Getting Started & Environment Setup

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
• hw3/p1.py

This page provides technical documentation for setting up the environment and executing the statistical analysis modules within the BME533 project. The system is designed to ingest raw numeric datasets from text files and perform a series of descriptive statistical calculations using standard Python libraries.

The project architecture relies on structured input data (such as cholesterol and weight measurements) processed through functional scripts that utilize numerical computing and statistical modules.

Environment Dependencies

The project requires a Python 3 environment with specific external libraries for numerical processing and statistical modeling.

Dependency	Purpose	Implementation Example
numpy	Array manipulation and basic stats (mean, median, range, std, var)	np.mean(data), np.ptp(data)
statistics	Specialized statistical functions (mode)	statistics.mode(data)
python-io	Built-in file handling for .txt ingestion	with open(file_name, 'r') as file

Sources: hw1/p1.py:1-2, hw1/p1.py:7-10

Data Ingestion & Preprocessing

The system processes data from external text files. Each file is expected to contain numeric values on separate lines. The ingestion logic includes basic error handling to skip non-numeric entries during the float conversion process.

def open_file(file_name):
    with open(file_name, 'r') as file:
        for line in file:
            line = line.strip()
            if line:
                try:
                    data.append(float(line))
                except ValueError:
                    print(f"Skipping non-numeric value: {line}")

Sources: hw1/p1.py:6-15

Data Flow Architecture

The following diagram illustrates the flow from raw data files through the ingestion layer to the computation engine.

The diagram shows how the system validates and transforms raw file data into numeric lists for analysis.
Sources: hw1/p1.py:7-20, hw1/CHOLESTEROL.txt:1-28, hw1/WEIGHT.txt:1-12

Statistical Computation Module

The core logic resides in the compute_file function, which executes eight distinct statistical calculations.

Primary Calculations

1. Central Tendency: Calculates Mean, Median, and Mode.
2. Dispersion: Calculates Range (Peak-to-Peak), Standard Deviation, and Variance.
3. Shape & Variation: Calculates the 1st and 2nd Coefficients of Variation.

Coefficient of Variation Implementation

The system implements two distinct formulas for variation:

• 1st Coefficient: Based on the relationship between Mean, Median, and Standard Deviation: 3 * (mean - median) / std.
• 2nd Coefficient: A skewness-related calculation using the third moment about the mean: sum([(x - mean)**3]) / (n * std**3).

Sources: hw1/p1.py:38-45

Execution Sequence

The execution flow follows a synchronous pattern where files are loaded into a global list before the calculation suite is triggered.

The sequence demonstrates the linear processing from file reading to final console output.
Sources: hw1/p1.py:17-46

Summary of Dataset Schema

The system is currently configured to process datasets structured as single-column numeric lists.

File	Type	Sample Range	Context
CHOLESTEROL.txt	Integer/Float	74 - 282	Bio-metric data
WEIGHT.txt	Integer/Float	49 - 71	Physical measurement data

Sources: hw1/CHOLESTEROL.txt:1-28, hw1/WEIGHT.txt:1-12

To conclude, the environment setup for BME533 requires a standard Python distribution with numpy installed. Users initiate analysis by passing target filenames to the compute_file function, which aggregates data into a shared memory structure for statistical processing.