BME 533 Course Context
Relevant source files
The following files were used as context for generating this wiki page:
The BME 533 Course Context encompasses a collection of computational tools and datasets designed for biomedical engineering statistical analysis. The repository focuses on implementing fundamental statistical metrics and data processing workflows to analyze physiological data such as cholesterol levels and body weight.
The primary scope includes the ingestion of raw text-based datasets and the execution of descriptive statistical functions. This system provides a foundation for more complex data analysis tasks within the BME 533 curriculum, utilizing standard Python libraries for numerical computation and statistical modeling.
Sources: hw1/p1.py, hw1/CHOLESTEROL.txt, hw1/WEIGHT.txt
Data Ingestion and Processing
The system utilizes a modular approach to handle external data sources. Data is typically stored in plain text files with one numeric value per line. The processing logic involves reading these files, cleaning the input by stripping whitespace, and converting strings to floating-point numbers while handling potential non-numeric errors.
Input File Structure
Data files are structured as simple lists of values. For example:
- CHOLESTEROL.txt: Contains 28 readings ranging from 74 to 282.
- WEIGHT.txt: Contains 12 readings ranging from 49 to 71.
Sources: hw1/CHOLESTEROL.txt:1-29, hw1/WEIGHT.txt:1-12
Ingestion Logic
The ingestion process is managed by the open_file function, which populates a global list data used for subsequent calculations.
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
Statistical Analysis Engine
The core functionality of the project resides in the statistical calculation suite. It leverages numpy and the Python built-in statistics module to derive measures of central tendency, dispersion, and distribution shape.
Primary Statistical Metrics
The following table summarizes the metrics calculated by the compute_file function:
| Metric | Implementation Method | Description |
|---|---|---|
| Mean | np.mean(data) | Arithmetic average of the dataset. |
| Median | np.median(data) | Middle value in the sorted dataset. |
| Mode | statistics.mode(data) | Most frequent value in the dataset. |
| Range | np.ptp(data) | Peak-to-peak (Maximum - Minimum) difference. |
| Std Deviation | np.std(data) | Square root of the variance. |
| Variance | np.var(data) | Average of squared deviations from the mean. |
Sources: hw1/p1.py:21-41
Advanced Coefficients
The system calculates specific coefficients to describe the distribution:
- 1st Coefficient of Variation: Calculated as $3 \times \frac{(\text{mean} - \text{median})}{\text{std}}$.
- 2nd Coefficient of Variation: Calculated using the third moment of the mean: $\frac{\sum (x - \text{mean})^3}{n \times \text{std}^3}$.
Sources: hw1/p1.py:43-48
Workflow Architecture
The execution flow involves a sequential transition from file reading to data transformation and final output generation.
This diagram illustrates the operational sequence for processing a single data file.
Sources: hw1/p1.py:6-50
Data Relationships and Structure
The relationship between the analysis script and the data files is straightforward, where the script acts as a consumer of the flat-file database represented by the .txt files.
This ER diagram shows the interaction between the Python logic and the external data storage.
Sources: hw1/p1.py:5-18, hw1/WEIGHT.txt
Summary of Implementation
The BME 533 Course Context repository provides a targeted set of scripts for biomedical data analysis. By combining manual file parsing with robust numerical libraries like numpy, it ensures accurate calculation of essential statistical parameters. The structure emphasizes reusability, allowing the same logic to be applied across different physiological datasets such weight and cholesterol measurements.