Version Control & Commit Workflow
Relevant source files
The following files were used as context for generating this wiki page:
Introduction
The version control and commit workflow for this project centers on the systematic processing of external data files and the implementation of statistical analysis scripts. The repository is structured into homework modules (e.g., hw1, hw2, hw3), suggesting a linear progression of feature development and data management.
The primary workflow involves ingesting raw numerical data from text files, such as CHOLESTEROL.txt and WEIGHT.txt, and processing them through Python-based analysis tools to generate statistical metrics. This ensures that every commit representing a functional change is backed by reproducible data processing logic.
Data Ingestion & Processing Logic
The project utilizes a specific file-reading mechanism to transition raw data into the computational environment. This logic is encapsulated within the open_file function, which manages file streams and error handling for non-numeric content.
File Processing Flow
The system follows a sequential flow from file discovery to metric calculation:
The flow demonstrates the transition of raw data from text files into the global data list for processing.
Sources: hw1/p1.py:6-16, hw1/p1.py:18-20
Key Processing Functions
| Function | Purpose | Implementation Detail |
|---|---|---|
open_file(file_name) | Parses text files for numeric data. | Uses strip() and try-except for ValueError. |
compute_file(file_name) | Orchestrates the analysis pipeline. | Invokes calculations for mean, median, and variance. |
Sources: hw1/p1.py:6-20
Statistical Analysis Implementation
The repository contains implementations for various statistical metrics, utilizing both the numpy library and standard Python statistics modules. This modular approach allows for the calculation of central tendency and dispersion.
Sequence of Operations
The following diagram illustrates the interaction between the script and the data storage during a computation run.
This sequence ensures that calculations are performed only after the data structure is fully populated.
Sources: hw1/p1.py:18-53
Metric Calculations
The implementation includes complex derivations, such as the two coefficients of variation:
- CV1: $3 \times (mean - median) / std$
- CV2: Based on the sum of cubed differences relative to the length and standard deviation cubed.
# 7. Calculate 1st Coefficient of Variation
cv1 = 3*(mean - median) / std
print("Coefficient of Variation: ", cv1)
# 8. Calculate 2nd Coefficient of Variation
cv2 = sum([(x - mean) ** 3 for x in data]) / (len(data) * std ** 3)
print("Coefficient of Variation: ", cv2)
Sources: hw1/p1.py:48-53
Data Structure & External Files
The project relies on external .txt files containing raw numerical observations. These files serve as the input for the version-controlled analysis logic.
| File Path | Description | Sample Content |
|---|---|---|
hw1/CHOLESTEROL.txt | Dataset for cholesterol levels. | 205, 166, 236... |
hw1/WEIGHT.txt | Dataset for weight observations. | 62, 51, 56... |
Sources: hw1/CHOLESTEROL.txt:1-28, hw1/WEIGHT.txt:1-12
Summary
The version control and commit workflow is anchored in a reliable data processing pipeline that converts text-based numerical inputs into calculated statistical insights. By utilizing standard libraries like numpy and statistics within a structured file ingestion framework, the project maintains consistent analysis across different datasets such weight and cholesterol metrics.