This repository contains a collection of Python scripts developed for various assignments. Each script showcases different aspects of Python programming, including working with Numpy arrays, creating a number guessing game, data visualization, and image processing.
- Task 1A: Numpy Arrays
- Task 1B: Guess the Number Game
- Task 1C: Multiplication Table Trainer
- Task 3: Data Visualization and Image Processing
- Task 4: Visual Stimuli Generation
- Task 5: Final Behavioral Data Analysis Project
This script demonstrates basic operations with Numpy arrays, including array creation, mathematical operations, and filtering elements based on conditions.
- Array Creation: Arrays
aandbare created using Numpy. - Operations: Compute the size, mean, median, and sum of the arrays. Perform element-wise operations such as addition, subtraction, and modulus.
- Odd Number Extraction: Extract odd numbers from arrays
aandb. - Range Array: Generate arrays containing all integer values between 1 and 10,000 using different methods.
Simply run the script, and the results will be printed to the console.
This is a simple number guessing game where the user has up to six attempts to guess a random number between 1 and 100.
- Random Number Generation: The script generates a random number between 1 and 100.
- User Interaction: The user is prompted to guess the number, with feedback provided on whether the guess is too high or too low.
- Win/Loss Conditions: The game informs the user if they guessed the number correctly or if they ran out of attempts.
Run the script and follow the on-screen instructions to guess the number.
This script helps users practice multiplication tables by generating random multiplication problems.
- Random Problems: The user selects a multiplication table to practice, and the script generates random problems.
- Feedback: The script provides feedback on whether the user's answer is correct.
- Exit Option: Users can quit the game by pressing 'q'.
Run the script, input the desired multiplication table, and solve the problems.
This script visualizes eye velocity data over time using Matplotlib.
- Plotting: The script plots eye velocity against time and marks the average velocity with a dashed line.
- Histogram: A histogram of eye movement velocities is generated to analyze the distribution.
This script demonstrates loading and manipulating images using Matplotlib, PIL, and OpenCV.
- Image Display: Display multiple images individually and as a 2x2 grid.
- Image Manipulation: Rotate, resize, and concatenate images using OpenCV.
- Image Saving: The final image is saved as both a JPG and PDF.
Ensure you have the required image files (1.png, 2.png, 3.png, 4.png) in the working directory. Run the script to see the visualizations and image manipulations.
This script generates visual stimuli consisting of a target shape among distractors, useful for psychology experiments.
- Background Setup: Define the background size and color.
- Target and Distractors: Specify properties such as shape, size, color, and number for both the target and distractors.
- Random Placement: The target and distractors are randomly placed on the background.
- Image Saving: The generated stimuli are saved as an image file.
Run the script, and the stimuli image will be saved in the Stimuli directory.
This script completes data wrangling procedures, visualizes datasets of interest, runs mixed effects models, and performs inferential statistical analyses.
- Data Management: Handles data loading, preprocessing, and management, including column renaming and condition-based categorization.
- Analysis: Performs various analyses such as repeated measures ANOVA and mixed effects models on different datasets.
- Visualization: Generates plots and visualizations for accuracy, response time, and memory specificity data.
Ensure all required libraries are installed before running the script. Update file paths in the script as needed for local data access. Run the script in a Python environment with necessary dependencies.
- pandas
- numpy
- matplotlib
- statsmodels
- seaborn
- scipy
- This script performs extensive data wrangling and visualization. Ensure the input data is correctly formatted as specified in the script.
- Results will be saved in specified directories; ensure appropriate permissions and paths are set.
For further details on any of the scripts, refer to their individual sections or the comments within the scripts themselves.