Plotting the Final Gaussian Function with Input Processing

In this process, we first need to read input information from keyboard input. We can acquire input data through various methods such as calling API functions, reading from files, or communicating with other applications. Once the input data is obtained, we need to process it to prepare for plotting the final Gaussian function. Input processing typically involves data cleaning techniques (removing outliers or invalid entries), data transformation (normalization or standardization), and data formatting (structuring data into appropriate arrays or matrices).

Before generating the Gaussian function, we must determine key parameters such as the mean (μ) and standard deviation (σ) of the Gaussian distribution. These parameters can be calculated from the input data using statistical functions or specified directly by the user. The Gaussian function is mathematically defined as f(x) = (1/(σ√(2π))) * e^(-(x-μ)²/(2σ²)), which we implement using mathematical operations and array computations.

Finally, we utilize plotting libraries (such as matplotlib in Python or plot functions in MATLAB) to visualize the Gaussian function. The implementation involves creating a range of x-values, computing corresponding y-values using the Gaussian equation, and generating the plot with proper labels and formatting. The resulting visualization can be output to files (PNG, PDF formats) or displayed directly on screen for further analysis and research purposes.