Running Scripts Locally

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

The local execution environment for this project is designed to process external datasets stored in text files through Python scripts utilizing scientific libraries. The system focuses on statistical analysis, data ingestion, and mathematical modeling, primarily relying on numpy and statistics modules to perform calculations on numerical data sets.

Local scripts are structured to handle file I/O operations, specifically reading lists of numerical values (such as biological measurements) and outputting derived statistical metrics to the console. The primary workflow involves loading data into memory as lists or arrays before executing a series of predefined mathematical functions.
Sources: hw1/p1.py:1-4, hw1/CHOLESTEROL.txt

Data Ingestion and Processing

The core functionality of the local scripts begins with file ingestion. Data is typically stored in .txt files with one numeric value per line. The scripts use iterative reading to populate internal data structures while handling potential non-numeric values through error checking.

File Loading Logic

The script hw1/p1.py implements a specific function to parse text files. It strips whitespace and attempts to cast each line to a float, appending it to a global data list.

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:7-16, hw1/WEIGHT.txt

Execution Workflow

The following diagram illustrates the data flow from a local text file through the processing logic to the final statistical output.

The workflow ensures that only valid numeric data from files like CHOLESTEROL.txt or WEIGHT.txt is passed to the calculation engine.
Sources: hw1/p1.py:18-20, hw1/CHOLESTEROL.txt

Statistical Analysis Module

Once data is loaded locally, the system performs a suite of eight primary statistical calculations. These calculations utilize both the standard statistics library and the numpy package for optimized numerical operations.

Calculated Metrics

The system generates the following metrics for any provided input file:

Metric	Implementation Method	Description
Mean	np.mean(data)	Arithmetic average of the dataset.
Median	np.median(data)	The middle value when data is sorted.
Mode	statistics.mode(data)	The most frequently occurring value.
Range	np.ptp(data)	Peak-to-peak range (max - min).
Std Deviation	np.std(data)	Square root of variance.
Variance	np.var(data)	Average squared deviation from the mean.
CV (1st)	3*(mean - median) / std	First coefficient of variation (Skewness).
CV (2nd)	sum((x-mean)**3) / (N * std**3)	Second coefficient of variation (Moment-based).

Sources: hw1/p1.py:23-52

Dependency Structure

The local environment requires specific Python packages to be installed to support the script execution:

Execution Requirements

To run scripts like hw1/p1.py locally, the environment must meet the following criteria:

1. Python 3.x: Required for with open syntax and statistics module.
2. Library Installation: numpy must be installed via a package manager (e.g., pip install numpy).
3. File Pathing: Input files such as WEIGHT.txt must be located in the same directory as the script or the relative path must be correctly specified in the compute_file call.

In the provided implementation, the script is hardcoded to execute on the local file WEIGHT.txt at the end of the file.
Sources: hw1/p1.py:54, hw1/WEIGHT.txt

The local script execution provides a robust framework for basic biological data analysis, enabling rapid calculation of statistical moments and variations directly from raw text data.