az.plot_trace(results, compact=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Significance involves evaluating the posteriors" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the fixed effect posteriors to examine significance\n", + "az.plot_posterior(results, ref_val=0.0, var_names=['cond', 'valence_mean', 'cond:valence_mean']);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Perform model comparison to determine what parameters to keep\n", + "\n", + "- Not all the parameters of the model help with the fit\n", + "- We can perform Bayesian model comparison to identify the simplest model that best captures the data" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/per/anaconda3/lib/python3.7/site-packages/bambi/models.py:269: UserWarning: Modeling the probability that correct=='1'\n", + " self.y.name, str(self.clean_data[self.y.name].iloc[event])\n", + "Auto-assigning NUTS sampler...\n", + "INFO:pymc3:Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "INFO:pymc3:Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "INFO:pymc3:Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [1|subj_offset, 1|subj_sd, cond:valence_mean, valence_mean, cond, Intercept]\n", + "INFO:pymc3:NUTS: [1|subj_offset, 1|subj_sd, cond:valence_mean, valence_mean, cond, Intercept]\n", + "Sampling 4 chains, 0 divergences: 100%|██████████| 6000/6000 [00:28<00:00, 210.09draws/s]\n", + "The acceptance probability does not match the target. It is 0.8833219873387895, but should be close to 0.8. Try to increase the number of tuning steps.\n", + "WARNING:pymc3:The acceptance probability does not match the target. It is 0.8833219873387895, but should be close to 0.8. Try to increase the number of tuning steps.\n", + "The number of effective samples is smaller than 25% for some parameters.\n", + "INFO:pymc3:The number of effective samples is smaller than 25% for some parameters.\n", + "/home/per/anaconda3/lib/python3.7/site-packages/arviz/data/io_pymc3.py:91: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + " FutureWarning,\n" + ] + } + ], + "source": [ + "# next build the regression without the item random effect\n", + "res_noitem = model.fit('correct ~ cond * valence_mean', \n", + " random=['1|subj'], \n", + " family='bernoulli')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/per/anaconda3/lib/python3.7/site-packages/bambi/models.py:269: UserWarning: Modeling the probability that correct=='1'\n", + " self.y.name, str(self.clean_data[self.y.name].iloc[event])\n", + "Auto-assigning NUTS sampler...\n", + "INFO:pymc3:Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "INFO:pymc3:Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "INFO:pymc3:Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [1|subj_offset, 1|subj_sd, valence_mean, cond, Intercept]\n", + "INFO:pymc3:NUTS: [1|subj_offset, 1|subj_sd, valence_mean, cond, Intercept]\n", + "Sampling 4 chains, 0 divergences: 100%|██████████| 6000/6000 [00:17<00:00, 341.31draws/s]\n", + "The number of effective samples is smaller than 25% for some parameters.\n", + "INFO:pymc3:The number of effective samples is smaller than 25% for some parameters.\n", + "/home/per/anaconda3/lib/python3.7/site-packages/arviz/data/io_pymc3.py:91: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + " FutureWarning,\n" + ] + } + ], + "source": [ + "# next build the regression without the interaction\n", + "res_nointer = model.fit('correct ~ cond + valence_mean', \n", + " random=['1|subj'], \n", + " family='bernoulli')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/per/anaconda3/lib/python3.7/site-packages/bambi/models.py:269: UserWarning: Modeling the probability that correct=='1'\n", + " self.y.name, str(self.clean_data[self.y.name].iloc[event])\n", + "Auto-assigning NUTS sampler...\n", + "INFO:pymc3:Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "INFO:pymc3:Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "INFO:pymc3:Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [1|subj_offset, 1|subj_sd, valence_mean, Intercept]\n", + "INFO:pymc3:NUTS: [1|subj_offset, 1|subj_sd, valence_mean, Intercept]\n", + "Sampling 4 chains, 0 divergences: 100%|██████████| 6000/6000 [00:18<00:00, 327.48draws/s]\n", + "The number of effective samples is smaller than 25% for some parameters.\n", + "INFO:pymc3:The number of effective samples is smaller than 25% for some parameters.\n", + "/home/per/anaconda3/lib/python3.7/site-packages/arviz/data/io_pymc3.py:91: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + " FutureWarning,\n" + ] + } + ], + "source": [ + "# next build the regression with only valence\n", + "res_valence = model.fit('correct ~ valence_mean', \n", + " random=['1|subj'], \n", + " family='bernoulli')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/per/anaconda3/lib/python3.7/site-packages/bambi/models.py:269: UserWarning: Modeling the probability that correct=='1'\n", + " self.y.name, str(self.clean_data[self.y.name].iloc[event])\n", + "Auto-assigning NUTS sampler...\n", + "INFO:pymc3:Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "INFO:pymc3:Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "INFO:pymc3:Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [1|subj_offset, 1|subj_sd, cond, Intercept]\n", + "INFO:pymc3:NUTS: [1|subj_offset, 1|subj_sd, cond, Intercept]\n", + "Sampling 4 chains, 0 divergences: 100%|██████████| 6000/6000 [00:12<00:00, 496.11draws/s]\n", + "The acceptance probability does not match the target. It is 0.8789515664864597, but should be close to 0.8. Try to increase the number of tuning steps.\n", + "WARNING:pymc3:The acceptance probability does not match the target. It is 0.8789515664864597, but should be close to 0.8. Try to increase the number of tuning steps.\n", + "The number of effective samples is smaller than 25% for some parameters.\n", + "INFO:pymc3:The number of effective samples is smaller than 25% for some parameters.\n", + "/home/per/anaconda3/lib/python3.7/site-packages/arviz/data/io_pymc3.py:91: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + " FutureWarning,\n" + ] + } + ], + "source": [ + "# next build the regression with only cond\n", + "res_cond = model.fit('correct ~ cond', \n", + " random=['1|subj'], \n", + " family='bernoulli')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Compare the models\n", + "\n", + "- The model with the fewest parameters that is not significantly worse than the other models is the best to keep." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/per/anaconda3/lib/python3.7/site-packages/arviz/stats/stats.py:150: UserWarning: \n", + "The scale is now log by default. Use 'scale' argument or 'stats.ic_scale' rcParam if you rely on a specific value.\n", + "A higher log-score (or a lower deviance) indicates a model with better predictive accuracy.\n", + " \"\\nThe scale is now log by default. Use 'scale' argument or \"\n" + ] + }, + { + "data": { + "text/html": [ + "
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rankloop_lood_looweightsedsewarningloo_scale
full0-2763.4984.264900.37673939.57530Falselog
noint1-2763.8223.77790.3269620.22060239.64042.28266Falselog
noitem2-2764.5224.84191.026580.11285139.64692.11862Falselog
valence3-276522.78621.511670.16790339.49893.16058Falselog
cond4-2765.822.88852.314060.12190639.61413.32662Falselog
\n", + "
" + ], + "text/plain": [ + " rank loo p_loo d_loo weight se dse warning \\\n", + "full 0 -2763.49 84.2649 0 0.376739 39.5753 0 False \n", + "noint 1 -2763.82 23.7779 0.326962 0.220602 39.6404 2.28266 False \n", + "noitem 2 -2764.52 24.8419 1.02658 0.112851 39.6469 2.11862 False \n", + "valence 3 -2765 22.7862 1.51167 0.167903 39.4989 3.16058 False \n", + "cond 4 -2765.8 22.8885 2.31406 0.121906 39.6141 3.32662 False \n", + "\n", + " loo_scale \n", + "full log \n", + "noint log \n", + "noitem log \n", + "valence log \n", + "cond log " + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cres = az.compare({'full':results, 'noitem': res_noitem, \n", + " 'noint':res_nointer, 'cond': res_cond, 'valence': res_valence})\n", + "cres" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(res_nointer, ref_val=0.0, var_names=['cond', 'valence_mean']);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Conclusion\n", + "\n", + "- There seem to be effects of both condition and valence value on whether or not participants got targets correct.\n", + "- Many more analyses to do!!!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Thanks for a great semester!!!\n", + "\n", + "- We will post the final project template this weekend\n", + "- Find us on Slack or at office hours for assistance\n", + "- Don't forget to fill out course evals!\n", + "\n", + "### Have a great break!!!" + ] + } + ], + "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": "3.8.3" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CS4500_CompMethods/lessons/3subj_data.zip b/CS4500_CompMethods/lessons/3subj_data.zip new file mode 100644 index 0000000..2f4939f Binary files /dev/null and b/CS4500_CompMethods/lessons/3subj_data.zip differ diff --git a/CS4500_CompMethods/lessons/all_data.zip b/CS4500_CompMethods/lessons/all_data.zip new file mode 100644 index 0000000..bab5779 Binary files /dev/null and b/CS4500_CompMethods/lessons/all_data.zip differ diff --git a/CS4500_CompMethods/lessons/ci_within.py b/CS4500_CompMethods/lessons/ci_within.py new file mode 100644 index 0000000..0f9bef3 --- /dev/null +++ b/CS4500_CompMethods/lessons/ci_within.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/CS4500_CompMethods/lessons/data/SMILE/test000/20201128_001601/log_math_distract_0.slog 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b/CS4500_CompMethods/notes_chr4qt.txt @@ -0,0 +1,585 @@ +--9/10 Lecture + + --General Refresher + --Can multiply string by number to print that string x number times. + --Modulo gives you the remainder. + --Use an "if var.isdigit(): " to determine if the input is an integer + --Remember to +1 to include the final number in the list because it's not inclusive + --Put the input inside the function + --Lists + --Remember that lists use [], and lists times number gives the list that number of times + --Use index to change object in list. list[0] = 'new input' + --list[3:] starts at index 3. l[:3] goes until index 3 noninclusive. l[::2] starts at 0 and returns every 2nd value. + --list.append('string') adds an individual item + --list.extend(var) can add the full list stored for var to the end + --can also just use += ['item']. If you assign as a different variable + is still only concatenation. + --pure list assignments a=b change both. Copies made using c=a[:] + --can determine what some functions do using r.*tab*? + --Tuples are just immutable lists that use () + --min(r) and max(r) return the alphanumerical placement of the letter with " " at 0 + --Starting a function with * returns arguments as a tuple + --test(*arguments): + --print(arguments) + --printall(1, 2.0, 'seven') gives a tuple of those 3. + --Can return sequences as a zipped tuple by pairing indices in sequences + --s = 'xyz' + --t = [4,5,6] + --test = zip(s,t) # This does the pairing, zip will stop at earliest point in either list + --Would print things like (x,4) or (z,6) + --Dictionaries (Dict), which relies on keys + --Best practice is to use tuples, NOT lists because you don't want to switch up the key + --Values can be mutable tho. + --Make new dict. dict_name = { + 'first': 1, + 'second': 2, + } + --Change/Add keyval pair: dict_name[key_name] = '' + --Mutability of dictionaries help easily create histograms (last example on slide) + + + + +--9/17 Lecture + + --Overview of what we'll go over + --What are modules in Python + --What is 'namespace' and 'variable scope' + --Import modules and libraries + --Create N-dimensional arrays + --Index values in the arrays + --Perform operations on the arrays + --Modules + --Files that have a collection of related fxns (Python has hundreds in Python Standard Library). Can make new ones + --In psych and neuro, frwquently create new or modify existing modules to fit experiments. + --First need to import it into your namespace and then make use of it + --If want to use trig, (import math) and make sure to convert to Rads. + --(math.pi) is a constant value + --rememebr (math.*tab*) will show you which ones you can use + --(from module_name import *fxn*) can bring functions in but would override variables that have the same name keeping the one that comes later. Easier to do (math.fxn) + --Libraries + --These are larger collections of modules + --(numpy), (scipy), and (matplotlib) + --The first two essentially give you matlab + --(Numpy) + --Overview + --Open source extension package for multidimmesional arrays + --Gives interface to low level highly optimized libraries for numbers + --Designed for scientists + --Has arrays which contain + --Discrete time of experiment/situation + --signal recorded by measuring device + --pixels + --Basics + --Main object is the homogenous array + --Table of elements {probs numbers} of same time indexed by tuple of numbers + --Dimensions are "axes" and number of axes are "rank" + --Coordinates of a point in 3D space [1, 2, 1]: + --Is an array of rank 1, because it has one axis. + --That axis has a length of 3. + --Making arrays + --Recast list as an array {(a = np.array([0,1,2,3]))} + --Calling (*array*.len) returns the number of axes {rows} + --Using (a = np.arange(10)) to make an array from 1-8 {NON-INCLUSIVE} + --Can also chose lower and upper bounds to divide evenly + --(c = np.linspace(0,1, 6)) + --(c = np.linspace(0,1, 5, endpoint = False)) gives the same split but doesn't include the last point. + --A 3 by 3 matrix with values all == 1 + --(a = np.ones((3, 3))) # reminder: (3, 3) is a tuple + --A 2 by 2 matrix with values all == 0 + --(b = np.zeros((2, 2))) + --A matrix that's 3 by 3 with values == 1 along the diagonal + --(c = np.eye(3)) + --Mersenne Twisters generate pseudo random numbers + --(a = np.random.rand(4)) + --Using the code below gives you random yet repeatable numbers + --(np.random.seed(12345)) + --(print(np.random.rand(3))) + --Data types + --Integers run faster than floats, but float is default type + --Can index in arrays also + --To replace, use index to alter + --a[:, 2] = 10 makes all rows at column 2 equal to 10 + --Elementwise operations + --Just use *numpy_var* *operation* *value* + --(a = np.array([1, 2, 3, 4])) + --(a + 1) + --For Multiplication, CANNOT use c * c to do matrix multiplication + --Needs to be c @ c + --Comparative operations can be used to see if each element follows + --(a == b) sees if each point is equal to the respective in the other + --Logical operations also run elementwise + --For "and", "or", and "not" operations, need to use "&", "|", and "~" + --Linear Algebra + --Can create triangles in the matrices + --Can use (a.T) to transpose the matrix by flipping rows and columns + --Reducing Arrays + --Can use (np.sum(x)) or x.sum to get sum + + + + +--9/24 Lecture + --Read & Write basic Text files + --Reading from files + --Open files in the same directory using (f = open('file_name.txt', 'r') + --{'r' is for read, 'w' is for write, 'a' is for append} + --To read one line in use (line = f.readline()) + --MAKE SURE TO (f.close()) WHEN DONE + --Parsing the numbers turns big string into a list of numbers + --Use (line.strip()) to pull off the new line entry at the end of each line + --Use (line.split()) to make each val a new entry in a list + --Can specifiy where to split using (''), (' '), (',') etc. + --Needs to be run after line.strip + --To turn a list from string to numbers, can embed for loop into list trrough list comprehension + --(ints = [int(s) for s in line.strip().split(' ')]) + --List comprehensions are significantly faster than for loops. + --Can then sort the numbers WITHIN THE SAME LIST using sort + --(ints.sort()) + --Can sort in descending order using (ints.sort(reverse=True)) + --Writing the file back out + --Use (f.write()) to file. Can format text with options LOOK INTO THEM + --Use Random Generators to write using a random fashion + --(random.shuffle(ints)) is very useful to randomize lists IN PLACE + --Read and write CSV files {comma separated values} + --Typical in research which is why it led to module creation; essentially a spreadsheet + --Classes (csv.DictReader(open('exp_res.csv', 'r'))) helps readily sort created ditionaries + --May not use this a lot since Pandas is also very useful for what we'll do + --Pickling objects + --Dump an object to file for some future use; allows to serialize Python objects + --Turns them into a byte stream + --Doesn't produce human readable content, i.e. writes out as binary + --Use (d2 = pickle.load(open('my_dict.pickle','rb')) to read the binary txt + --Special things to note + --Are not portable across languages, usually meant for personal storage + --Fundamentals of Expt. design + --Link btwn Science and Coding + --The human brain is a very complex function; the goal is to change parameters and see what happens + --Depend. vs Independ. vars + --The IV are our inputs, the DV are our outputs. Standard + --Parameters/input, return/print, assignments. + --Constraints on how to structure lists + --How to make simple list of dicts. to define a trial + + + + +--10/1 + --ListGen Assignment + --Goal is to generate the lists that will be presented to subjects to collect responses + --Hold information that will be presented inside its own dictionary + --Try to include as much info as possible {Stim, type, cond, novelty} + --Novelty means is it target or lure + --For each trial, there is one dictionary. Then all trials are in a mega list + --The mega list is the key within AN EVEN LARGER dictionary for study or test + --Study and test dictionaries are held in the ULTIMATE list + --Lures must be of the same condition as the study material + --Each ULTIMATE block should have 2 LARGER dictionaries () + --Define Hierarchical State Machine + --State Machine Interface Library for Experiments AKA SMILE + --Goal in develpment was: + --to have easy and hard takes simple with millisecond accuracy + --write expts that run cross-platform + --log everything for repetetive cases. + --Requires Kivy download {Look at code to install} + --Cross-platform python application development library + --Means you can use Python code-base to deploy app to iOS, Windows, etc. + --All core librares are compiled to C code, making it very fast + --Built on OpenGL which results in powerful graphics + --What is a State Machine in general? + --Really focused on fintate state machine since not unlimited + --Common way of modeling systems all over + --Often represented by a directed graph with nodes as states and edges as transitions + --Better understood as a stoplight wehre you progress when condition met + --Powerful extension of a base state machine is to make it hierarchical {HSM} + --Just means states cane be entire finite state machins + --HSM can represent almost any computer program + --Most computer games are just complex HSMs + --In that state doing something until hit achievement, then go into next connected state + --Smile helps you build these Sttae machines + --Difference between build-time and run-time in SMILE + --The building of the exp and the running of it are two DISTINCT features. + --Building/build-times + --When building states, durations are important + --If you don't add one, can go on forever unless have conditional break + --Calls to the SMILE states construct the state machine + --Acutal values in Python variables will not be available yet + --Can't evaluate python variables until later so you use references to allow for the evalueation + --Running/run-times + --Occurs once you hit (exp.run()) + --Will not run the experiment code again + --Don't want to run in the file with code to avoid mistakes + --Use (python exp name.py -s subj001) + -- (-s) opens up full screens for tests, (-f) turns off full screen + --State machine is initialized at the first state and runs to completion + --Does NOT run code in script, just jumps from state machine to state machine + --Difference between Action and Flow states in SMILE + --Action States + --Carry out some specific input or outpur operation and often have a duration + --Examples include: + --(Image): presentes image on screen + --(Label): Places text on screen + --(KeyPress): Accepts user input + --(MovingDot): Presents moving dot stimulus on screen + --({Shapename}): Can present that shape on the screen + --Flow States + --Control the order of operations for the action states, rare duration + --Determine how the action state is carried out + --In a diagram, these are the lines between the bubbles + --Examples include: + --(Parallel) and (Serial): Controll sequences of states + --Simultaneous or Sequential respectively + --(If), (Elif), (Else): Conditioning + --(Meanwhile), (UntilDone): Run states while other states run + --To make a block, need to use (with __) + --To affect the one above on a done use (with UntilDone():) + --How to build simple experiments in SMILE + --For keyboard responses using (kp = KeyPress()), set a (resp_keys) + --Common ones include "f" or "j" + --Input as (['F', 'J']), then do KeyPress(keys=resp_keys) + --Default for escaping an experiment is (SHIFT + Esc) + --For wait durations can add uniformed randomization + --Use (jitter) under wait + --(Loop()) can take in either a number or a list + --All of the SMILE states secretely log time stamps. + + + + +--10/8 + --General Updates + --The goal is to be able to step away from the general experimental set ups + --Create any expt you can think of + --Recap, SMILE is making a Hierarchical State system that uses flow of data points + --All expts end up being a directed acyclic graph + --This is a controlled library so it is often altered/improved/modified + --One beginning and one end, can trace through the family tree to get A->B + --How to update SMILE to the latest version + --Use install line to see if there is a new version of Kivy + --(conda install -c conda-forge kivy) + --Update SMILE from github repository + --(pip install git+https://github.com/compmem/smile --upgrade) + --Use the front chunk to install but (--upgrade) makes sure using latest version. + --Understanding Flow + --Default in SMILE is to proceed sequentially AKA in serial + --(Serial) Parent state begins when some other thing ends + --(Parallel) state runs everything at the same time and ends it together + --Children at starting line and says run. All finished when last one done + --Can bypass the lagging 'runner' by using (blocking) + --(UntilDone) and (Meanwhile) are really elaborate blocking styles + --(UntilDone) Can establish a condition and change when receive input + --(Meanwhile) Establish desire for input and hold condition until met + --Seem a little more organized personally + --For parents, always using (with) + --Still need parents to join + --Placement is important, needs to be the parent RIGHT ABOVE + --Can be separated by physical lines but any additional not states + --Important note!!! + --When a SMILE crashes, you need to reset the whole kernel + --How to present images + --Uses a visual state to present images + --(Image(source='.jpg')) + --Default is to present everything at the center of the screen, maintaining pixels + --When want to change size, use (, allow_stretch=True) + --To allow for distortion in size change (, keep_ratio=False) + --To visualize the DAG for an experiment + --Use dag to give a visual layout of what your code is doing + --( + from smile.dag import DAG + d = DAG(exp) + d.view_png() + ) + --How to lay out visual states on the screen + --Can place them relative to the screen {0,0 in bottom left} OR to other states + --Space relative to standard black screen is + --(exp = Experiment(show_splash=False, resolution=(1024,768))) + --(im_name = Image(source="", left=exp.screen.center_x - var) + --Can set up visual states to do natural movements for some duration + --How to include mouse interaction + --Mouse dissapears by default, can readd using (MouseCursor()) in (Parallel) + --Track position of mouse for cognitive analysis using: + --(mrec = Record(mouse_pos=MousePos())) + -- Remember (|) means "or" + --Can then create Debug states to make keyword arguments that print at screen + --For animations, can link the spatial properties of visual spaces with other things + --Typical is mouse position + --To log information for easy analysis + --SMILE states automatically log themselves, meaning you rarely lose info + --Every single state logs what it does but it's a pain to find and sort + --More manageable to analyze well-organized log file from your own expt. + --Make a list of Key/Value pairs that might be interesting + --How to write subroutines to organize your code + --One of most powerful tools for long code is function + --Mimic the expression of functions using (Subroutine) decorator + --Decorator is just (@Subroutine) and turns fxn into subroutine + --Must define at start and have first parameter be 'self' + --(def MyTrial(self, text):) + --Elaborating using tests + --Make sure not to take unputs until the stimuli are up + --Do this using (Wait(until=stim.appear_time) + --Use (Log()) to create the trial + --There are instances where it's ideal to an domize location (like in flanker) + --Rememebr that Jitter takes a starting value and then goes up to the second val + --Going to want nested loops but start off with just presenting the stimuli + --Using Sliding techniques, can pair responses with a block to make it gauge confidence + --For the DAG portion of the assignment, do conda install pydot + + + + +--10/15 + --Reviewing the provided solution to ListGen (Just one possible) + --He made as general purpose a code as possible + --Distinctional Effect: If you include an item that stands out more you remember it better. + --What will you need to transfer? + --The stuff from the first cell with import csv + --Cells from "# config variables" down three blocks to "len(blocks)" + --How to polish up an experiment + --To start with, ALWAYS make sure your SMILE and Kivy is up to date, upgrade + --DONT link participant results to their Identification scheme. Keep separate + --Follow the block under "Subject Info" + --Relies on an (InputSubject) at the start of the code + --Can make elaborate instructions to explain expt. but to start, just use label + --Use (left=exp.screen.left), and can change text alighment with (halign='right') + --Must make sure that the screen properly accomodates for different window screens. + --Uses mechanism called scaling. + --Pixel Density is standard are at around 96 pixels per inch. + --Import the fxn into the file as shown in Lec. 8 + --How SMILE stores Data + --Use Slogs which are SMILE (Logs) that have condensed files for a lot of information + --They are dict lists. Can be read using: + --(from smile.log import log2dl + dl = log2dl('flanker_log.slog')) + --How to read in slog files + --Just follow along with the lecture, maybe rewatch. On the mend from test cram + --Continuing the Flanker Expt. + --Use (""" """) to maintain written separations in lines + --Don't put the scale in the config, put it in the bottom + --Once you have the data stored in a list of dictionaries, can use pandas to analyze it + --(df = pd.DataFrame(dL)) can help it look a lot more manageable. + --(df.head()) gives you the first five pieces of info. + --Time on screen should be pretty short, around 1.5 sec, use a config variable, no jitter + --The duration of the intersititial space can have jitter + --Make them long list bois. + --Can add a reminder of the testing configurations between each break + + + + +--10/22 + --General Course Plan and Housekeeping + --From here on out focused on data collection, run the three separate tests. + --Use computing ID for identifier (normally not do this for anonymity but need grade) + --Format being used here is identical to that used in Per's actual research lab + --Updating SMILE software needs upgrade code + --(conda install -c conda-forge kivy==1.11.1) + --(pip install git+https://github.com/compmem/smile --upgrade) + --Update is for Math Distract to help adress using delay period between study and test without having uncontrolled rehearsing techniques confounding variables. + --Need to find a way to empty out working memory w stimuli that are orthoganol to the stimuli {If using words, make the delay math using numbers} + --Read data from slog files + --Takes a dictionary and pickles it, compresses it, and sends it out to file + --Can be easily read back in using (log2dl) + --Pandas is at the core of Data Science work + --Birthed from "R-code" with a sole purpose of s p e e d + --Provides 2 main data structures: (Series) and (DataFrames) + --Key feature of Pandas is that DATA ALIGNMENT IS INTRINSIC + --The link between labels and data will not be broken unless explicitly stated + --The Series and DataFrame data structures in Pandas + --Series are one-dimmensional labeled arrays taht can hold any data type + --Ints, strings, floats, python objects, etc... + --Will label with dtype at the end, using values and giving (object) if mixed + --Axes labels are refered to as the index + --Non-specific indices will default to 0 based numbers + --Does not have to be unique {Can repeat indices, but may fail certain tasks} + --Standard upload practices {like numpy as mp} is (import pandas as pd) + --Kinda like a np array but also like a dictionary with index/values->key/value pairs. + --Can add values from separate indices together as long as they are aligned + --If one of the values doesn't exist it will return NaN {AKA DNE} + --Series essentially just act as one column, but don't often use just one + --DataFrames are 2-dimensional labeled data structures with multiple columns for different variable recordings {Rows are usually different participants/trials} + --Many different ways to create a df + --Can separately make dictionary where keys become columns. Cast as df with indices listed turning into the rows + --Can use the same base in numpy record arrays, but the manipulation would have been much harder + --Pick certain columns out of the df treating it like a dict (df['one]) + --Indices stick when you pull the column which is important + --Can add columns or manipulate to create new as if dict also + --To find type make plural (df.dtypes) + --Remove columns using (del df['name']) or do (df.pop('name')) + --If a df is given a column with only one thing, will fill the whole column with that one value + --Load slogs as a DataFrame + --Can cherry pick columns using a list of separate values (df[['name1', 'name2']]) + --For rows can use something like (df.loc['index_name']) to give series where indices are old columns + --Can get rows based on numeric placement using (df.iloc[index]) + --Some basic operations on the data + --Can get a quick summary of the data being read using (df.describe()) + --Returns columns with useful statistical analyses + --For reaction times, better to use the log of the rt + --np.log(df['rt']) + --As you get bigger, better to reduce column amount by only having important ones + --(df.columns) to show existing columns + --(pd.read_excel) will give methods for reading excel sheets + --Only brings in one sheet at a time tho. + + + +--29 + --Read in some real data + --For cell under Diving into Single Subj; + --In the order data, exp_name, subject, session, slogs + --Must calculate time between stimulus presentation and reaction time, which requires after-slogging + --Perform some simple data clean-up + --Can reverse work to find out which data set comes from which condition + --Some visualizations with Pandas + --Simple statistics with SciPy and Stats Models + --Use SciPy {Scientific Python} to understand the data better + --NEED TO IMPORT NUMPY JUST TO BE SAFE, FIND OUT HOW TO INSTALL THIS BY GOING BACK AND WATCHING VIDS + --For legitimacy analysis, ideally use ways of tracking in portion independent of the thing being tested + --So like for this, you would use the intermediate math blocks. + --Especially prevelant for cases like degenerative diseases where chance performance is ACTUAL resp. + --Use binomial tests to analyze probabilities within two outcomes + --Like findind out if a coin flip is biased. + --Use t-tests to determine if the data is significantly distributed + --We only used the non-paired versions + --Can't use t-tests on our data as is because the information isn't normally distributed. + --Becomes more normal if use the numpy log of the values + --Other non-parametric statistic methods in SciPi for situations where you don't want to assume a normal distribution. + + + +--5 + --Behavioral Data Analysis + --These experiments only have 2 DV, Choice and Reaction Time + --For these experiments, the thought is that indoor would be remembered better because more distinctive + --Also an environmental event + --Distinctive stimuli seem to be most memorable which has evolutionary tones + --Read in all of the data from experiments + --Remember to start your files with the imports you'll need + --(log2dl from smile.log) + --(from glob import glob) can pull data from initial directory condition ('data/Taskapalooza/s*') + --The (*) is a wildcard that allows any file added as long as leading end matched. + --Does NOT organize the returned lists, just parses quickly + --The (?) allows for randomization but only for ONE character. + --Good for lots of files that match the naming scheme. + --(import os) allows you to manipulate data across systems despite different keystroke schemes + --(os.path.split) + --Perform simple data clean-up + --Lambda is a core variable in python + --Some more Pandas analysis tricks + --Can graph problems using a quick plot to analyze corretness or rt values + --Regresssion across subjects + --Correlation analysis rely on at least 100 people + --Housekeeping + --Provide methods and results for the experiment you chose + --Give them all of the information needed to recreate your experiment + --Analyze the results and condense. Give a figure and validate with statistical gatherings. + + + + +--12 + --Overview + --If you find yourself using the same functions over and over, just throw it into a raw (.py) file and then just call it when you need it. It lowers chances of new errors between expts. + --Read more into the results you got. For example, if they got it wrong on a lure and chose old, that means they saw a completely new image and had a false alarm that convinced them they had already seen it. + --(astype(np.int) converts a boolean into a 0 or 1 int) + --Basic probability + --At the core of all stats and quantification of outcome likelihood + --Integrals of probability function must always be equal to 1 + --If you break the bound, your model is the thing that is incorrect + --Models can get pretty complicated so we're sticking with continuous models + --Uniformed distributions establish an equal likelihood of an occurence on either end of a value in a given range + --This is a really bad model of the world tho. + --Law of Large numbers states as the observations go towards infinity, distributino turns Gaussian AKA Normal Dist. + --Gausians rely on a standard deviation to scale the function + --Are better because there is an infintismally small but existant probability going towards infinity + --Strength theories of memory + --It is a very Simple Model (Code for it's not accurate in specific cases, just trends). Assumes baseline familiarity is based on repeated association. + --In the case of words, the distribution is Gaussian + --Increasing association strength uniformly shifts the plot to some greater value + --Relation to Signal Detection Theory + --Theory developed by radar operators to contrast signal from noise + --In strength models, you alter criterion based on particular goal {i.e. minimize wrong while maximize right} + --How to calculate Sensitivity {d' AKA d-prime} and bias {c} + --Separation of distributions as measured by the distance between Gaussian peaks + --In units of standard deviation, it's just alpha {shift} divided by deviation + --{d'=alpha/sigma} + --Can use (Z(name_trans)) to get d' but need to adjust because 0 and 1 don't work under Z form. Shifts results slightly but does well enough. + --Plotting and statistics w/ d-prime and bias + --Can install plotline as pn for even more visualization + --(conda install -c conda-forge plotnine) + --To plot them with different colors on the pn.ggplot using (fill='name of column') + --Bar geom needs the 'identity' component + --Specify next to instead of overlapping by using (position_doge(.9)) + --Split out subplots {like lure vs target within indoor and outdoor performance} + --Use (pn.facet_wrap('subgroup') + --For error values, get length by doing mean minus ci and mean plus ci. + --Essential theme is to start with a plot and then add on to that plot += + + + + +--19 + --Housekeeping + --People don't account for uncertainty in models leading to conclusions that don't generalize as they should across a population. + --GIVE THE FEEDBACK IN THE CLASS + --Improve by including literature to help get a grasp of concepts beforehand + --Final project is a synthesis of the experiment so far. Involves METHODS: a write-up of our task-design and methods and RESULTS: plots with statistics organized based on the question being asked. Short discussion with each plot stating what was found. + --The write-up is of ONE of the experiments, and should be detailed enough to allow for replication. + --Number of participants was {21 I think} + --Make use of the methods being used today!! Can ask a new question but apply the same material. + --Quick ANOVA example + --Let's you rapidly test for key interactions using table information like linear regressions + --First step in all these ANOVAs is to group results based on subject and condition + --Goal is to remove valenc though and use actual numeric values. + --Important to acknoweldge the possibility of a trial level effect + --To do it correctly, you need to run one ANOVA run by subject and another run by items. Making sure it is only in one ensures there is no overlap + --Intro to mixed-effects models + --Allows you to account for variance at the item and subject levels at the same time + --Split models into random and fixed effects + --Random variability may be across subjects or items {like ID of participants or of image showing} + --If trying to take into account probability of correctness, the measured changes would be fixed + --If one subject performs really highly and one performs really poorly that would be a random effect + --To accurately account for that you would need to allow different subjects to have different intercepts and just measure relative changes. + --To fit a model allowing each subject to have an individual slope and intercept would need much more data + --To get into all of that and include hierarchical models, would be better to take Quant. Cog. + --Intro to Bayesian models + --T-test assess means and standard distributions of different data points to see if there is significant distinction + --These models generate models using probabiity distributions + --Expect the two overlapping t graphs to be more conservative for the word count based on the stats shown in the graph + --Started with a histogram of results, where the area was total probability of results = 1 + --Using rug plots to plot the actual point, can then craft a probability function and extend lines to see where they intercept with the function. {Stem Plot} + --Fit and visualize a Bayesian mixed-effects regression to our data + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/CS4500_CompMethods/personal_notes/Untitled.ipynb b/CS4500_CompMethods/personal_notes/Untitled.ipynb new file mode 100644 index 0000000..f0676e8 --- /dev/null +++ b/CS4500_CompMethods/personal_notes/Untitled.ipynb @@ -0,0 +1,104 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Guess a number between 1 and 100.\n", + "press y if you want to show the hint and n if you don't want it: 80\n", + "Your Guess: 80\n", + "Guess higher!\n", + "Your Guess: 90\n", + "Guess lower!\n", + "Your Guess: 85\n", + "Guess lower!\n", + "Your Guess: 84\n", + "Guess lower!\n", + "Your Guess: 83\n", + "Guess lower!\n", + "Your Guess: 82\n", + "You got it right congratulations!\n", + "Thanks for playing this lovely game.\n" + ] + } + ], + "source": [ + "'''made by Salah Gfx, a complete noob in python, in the 2/5/2017'''\n", + "# make sure to un the code in a python idle because it seems that SoloLearn does not run it properly \n", + "import random\n", + "\n", + "def main():\n", + " print(\"Guess a number between 1 and 100.\")\n", + "\n", + " # rndmNumber = 35 just to test things if they work as I wish\n", + " rndmNumber = random.randint(1, 100)\n", + " # yorn stand for yes or no\n", + " \n", + " def yorn():\n", + " usChoice = str(input(\"press y if you want to show the hint and n if you don't want it: \"))\n", + " if usChoice == str(\"y\"):\n", + " if rndmNumber in range(1, 100, 2):\n", + " print(\"Hint:(It\\'s an odd number)\")\n", + " return False\n", + " else:\n", + " print(\"Hint:(It\\'s an even number)\")\n", + " elif usChoice == str(\"n\"):\n", + " return False\n", + " else:\n", + " return True\n", + "\n", + " yorn()\n", + "\n", + " found = False\n", + "\n", + " while not found:\n", + " usGuess = int(input(\"Your Guess: \"))\n", + " \n", + " if usGuess == rndmNumber:\n", + "\n", + " print(\"You got it right congratulations!\\nThanks for playing this lovely game.\")\n", + " # found the correct guess\n", + " found = True\n", + "\n", + " elif usGuess > rndmNumber:\n", + " print(\"Guess lower!\")\n", + "\n", + " else:\n", + " print(\"Guess higher!\")\n", + " \n", + "\n", + "\n", + "if __name__ == '__main__':\n", + " main()\n", + " \n", + "# make sure to like (y) and shaire -&+ :) " + ] + } + ], + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CS4500_CompMethods/personal_notes/notes_chr4qt.txt b/CS4500_CompMethods/personal_notes/notes_chr4qt.txt new file mode 100644 index 0000000..89e78ed --- /dev/null +++ b/CS4500_CompMethods/personal_notes/notes_chr4qt.txt @@ -0,0 +1,389 @@ +--9/10 Lecture + + --General Refresher + --Can multiply string by number to print that string x number times. + --Modulo gives you the remainder. + --Use an "if var.isdigit(): " to determine if the input is an integer + --Remember to +1 to include the final number in the list because it's not inclusive + --Put the input inside the function + --Lists + --Remember that lists use [], and lists times number gives the list that number of times + --Use index to change object in list. list[0] = 'new input' + --list[3:] starts at index 3. l[:3] goes until index 3 noninclusive. l[::2] starts at 0 and returns every 2nd value. + --list.append('string') adds an individual item + --list.extend(var) can add the full list stored for var to the end + --can also just use += ['item']. If you assign as a different variable + is still only concatenation. + --pure list assignments a=b change both. Copies made using c=a[:] + --can determine what some functions do using r.*tab*? + --Tuples are just immutable lists that use () + --min(r) and max(r) return the alphanumerical placement of the letter with " " at 0 + --Starting a function with * returns arguments as a tuple + --test(*arguments): + --print(arguments) + --printall(1, 2.0, 'seven') gives a tuple of those 3. + --Can return sequences as a zipped tuple by pairing indices in sequences + --s = 'xyz' + --t = [4,5,6] + --test = zip(s,t) # This does the pairing, zip will stop at earliest point in either list + --Would print things like (x,4) or (z,6) + --Dictionaries (Dict), which relies on keys + --Best practice is to use tuples, NOT lists because you don't want to switch up the key + --Values can be mutable tho. + --Make new dict. dict_name = { + 'first': 1, + 'second': 2, + } + --Change/Add keyval pair: dict_name[key_name] = '' + --Mutability of dictionaries help easily create histograms (last example on slide) + + + + +--9/17 Lecture + + --Overview of what we'll go over + --What are modules in Python + --What is 'namespace' and 'variable scope' + --Import modules and libraries + --Create N-dimensional arrays + --Index values in the arrays + --Perform operations on the arrays + --Modules + --Files that have a collection of related fxns (Python has hundreds in Python Standard Library). Can make new ones + --In psych and neuro, frwquently create new or modify existing modules to fit experiments. + --First need to import it into your namespace and then make use of it + --If want to use trig, (import math) and make sure to convert to Rads. + --(math.pi) is a constant value + --rememebr (math.*tab*) will show you which ones you can use + --(from module_name import *fxn*) can bring functions in but would override variables that have the same name keeping the one that comes later. Easier to do (math.fxn) + --Libraries + --These are larger collections of modules + --(numpy), (scipy), and (matplotlib) + --The first two essentially give you matlab + --(Numpy) + --Overview + --Open source extension package for multidimmesional arrays + --Gives interface to low level highly optimized libraries for numbers + --Designed for scientists + --Has arrays which contain + --Discrete time of experiment/situation + --signal recorded by measuring device + --pixels + --Basics + --Main object is the homogenous array + --Table of elements {probs numbers} of same time indexed by tuple of numbers + --Dimensions are "axes" and number of axes are "rank" + --Coordinates of a point in 3D space [1, 2, 1]: + --Is an array of rank 1, because it has one axis. + --That axis has a length of 3. + --Making arrays + --Recast list as an array {(a = np.array([0,1,2,3]))} + --Calling (*array*.len) returns the number of axes {rows} + --Using (a = np.arange(10)) to make an array from 1-8 {NON-INCLUSIVE} + --Can also chose lower and upper bounds to divide evenly + --(c = np.linspace(0,1, 6)) + --(c = np.linspace(0,1, 5, endpoint = False)) gives the same split but doesn't include the last point. + --A 3 by 3 matrix with values all == 1 + --(a = np.ones((3, 3))) # reminder: (3, 3) is a tuple + --A 2 by 2 matrix with values all == 0 + --(b = np.zeros((2, 2))) + --A matrix that's 3 by 3 with values == 1 along the diagonal + --(c = np.eye(3)) + --Mersenne Twisters generate pseudo random numbers + --(a = np.random.rand(4)) + --Using the code below gives you random yet repeatable numbers + --(np.random.seed(12345)) + --(print(np.random.rand(3))) + --Data types + --Integers run faster than floats, but float is default type + --Can index in arrays also + --To replace, use index to alter + --a[:, 2] = 10 makes all rows at column 2 equal to 10 + --Elementwise operations + --Just use *numpy_var* *operation* *value* + --(a = np.array([1, 2, 3, 4])) + --(a + 1) + --For Multiplication, CANNOT use c * c to do matrix multiplication + --Needs to be c @ c + --Comparative operations can be used to see if each element follows + --(a == b) sees if each point is equal to the respective in the other + --Logical operations also run elementwise + --For "and", "or", and "not" operations, need to use "&", "|", and "~" + --Linear Algebra + --Can create triangles in the matrices + --Can use (a.T) to transpose the matrix by flipping rows and columns + --Reducing Arrays + --Can use (np.sum(x)) or x.sum to get sum + + + + +--9/24 Lecture + --Read & Write basic Text files + --Reading from files + --Open files in the same directory using (f = open('file_name.txt', 'r') + --{'r' is for read, 'w' is for write, 'a' is for append} + --To read one line in use (line = f.readline()) + --MAKE SURE TO (f.close()) WHEN DONE + --Parsing the numbers turns big string into a list of numbers + --Use (line.strip()) to pull off the new line entry at the end of each line + --Use (line.split()) to make each val a new entry in a list + --Can specifiy where to split using (''), (' '), (',') etc. + --Needs to be run after line.strip + --To turn a list from string to numbers, can embed for loop into list trrough list comprehension + --(ints = [int(s) for s in line.strip().split(' ')]) + --List comprehensions are significantly faster than for loops. + --Can then sort the numbers WITHIN THE SAME LIST using sort + --(ints.sort()) + --Can sort in descending order using (ints.sort(reverse=True)) + --Writing the file back out + --Use (f.write()) to file. Can format text with options LOOK INTO THEM + --Use Random Generators to write using a random fashion + --(random.shuffle(ints)) is very useful to randomize lists IN PLACE + --Read and write CSV files {comma separated values} + --Typical in research which is why it led to module creation; essentially a spreadsheet + --Classes (csv.DictReader(open('exp_res.csv', 'r'))) helps readily sort created ditionaries + --May not use this a lot since Pandas is also very useful for what we'll do + --Pickling objects + --Dump an object to file for some future use; allows to serialize Python objects + --Turns them into a byte stream + --Doesn't produce human readable content, i.e. writes out as binary + --Use (d2 = pickle.load(open('my_dict.pickle','rb')) to read the binary txt + --Special things to note + --Are not portable across languages, usually meant for personal storage + --Fundamentals of Expt. design + --Link btwn Science and Coding + --The human brain is a very complex function; the goal is to change parameters and see what happens + --Depend. vs Independ. vars + --The IV are our inputs, the DV are our outputs. Standard + --Parameters/input, return/print, assignments. + --Constraints on how to structure lists + --How to make simple list of dicts. to define a trial + + + + +--10/1 + --ListGen Assignment + --Goal is to generate the lists that will be presented to subjects to collect responses + --Hold information that will be presented inside its own dictionary + --Try to include as much info as possible {Stim, type, cond, novelty} + --Novelty means is it target or lure + --For each trial, there is one dictionary. Then all trials are in a mega list + --The mega list is the key within AN EVEN LARGER dictionary for study or test + --Study and test dictionaries are held in the ULTIMATE list + --Lures must be of the same condition as the study material + --Each ULTIMATE block should have 2 LARGER dictionaries () + --Define Hierarchical State Machine + --State Machine Interface Library for Experiments AKA SMILE + --Goal in develpment was: + --to have easy and hard takes simple with millisecond accuracy + --write expts that run cross-platform + --log everything for repetetive cases. + --Requires Kivy download {Look at code to install} + --Cross-platform python application development library + --Means you can use Python code-base to deploy app to iOS, Windows, etc. + --All core librares are compiled to C code, making it very fast + --Built on OpenGL which results in powerful graphics + --What is a State Machine in general? + --Really focused on fintate state machine since not unlimited + --Common way of modeling systems all over + --Often represented by a directed graph with nodes as states and edges as transitions + --Better understood as a stoplight wehre you progress when condition met + --Powerful extension of a base state machine is to make it hierarchical {HSM} + --Just means states cane be entire finite state machins + --HSM can represent almost any computer program + --Most computer games are just complex HSMs + --In that state doing something until hit achievement, then go into next connected state + --Smile helps you build these Sttae machines + --Difference between build-time and run-time in SMILE + --The building of the exp and the running of it are two DISTINCT features. + --Building/build-times + --When building states, durations are important + --If you don't add one, can go on forever unless have conditional break + --Calls to the SMILE states construct the state machine + --Acutal values in Python variables will not be available yet + --Can't evaluate python variables until later so you use references to allow for the evalueation + --Running/run-times + --Occurs once you hit (exp.run()) + --Will not run the experiment code again + --Don't want to run in the file with code to avoid mistakes + --Use (python exp name.py -s subj001) + -- (-s) opens up full screens for tests, (-f) turns off full screen + --State machine is initialized at the first state and runs to completion + --Does NOT run code in script, just jumps from state machine to state machine + --Difference between Action and Flow states in SMILE + --Action States + --Carry out some specific input or outpur operation and often have a duration + --Examples include: + --(Image): presentes image on screen + --(Label): Places text on screen + --(KeyPress): Accepts user input + --(MovingDot): Presents moving dot stimulus on screen + --({Shapename}): Can present that shape on the screen + --Flow States + --Control the order of operations for the action states, rare duration + --Determine how the action state is carried out + --In a diagram, these are the lines between the bubbles + --Examples include: + --(Parallel) and (Serial): Controll sequences of states + --Simultaneous or Sequential respectively + --(If), (Elif), (Else): Conditioning + --(Meanwhile), (UntilDone): Run states while other states run + --To make a block, need to use (with __) + --To affect the one above on a done use (with UntilDone():) + --How to build simple experiments in SMILE + --For keyboard responses using (kp = KeyPress()), set a (resp_keys) + --Common ones include "f" or "j" + --Input as (['F', 'J']), then do KeyPress(keys=resp_keys) + --Default for escaping an experiment is (SHIFT + Esc) + --For wait durations can add uniformed randomization + --Use (jitter) under wait + --(Loop()) can take in either a number or a list + --All of the SMILE states secretely log time stamps. + + + + +--10/8 + --General Updates + --The goal is to be able to step away from the general experimental set ups + --Create any expt you can think of + --Recap, SMILE is making a Hierarchical State system that uses flow of data points + --All expts end up being a directed acyclic graph + --This is a controlled library so it is often altered/improved/modified + --One beginning and one end, can trace through the family tree to get A->B + --How to update SMILE to the latest version + --Use install line to see if there is a new version of Kivy + --(conda install -c conda-forge kivy) + --Update SMILE from github repository + --(pip install git+https://github.com/compmem/smile --upgrade) + --Use the front chunk to install but (--upgrade) makes sure using latest version. + --Understanding Flow + --Default in SMILE is to proceed sequentially AKA in serial + --(Serial) Parent state begins when some other thing ends + --(Parallel) state runs everything at the same time and ends it together + --Children at starting line and says run. All finished when last one done + --Can bypass the lagging 'runner' by using (blocking) + --(UntilDone) and (Meanwhile) are really elaborate blocking styles + --(UntilDone) Can establish a condition and change when receive input + --(Meanwhile) Establish desire for input and hold condition until met + --Seem a little more organized personally + --For parents, always using (with) + --Still need parents to join + --Placement is important, needs to be the parent RIGHT ABOVE + --Can be separated by physical lines but any additional not states + --Important note!!! + --When a SMILE crashes, you need to reset the whole kernel + --How to present images + --Uses a visual state to present images + --(Image(source='.jpg')) + --Default is to present everything at the center of the screen, maintaining pixels + --When want to change size, use (, allow_stretch=True) + --To allow for distortion in size change (, keep_ratio=False) + --To visualize the DAG for an experiment + --Use dag to give a visual layout of what your code is doing + --( + from smile.dag import DAG + d = DAG(exp) + d.view_png() + ) + --How to lay out visual states on the screen + --Can place them relative to the screen {0,0 in bottom left} OR to other states + --Space relative to standard black screen is + --(exp = Experiment(show_splash=False, resolution=(1024,768))) + --(im_name = Image(source="", left=exp.screen.center_x - var) + --Can set up visual states to do natural movements for some duration + --How to include mouse interaction + --Mouse dissapears by default, can readd using (MouseCursor()) in (Parallel) + --Track position of mouse for cognitive analysis using: + --(mrec = Record(mouse_pos=MousePos())) + -- Remember (|) means "or" + --Can then create Debug states to make keyword arguments that print at screen + --For animations, can link the spatial properties of visual spaces with other things + --Typical is mouse position + --To log information for easy analysis + --SMILE states automatically log themselves, meaning you rarely lose info + --Every single state logs what it does but it's a pain to find and sort + --More manageable to analyze well-organized log file from your own expt. + --Make a list of Key/Value pairs that might be interesting + --How to write subroutines to organize your code + --One of most powerful tools for long code is function + --Mimic the expression of functions using (Subroutine) decorator + --Decorator is just (@Subroutine) and turns fxn into subroutine + --Must define at start and have first parameter be 'self' + --(def MyTrial(self, text):) + --Elaborating using tests + --Make sure not to take unputs until the stimuli are up + --Do this using (Wait(until=stim.appear_time) + --Use (Log()) to create the trial + --There are instances where it's ideal to an domize location (like in flanker) + --Rememebr that Jitter takes a starting value and then goes up to the second val + --Going to want nested loops but start off with just presenting the stimuli + --Using Sliding techniques, can pair responses with a block to make it gauge confidence + --For the DAG portion of the assignment, do conda install pydot + + +--10/15 +-- +-- +-- +-- + + + + +--22 +-- +-- +-- +-- + + + + +--29 +-- +-- +-- +-- + + + + +--5 +-- +-- +-- +-- + + + + +--12 +-- +-- +-- +-- + + + + +--19 +-- +-- +-- +-- +-- +-- +-- +-- +-- +-- +-- +-- +-- +-- +-- +-- +-- \ No newline at end of file diff --git a/CS4500_CompMethods/personal_notes/quotes.txt .txt b/CS4500_CompMethods/personal_notes/quotes.txt .txt new file mode 100644 index 0000000..9823723 --- /dev/null +++ b/CS4500_CompMethods/personal_notes/quotes.txt .txt @@ -0,0 +1,8 @@ +"Eat slugs, Malfoy" + -Ron Weasly, _Harry Potter_ + +"Thank you for nothing, you useless reptile" + -Hiccup Haddock III, _HTTYD_ + +"Why is it so hard to think of a quote when you need to?" + -Carlos Rodriguez \ No newline at end of file diff --git a/CS4500_CompMethods/syllabus/syllabus.md b/CS4500_CompMethods/syllabus/syllabus.md new file mode 100644 index 0000000..1fc4aca --- /dev/null +++ b/CS4500_CompMethods/syllabus/syllabus.md @@ -0,0 +1,138 @@ +--- +title: 'Computational Methods in Psychology and Neuroscience' +subtitle: 'Psychology 4500/7559 --- Fall 2020' +author: Per B. Sederberg, PhD +documentclass: scrartcl +date: Version 2020-09-15 +header-includes: + - \usepackage{array,hyperref} + - \usepackage[letterpaper, margin=1in]{geometry} + - \usepackage{libertine} + - \usepackage[libertine]{newtxmath} +--- + + +# Quick Reference + +Credit: +: 3 units ; Class # 18865 + +Time: +: Thursday, 14:00 -- 16:30 + +Place: +: Online + +Text: +: Assigned readings + +Course Web Page: +: GitHub (https://github.com/compmem/compsy) + +Course assistants: +: Ryan Kirkpatrick (and CompMem lab members) + +Instructor: +: Dr. Per Sederberg + +Office: +: Online + +E-mail: +: pbs5u@virginia.edu + +Lab Website: +: Computational Memory Lab (https://compmem.org) + +Office hours: +: Tuesday, 14:30--15:00 and Wednesday, 13:30--14:00 + +Final: +: Project-based + + + +# Overview and Course Objectives + +In the late 1800s, Hermann Ebbinghaus and Georg Elias Müller were busy launching the systematic study of human memory. They painstakingly wrote out study lists and and kept track of their own and their participants' memory performance by hand after studying the items. Müller even built a "memory drum" that had stimuli wrapped around a rotating drum that revealed them one at a time at a fixed rate. All analysis and visualization of their results involved tabulating data by hand in notebooks. Nonetheless, through years of dedicated work they were able to lay the foundation for the next century of memory research. Today, we have the help of computers, which have become an indispensable tool in Psychology and Neuroscience. Yet, in these same fields, computers are rarely employed to their full potential, limiting the productivity, reproducibility, and quality of our work. + +Science is hard. We need as many tools as possible at our disposal to make our job easier. These days computers play an integral role in our scientific workflow and psychologists typically rely on a limited number of large-scale software packages to assist at each stage of this process (e.g., EPrime, Microsoft Excel, SPSS and, if we're willing to branch out into the land of programming, Matlab or R). This course is designed to break the fetters of commercial, and often inflexible, applications with the power of computer programming. With no assumptions of prior programming experience, we focus on the Python language and specifically on how it can help with *every* stage of our scientific workflow in Psychology and Neuroscience. The goal is that you will gain a better understanding of how a computer works (and can work for you), improve how you solve problems, and optimize and speed up your workflow, but, most importantly, that you will lessen the need to tailor your research questions based on the status quo. + +# Online Course Expectations + +This course will be taking place entirely online with "synchronous" classes on Zoom. As much as possible, I hope we can make this feel like we are all in this together, meeting in the same room. As such, these are the primary guidelines and expectations for our Zoom meetings: + +- You should keep your video on unless a transient issue arises (e.g., there is something seriously distracting going on in the room.) +- You can, however, keep your microphone muted when not talking. +- Feel free to ask questions anytime! It's often hard for me to see everyone, so interjecting by voice is perfectly fine (i.e., you don't need to use the hand-raising feature in the Zoom chat.) +- We will be recording the lessons, which will be made available *only* to those in the class via UVACollab. + +If you have any concerns about any of these policies, please set up a meeting with me and I will do my best to accommodate your needs. + +# Resources + +The two main sources for lesson materials are: + +- Downey, Allen (2016). Think Python: How to think like a computer scientist (2nd Edition). + Needham, MA: Green Tea Press. + - Free downloadable copy at https://greenteapress.com/wp/think-python-2e/ + +- Gael Varoquaux, Valentin Haenel, Pierre de Buyl, Gert-Ludwig Ingold, Emmanuelle Gouillart, Michael Hartmann, ... João Felipe Santos. (2020, March). scipy-lectures/scipy-lecture-notes: Release 2020.1 (Version 2020.1) + - https://scipy-lectures.org/index.html + +In addition, we will make use of a number of other online resources, including documentation and user manuals for the various Python libraries and packages that you will be learning to use. + + +# Computing Requirements + +This is a computational class and all work will be performed on a computer, and almost entirely with the Python programming language within Jupyter notebooks. You will need to bring a laptop running Windows, OSX, or Linux to every class. + +You will run the [Jupyter](https://jupyter.org) notebooks directly on your computer. This will also allow you to incorporate these approaches into your own research more easily. Thus, my recommendation is that you install and use the [Anaconda Python](https://www.anaconda.com/download/) distribution for your OS. + +We will spend time on the first day of class to ensure everyone has a functioning computer that will be able to run everything necessary for the course. + + +# Assistance + +I am eager for you to get as much as possible from this course, so please feel free to come to me with any questions you have. That said, science is a team effort and in order to reduce duplication of questions and discussions, we will be using Slack for all class communication and discussions. Please do not email me unless there is an issue with Slack. We will set up channels for general discussion. If you'd prefer to have a one-on-one discussion it is possible to send direct messages in Slack to either me or the TAs. We will spend some time on the first day ensuring everyone is set up to use Slack. I will also have weekly office hours to which you are always welcome to come and have virtual in-person discussions. + + +# Schedule + +The following is the general order of the topics covered in the course. Please note that sometimes we may cover multiple topics in a single lecture, or spend more than one lecture on a single topic, and this list is subject to modification at any time. That said, all major changes will also include an update to the syllabus, so it will remain a point of reference. + +0. Intro and Ecosystem setup +1. Version control with git +2. Python programming +3. Experiment design and implementation +4. Data collection and processing +5. Data visualization +6. Data analysis and statistics +7. Presentations +8. Advanced topics + +Lectures, in the form of Jupyter Notebooks, will typically be posted the day of class, so you can follow along. Assignments, from smaller exercises to larger projects (see below), will be assigned in class with clear due dates spread throughout the semester. + + +# Evaluation + +This is a upper-level course, which means that much of the burden of staying motivated to learn is transferred to the student. As such, there will not be any in-class exams. Students will be evaluated on the basis of: + +- Lesson exercises / class participation (30 pts) +- List generation project (20 pts) +- Experiment project (20 pts) +- Data Analysis project (30 pts) + +for a total of 100 points. + +Graduate students will have the following additional course requirements: + +- Experiment motivation and design document (20 pts) +- Final write-up with results and discussion (30 pts) + +for a total of 50 additional points. + +The course will be graded using the standard grading scale with your percentage of points earned out of the total possible points rounding to the nearest whole percentage point. + + + diff --git a/CS4500_CompMethods/syllabus/syllabus.pdf b/CS4500_CompMethods/syllabus/syllabus.pdf new file mode 100644 index 0000000..cbe4cfd Binary files /dev/null and b/CS4500_CompMethods/syllabus/syllabus.pdf differ diff --git a/quotes.txt .txt b/quotes.txt .txt new file mode 100644 index 0000000..9823723 --- /dev/null +++ b/quotes.txt .txt @@ -0,0 +1,8 @@ +"Eat slugs, Malfoy" + -Ron Weasly, _Harry Potter_ + +"Thank you for nothing, you useless reptile" + -Hiccup Haddock III, _HTTYD_ + +"Why is it so hard to think of a quote when you need to?" + -Carlos Rodriguez \ No newline at end of file