Python for Scientific Programming & Data Manipulation Fall 2023
- Python Programming: Advanced data manipulation and scripting.
- Statistical Analysis: Generating and analyzing statistical distributions.
- Waveform Generation: Simulating digital signals and waveforms.
- Dataflow Programming: Implementing PULL dataflow architectures with Python.
- Visual Data Representation: Using matplotlib for data visualization.
- Linux Command Line: Managing project environments and automation with makefiles.
- Version Control: Proficient use of Git for source code management.
- Problem Solving: Applied mathematical concepts to solve programming challenges.
- Efficiency Optimization: Employed partial function applications and closures.
- Code Documentation and Structuring: Writing clear, maintainable code with detailed comments.
This project is intended for educational purposes only.
Assignment 1 Summary: Python for Scientific Programming & Data Manipulation: Generating and Analyzing Statistical Distributions
In this project, I extended an existing script that generates uniform statistical distributions, enhancing it to include additional distribution types. The project was structured in a Linux environment, where I utilized a series of commands to set up my workspace and access provided files.
The point of my assignment was to modify a Python script, enriching it with code to generate and analyze various statistical distributions. These included normal and exponential distributions, both from the standard Python library and the NumPy module, adding a new layer of complexity and learning.
To ensure the accuracy of my work, I used a makefile for automating tests, a script for comparing CSV output files, and another for generating histograms to visually represent the data distributions. This hands-on approach not only helped my programming skills but also deepened my understanding of statistical concepts and their practical applications in data science.
Throughout the project, I was immersed in a real-world coding environment, using tools like SSH for remote access and engaging with a Linux command-line interface. This assignment was a blend of theoretical knowledge and practical application, challenging me to apply programming concepts to solve complex problems in scientific programming and data manipulation.
For this assignment, I ventured into the realm of discrete waveform generation, manipulating trigonometric and linear functions to produce various waveforms. The task involved writing a Python script that generates discrete steps for different waveforms, simulating a scenario where one might encounter such data in digital signal processing or audio engineering.
I worked with a set of pre-defined waveforms, including sine, cosine, square, and sawtooth waves, each with its own unique properties and challenges. The project was not just about generating waveforms; it also involved understanding the underlying mathematical functions and their implementation in a programming context.
Utilizing a Linux environment, I employed a makefile for test automation, used Python scripts for data manipulation and visualization, and learned to manage input and output files efficiently. This assignment was a great way to bridge theoretical knowledge from signal processing with practical programming skills, offering insights into how such concepts are applied in technology and engineering fields.
Assignment 3 Summary: Python for Scientific Programming & Data Manipulation: Exploring Function Bindings
This project was an exploration of different methods to create partial bindings for function parameters in Python, a concept that enhances modularity and reusability in code. I explored three distinct methods: using an object, a closure, and a partial function. Each method had its own unique approach to binding column names to column numbers, aiming to optimize the process of data manipulation within a CSV-like table structure.
The assignment was not just a coding task but a deep dive into advanced Python features, enhancing my understanding of functional programming concepts and their practical applications. By implementing these different binding methods, I gained insights into the nuances of Python's capabilities, furthering my understanding of how to write more efficient and maintainable code.
Assignment 4 Summary: Python for Scientific Programming & Data Manipulation: Dataflow Programming in Python
In this assignment, I tackled the concept of dataflow programming in Python, focusing on creating and manipulating statistical distributions within a PULL dataflow architecture. The task required me to write several Python generators that work together in a pipeline, simulating a dataflow where each function or generator acts as a node within the flow.
The assignment challenged me to think about programming from a different perspective, focusing on the flow of data through a series of transformations. I developed generators for creating normal and exponential distributions and implemented a pipeline that processed these distributions to generate statistical analyses.
This project was a fantastic opportunity to delve into dataflow programming, enhancing my skills in writing more modular, readable, and maintainable code. It also provided a practical application of statistical concepts in a programming context, reinforcing the importance of understanding both the theoretical and practical aspects of data manipulation and analysis.