Python Script Architecture
Relevant source files
The following files were used as context for generating this wiki page:
The Python script architecture in this project is designed for statistical analysis of numerical datasets stored in plain text files. The system utilizes standard libraries such as numpy and statistics to perform descriptive statistical calculations, ranging from basic central tendency measures to advanced coefficients of variation and distribution analysis.
The architecture follows a procedural execution flow where data is ingested from external files, parsed into list-based data structures, and then passed through a series of computational functions to generate metrics like mean, median, mode, and standard deviation. This structure facilitates rapid processing of medical or biological data, such as cholesterol levels or body weight.
Sources: hw1/p1.py:1-6, hw1/CHOLESTEROL.txt:1-28, hw1/WEIGHT.txt:1-12
Data Ingestion and Parsing
The system implements a robust file handling mechanism to convert text-based numerical data into Python-native formats. The open_file function is the primary entry point for data ingestion, reading files line-by-line and stripping whitespace to ensure clean input.
File Processing Logic
The ingestion process includes error handling to manage non-numeric values within source files. When a ValueError is encountered during float conversion, the system logs the error and continues processing subsequent lines, ensuring the integrity of the resulting dataset.
The logic ensures that only valid numeric data from files like WEIGHT.txt or CHOLESTEROL.txt populates the global data list.
Sources: hw1/p1.py:6-16, hw1/WEIGHT.txt:1-12
Statistical Computation Engine
The core logic resides in the compute_file function, which orchestrates the execution of various statistical algorithms. The architecture leverages numpy for high-performance calculations and the statistics module for specialized measures like mode.
Calculated Metrics
The following table outlines the statistical operations performed by the engine:
| Metric | Library/Method | Description |
|---|---|---|
| Mean | np.mean(data) | Arithmetic average of the dataset. |
| Median | np.median(data) | Middle value of the sorted dataset. |
| Mode | statistics.mode(data) | Most frequently occurring value. |
| Range | np.ptp(data) | Peak-to-peak range (Max - Min). |
| Std Deviation | np.std(data) | Measure of data dispersion. |
| Variance | np.var(data) | Square of the standard deviation. |
| 1st CV | 3*(mean - median) / std | First Pearson coefficient of skewness. |
| 2nd CV | sum((x-mean)^3)/(n*std^3) | Measure of relative variability/skewness. |
Sources: hw1/p1.py:18-54
Computational Flow
The system follows a synchronous execution pattern where each metric is calculated and printed to the console sequentially.
Sources: hw1/p1.py:18-56
Skewness and Variation Logic
The architecture includes specific implementations for determining data distribution characteristics through coefficients of variation.
Pearson Coefficient Implementation
The script calculates the first coefficient of variation using the relationship between the mean and median, scaled by the standard deviation. This provides insight into the skewness of the data derived from files such as CHOLESTEROL.txt.
Sources: hw1/p1.py:46-48, hw1/CHOLESTEROL.txt:1-28
Moment-Based Variation
A more complex coefficient is calculated by iterating through the dataset to determine the third moment about the mean, normalized by the cube of the standard deviation and the total count of elements.
# 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:50-54
Data Structures and Dependencies
The script relies on global state management and external library integration to maintain its functional architecture.
| Component | Type | Source/Dependency |
|---|---|---|
data | List | Global list storing parsed floats |
np | Module | numpy for array-based math |
statistics | Module | Python standard library for mode |
file_name | String | Argument passed to compute_file |
Sources: hw1/p1.py:1-6, hw1/p1.py:18-20
Conclusion
The "Python Script Architecture" provides a structured, procedural approach to data analysis. By separating file I/O from statistical computation, the system maintains a clean data pipeline from raw text files to processed analytical metrics. This modularity allows the script to be easily adapted for various numerical datasets like those found in hw1/WEIGHT.txt and hw1/CHOLESTEROL.txt.