# Technical Specification

## Assumptions

- The tool is a Python command-line application invoked with a single argument that identifies the input data directory.
- The input directory may contain one or more CSV files, and the tool processes every CSV file it finds there.
- Each CSV file contains tabular numeric data suitable for column-wise statistical analysis.
- NumPy is used for all statistical calculations, and Matplotlib is used for plot generation.
- The required output artifact is a PNG image file for each processed CSV file.
- The design does not depend on a graphical user interface or interactive plot display.
- No data cleaning, normalization, or non-CSV format support is required.

## Architecture Overview

The system is a small batch-oriented pipeline with four stages: command-line input, file discovery, statistical analysis, and plot generation/output. The CLI accepts a directory path, validates that it exists, and enumerates the CSV files inside it. Each CSV file is then loaded into a numeric data structure, summary statistics are computed column by column, and the results are rendered into a PNG plot saved to disk.

The design keeps these concerns separated so that the file discovery logic does not depend on the statistics implementation, and the plotting layer only consumes already-computed summary values. This makes the workflow easy to reason about and keeps each part aligned with the requirements.

## Component Structure and Responsibilities

### 1. Command-Line Interface

Responsibility: accept the data directory argument, validate basic usage, and initiate processing.

Inputs:
- A path to the directory containing CSV files.

Outputs:
- A processing request for the discovered CSV files.
- User-facing errors for invalid arguments or missing directories.

Dependencies:
- The file discovery component.

Notes:
- The CLI should be minimal and deterministic.
- Argument parsing should distinguish between missing input, invalid paths, and successful execution.

### 2. File Discovery

Responsibility: enumerate CSV files in the supplied directory and provide the list of files to process.

Inputs:
- The validated directory path from the CLI.

Outputs:
- A collection of CSV file paths.

Dependencies:
- The local filesystem.

Notes:
- Only files with the CSV extension are considered.
- The component should define a stable traversal order so repeated runs produce predictable output ordering.

### 3. Data Loading

Responsibility: read one CSV file into a form suitable for numeric analysis.

Inputs:
- A single CSV file path.

Outputs:
- A numeric matrix or equivalent column-oriented data structure.

Dependencies:
- Standard CSV parsing facilities and NumPy-compatible data handling.

Notes:
- The loader should preserve column structure so statistics can be computed independently for each column.
- Any CSV-specific parsing assumptions should remain narrow and consistent with the data files used by the tool.

### 4. Statistics Calculator

Responsibility: compute mean, minimum, maximum, and standard deviation for each column in the loaded dataset.

Inputs:
- The numeric dataset from the loader.

Outputs:
- Per-column summary statistics for mean, minimum, maximum, and standard deviation.

Dependencies:
- NumPy.

Notes:
- All summary values must come from NumPy-based calculations to match the requirements.
- The output should be structured so the plotting layer can consume it without additional transformation.

### 5. Plot Generator

Responsibility: create a visual representation of the computed statistics for a single CSV file.

Inputs:
- The per-column summary statistics.

Outputs:
- A Matplotlib figure ready to be written as PNG.

Dependencies:
- Matplotlib.

Notes:
- The design only requires that the plot represent the mean, minimum, maximum, and standard deviation across columns.
- Styling, layout, and naming specifics are intentionally left flexible because they are out of scope.

### 6. Output Writer

Responsibility: save the generated plot to disk in PNG format.

Inputs:
- The generated plot figure.
- The source CSV file identity used to derive the output filename.

Outputs:
- A PNG file written to the filesystem.

Dependencies:
- The filesystem and Matplotlib save functionality.

Notes:
- The output should be written non-interactively so users can review files after execution completes.

### 7. Orchestration Layer

Responsibility: coordinate the end-to-end flow for each CSV file.

Inputs:
- The directory path from the CLI.
- The discovered CSV file list.

Outputs:
- One PNG plot per CSV file.
- Completion status for the overall run.

Dependencies:
- All of the components above.

Notes:
- The orchestration layer should process files in a predictable order and continue until all valid CSV files are handled.
- Error handling should be localized so a failure in one file does not obscure which file caused the problem.

## Step-by-Step Implementation Guide

1. Define the command-line entry point that accepts a directory path and performs basic validation.
2. Implement directory scanning to collect all CSV files in the supplied location in a deterministic order.
3. Add CSV loading logic that converts one file into a numeric structure suitable for column-wise NumPy operations.
4. Implement the statistics calculation step for mean, minimum, maximum, and standard deviation on each column.
5. Build the plotting step that visualizes those summary statistics with Matplotlib.
6. Add the output-writing step so each plot is saved as a PNG file instead of being shown interactively.
7. Wire the orchestration flow so the tool repeats the load-analyze-plot-save cycle for every CSV file in the directory.
8. Validate the end-to-end behavior by running the tool against the provided data directory and confirming that PNG files are created for each CSV input.