Script Execution Flow

Relevant source files

The following files were used as context for generating this wiki page:

• hw1/p1.py
• hw1/CHOLESTEROL.txt
• hw1/WEIGHT.txt
• hw2/p1.py
• hw2/p2.py

Introduction

The script execution flow in this project focuses on the ingestion of clinical or biological data from text files and the subsequent application of statistical analysis. The primary objective is to automate the calculation of central tendency, dispersion, and distribution characteristics for datasets such as cholesterol levels and body weights.

The system utilizes standard Python libraries including numpy for numerical operations and statistics for specialized statistical measures. The flow is designed as a linear pipeline: file discovery, data sanitization, and metric computation.
Sources: hw1/p1.py:1-60, hw1/CHOLESTEROL.txt

Data Ingestion and Parsing

The execution begins by targeting specific flat-file databases. The script employs a robust reading mechanism to handle potential formatting inconsistencies within the source files.

File Loading Logic

The open_file function serves as the entry point for data. It iterates through each line of a provided filename, stripping whitespace and validating that the content is numeric before appending it to a global data structure.

The diagram illustrates the internal logic of the file parser, showing how non-numeric values are caught and skipped to prevent script termination.
Sources: hw1/p1.py:6-16

Statistical Computation Pipeline

Once data is loaded into the data list, the compute_file function orchestrates the calculation of eight distinct statistical metrics.

Computed Metrics Summary

Metric	Library/Method	Description
Mean	np.mean	Arithmetic average of the dataset.
Median	np.median	Middle value of the sorted data.
Mode	statistics.mode	Most frequently occurring value.
Range	np.ptp	Peak-to-peak difference (Max - Min).
Std Deviation	np.std	Measure of data dispersion.
Variance	np.var	Squared deviation from the mean.
CV 1	Pearson's Formula	$3 * (Mean - Median) / Std$
CV 2	Skewness Formula	Third moment calculation for distribution shape.

Sources: hw1/p1.py:22-54

Computational Flow

The following sequence diagram outlines the interaction between the main script execution, the helper functions, and the external libraries.

Data Structures and Global State

The script maintains a global list named data to store processed numeric values. This list is populated during the open_file phase and subsequently accessed by the numpy and statistics functions during the analysis phase.

• data (list): A dynamic array of floating-point numbers derived from files like WEIGHT.txt or CHOLESTEROL.txt.
• Source Data Format: Files contain single numeric values per line, often with trailing whitespace or empty lines.

Sources: hw1/p1.py:5, hw1/WEIGHT.txt, hw1/CHOLESTEROL.txt

Conclusion

The script execution flow provides a structured approach to univariate statistical analysis. By separating file I/O from mathematical computation, the system ensures that data is cleaned before being processed by the NumPy engine, resulting in accurate measures of central tendency and variation for the provided biological datasets.