Custom Shell

Creating a Custom Shell in Python

⚠️

This custom shell implementation is for educational purposes and may not include all security features necessary for a production environment. Always ensure proper input validation, error handling, and security measures when implementing command-line interfaces in real-world applications. Consider using more robust libraries like click or argparse for production-grade CLI tools.

Creating a custom shell in Python allows you to build tailored command-line interfaces for your applications, providing users with a familiar and interactive way to interact with your programs. This article will guide you through the process of creating advanced custom shells in Python, complete with real-life examples, use cases, and modern approaches.

Basic Shell Structure with Advanced Features

Let’s start with an enhanced basic shell structure that includes command history and tab completion:

advanced_shell.py

import cmd
import readline
import rlcompleter

class AdvancedShell(cmd.Cmd):
    intro = "Welcome to the Advanced Shell. Type 'help' or '?' to list commands."
    prompt = "(advanced) "

    def __init__(self):
        super().__init__()
        readline.set_completer(rlcompleter.Completer(self.__dict__).complete)
        readline.parse_and_bind("tab: complete")

    def do_hello(self, arg):
        """Say hello to the user"""
        print(f"Hello, {arg or 'there'}!")

    def do_quit(self, arg):
        """Exit the shell"""
        print("Goodbye!")
        return True

    def default(self, line):
        """Handle unknown commands"""
        print(f"Unknown command: {line}")

if __name__ == '__main__':
    AdvancedShell().cmdloop()

This enhanced shell includes tab completion and command history out of the box. Users can now use the up and down arrow keys to navigate through command history and use tab to autocomplete commands.

Real-Life Example: Data Analysis Shell

Let’s create a more complex shell for data analysis using popular libraries like pandas and matplotlib:

data_analysis_shell.py

import cmd
import pandas as pd
import matplotlib.pyplot as plt
from concurrent.futures import ThreadPoolExecutor
import numpy as np

class DataAnalysisShell(cmd.Cmd):
    intro = "Welcome to the Data Analysis Shell. Type 'help' or '?' to list commands."
    prompt = "(data) "

    def __init__(self):
        super().__init__()
        self.data = None
        self.executor = ThreadPoolExecutor(max_workers=4)

    def do_load(self, arg):
        """Load a CSV file: load <filename>"""
        try:
            self.data = pd.read_csv(arg)
            print(f"Loaded data from {arg}")
            print(self.data.head())
        except FileNotFoundError:
            print(f"File not found: {arg}")
        except pd.errors.EmptyDataError:
            print(f"The file {arg} is empty.")

    def do_describe(self, arg):
        """Show summary statistics of the loaded data"""
        if self.data is None:
            print("No data loaded. Use 'load' command first.")
            return
        print(self.data.describe())

    def do_plot(self, arg):
        """Plot a scatter plot: plot <x_column> <y_column>"""
        if self.data is None:
            print("No data loaded. Use 'load' command first.")
            return

        args = arg.split()
        if len(args) != 2:
            print("Usage: plot <x_column> <y_column>")
            return

        x_col, y_col = args
        if x_col not in self.data.columns or y_col not in self.data.columns:
            print("Invalid column names.")
            return

        plt.figure(figsize=(10, 6))
        plt.scatter(self.data[x_col], self.data[y_col])
        plt.xlabel(x_col)
        plt.ylabel(y_col)
        plt.title(f"{y_col} vs {x_col}")
        plt.show()

    def do_correlation(self, arg):
        """Calculate correlation matrix"""
        if self.data is None:
            print("No data loaded. Use 'load' command first.")
            return

        corr_matrix = self.data.corr()
        print(corr_matrix)

        plt.figure(figsize=(10, 8))
        plt.imshow(corr_matrix, cmap='coolwarm')
        plt.colorbar()
        plt.xticks(range(len(corr_matrix.columns)), corr_matrix.columns, rotation=90)
        plt.yticks(range(len(corr_matrix.columns)), corr_matrix.columns)
        plt.title("Correlation Matrix")
        plt.tight_layout()
        plt.show()

    def do_analyze(self, arg):
        """Perform basic analysis on a column: analyze <column_name>"""
        if self.data is None:
            print("No data loaded. Use 'load' command first.")
            return

        if arg not in self.data.columns:
            print(f"Column '{arg}' not found in the data.")
            return

        column = self.data[arg]

        # Use ThreadPoolExecutor for parallel computation
        with self.executor as executor:
            future_mean = executor.submit(np.mean, column)
            future_median = executor.submit(np.median, column)
            future_std = executor.submit(np.std, column)
            future_min = executor.submit(np.min, column)
            future_max = executor.submit(np.max, column)

        print(f"Analysis for column: {arg}")
        print(f"Mean: {future_mean.result():.2f}")
        print(f"Median: {future_median.result():.2f}")
        print(f"Standard Deviation: {future_std.result():.2f}")
        print(f"Minimum: {future_min.result():.2f}")
        print(f"Maximum: {future_max.result():.2f}")

        plt.figure(figsize=(10, 6))
        column.hist(bins=30)
        plt.title(f"Histogram of {arg}")
        plt.xlabel(arg)
        plt.ylabel("Frequency")
        plt.show()

    def do_exit(self, arg):
        """Exit the shell"""
        print("Goodbye!")
        self.executor.shutdown()
        return True

if __name__ == '__main__':
    DataAnalysisShell().cmdloop()

This data analysis shell provides commands for loading CSV files, describing data, plotting scatter plots, calculating correlation matrices, and performing basic analysis on columns. It uses pandas for data manipulation, matplotlib for visualization, and concurrent.futures for parallel computation to improve performance.

Here’s a breakdown of the key features:

The load command uses pandas to read CSV files.
The describe command provides summary statistics using pandas’ describe() method.
The plot command creates scatter plots using matplotlib.
The correlation command calculates and visualizes the correlation matrix.
The analyze command performs basic statistical analysis on a column and plots a histogram.
We use ThreadPoolExecutor to perform some calculations in parallel, improving performance for larger datasets.

To use this shell, you would run it and then use commands like:

(data) load my_data.csv
(data) describe
(data) plot column1 column2
(data) correlation
(data) analyze column1

Hardware Acceleration Example: Image Processing Shell

Let’s create a shell that uses hardware acceleration for image processing tasks using OpenCV with CUDA support:

image_processing_shell.py

import cmd
import cv2
import numpy as np
import matplotlib.pyplot as plt

class ImageProcessingShell(cmd.Cmd):
    intro = "Welcome to the Image Processing Shell. Type 'help' or '?' to list commands."
    prompt = "(image) "

    def __init__(self):
        super().__init__()
        self.image = None
        self.use_gpu = cv2.cuda.getCudaEnabledDeviceCount() > 0

    def do_load(self, arg):
        """Load an image: load <filename>"""
        try:
            self.image = cv2.imread(arg)
            if self.image is None:
                print(f"Failed to load image: {arg}")
            else:
                print(f"Loaded image from {arg}")
                self.show_image(self.image)
        except Exception as e:
            print(f"Error loading image: {e}")

    def do_blur(self, arg):
        """Apply Gaussian blur: blur <kernel_size>"""
        if self.image is None:
            print("No image loaded. Use 'load' command first.")
            return

        try:
            kernel_size = int(arg)
            if self.use_gpu:
                gpu_image = cv2.cuda_GpuMat()
                gpu_image.upload(self.image)
                blurred = cv2.cuda.createGaussianFilter(
                    self.image.dtype, self.image.dtype, (kernel_size, kernel_size), 0
                ).apply(gpu_image)
                self.image = blurred.download()
            else:
                self.image = cv2.GaussianBlur(self.image, (kernel_size, kernel_size), 0)
            print(f"Applied Gaussian blur with kernel size {kernel_size}")
            self.show_image(self.image)
        except ValueError:
            print("Invalid kernel size. Please provide an integer.")

    def do_edge(self, arg):
        """Detect edges using Canny edge detection"""
        if self.image is None:
            print("No image loaded. Use 'load' command first.")
            return

        if self.use_gpu:
            gpu_image = cv2.cuda_GpuMat()
            gpu_image.upload(self.image)
            edges = cv2.cuda.createCannyEdgeDetector(100, 200).detect(gpu_image)
            self.image = edges.download()
        else:
            self.image = cv2.Canny(self.image, 100, 200)
        print("Applied Canny edge detection")
        self.show_image(self.image)

    def do_resize(self, arg):
        """Resize the image: resize <width> <height>"""
        if self.image is None:
            print("No image loaded. Use 'load' command first.")
            return

        try:
            width, height = map(int, arg.split())
            if self.use_gpu:
                gpu_image = cv2.cuda_GpuMat()
                gpu_image.upload(self.image)
                resized = cv2.cuda.resize(gpu_image, (width, height))
                self.image = resized.download()
            else:
                self.image = cv2.resize(self.image, (width, height))
            print(f"Resized image to {width}x{height}")
            self.show_image(self.image)
        except ValueError:
            print("Invalid dimensions. Please provide two integers for width and height.")

    def show_image(self, image):
        plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
        plt.axis('off')
        plt.show()

    def do_exit(self, arg):
        """Exit the shell"""
        print("Goodbye!")
        return True

if __name__ == '__main__':
    ImageProcessingShell().cmdloop()

This image processing shell uses OpenCV with CUDA support for hardware acceleration when available. It provides commands for loading images, applying Gaussian blur, detecting edges, and resizing images. The shell automatically detects if CUDA is available and uses GPU acceleration for these operations when possible.

To use this shell, you would run it and use commands like:

(image) load my_image.jpg
(image) blur 5
(image) edge
(image) resize 800 600

Conclusion

Creating custom shells in Python allows you to build powerful, interactive command-line interfaces tailored to specific needs. By leveraging modern libraries and hardware acceleration techniques, you can create sophisticated tools for various applications, from data analysis to image processing.

Here’s a Mermaid diagram illustrating the structure of our custom shells:

classDiagram
    class CustomShell {
        +intro: str
        +prompt: str
        +cmdloop()
        +do_exit(arg): bool
    }

    class AdvancedShell {
        +__init__()
        +do_hello(arg)
        +default(line)
    }

    class DataAnalysisShell {
        -data: DataFrame
        -executor: ThreadPoolExecutor
        +do_load(arg)
        +do_describe(arg)
        +do_plot(arg)
        +do_correlation(arg)
        +do_analyze(arg)
    }

    class ImageProcessingShell {
        -image: ndarray
        -use_gpu: bool
        +do_load(arg)
        +do_blur(arg)
        +do_edge(arg)
        +do_resize(arg)
        -show_image(image)
    }

    CustomShell <|-- AdvancedShell
    CustomShell <|-- DataAnalysisShell
    CustomShell <|-- ImageProcessingShell

This diagram shows the inheritance structure of our custom shells and their main methods.

When creating custom shells, remember to handle errors gracefully, provide clear documentation for each command, and consider performance optimizations like parallel processing or hardware acceleration where appropriate.

For mathematical operations or documentation, you can use LaTeX syntax. For example, in the data analysis shell, you could add a command to calculate the correlation coefficient:

def do_corr_coef(self, arg):
    """
    Calculate the correlation coefficient between two columns:
    corr_coef <column1> <column2>

    The correlation coefficient is calculated using the formula:

    $$r = \frac{\sum_{i=1}^{n} (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum_{i=1}^{n} (x_i - \bar{x})^2 \sum_{i=1}^{n} (y_i - \bar{y})^2}}$$

    where $\bar{x}$ and $\bar{y}$ are the means of the two columns.
    """
    # Implementation here

By following these examples and guidelines, you can create sophisticated and useful custom shells for a wide variety of applications, leveraging modern Python libraries and hardware acceleration techniques.

For more information on creating advanced command-line interfaces in Python, you can refer to the following resources:

Building Python Based RAT Keylogger