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Introduction

Let's create separate object classes for data reading, automated data reading of an entire directory, plotting (single plot), and automated plotting using the @abstractmethod approach.

Step 1: Define an abstract base class for the data reading and plotting pipelines

from abc import ABC, abstractmethod

class DataReader(ABC):
    @abstractmethod
    def read_data(self, file_path):
        pass

class DataPlotter(ABC):
    @abstractmethod
    def plot_data(self, data):
        pass

Step 2: Create concrete subclasses for data reading

import pandas as pd

class CSVDataReader(DataReader):
    def read_data(self, file_path):
        # Implement CSV data reading logic here
        return pd.read_csv(file_path)

class DirectoryDataReader(DataReader):
    def read_data(self, directory_path):
        # Implement automated data reading from directory logic here
        data_list = []
        # Iterate through all files in the directory and read data from each file
        # Append data to the data_list
        return data_list

Step 3: Create concrete subclasses for plotting

import matplotlib.pyplot as plt

class SinglePlotDataPlotter(DataPlotter):
    def plot_data(self, data):
        # Implement single plot logic here
        plt.plot(data)
        plt.xlabel('X-axis')
        plt.ylabel('Y-axis')
        plt.title('Single Data Plot')
        plt.show()

class AutomatedPlotDataPlotter(DataPlotter):
    def plot_data(self, data_list):
        # Implement automated plotting logic here
        for i, data in enumerate(data_list):
            plt.plot(data, label=f'Data {i+1}')

        plt.xlabel('X-axis')
        plt.ylabel('Y-axis')
        plt.title('Automated Data Plot')
        plt.legend()
        plt.show()

Step 4: Use the data reading and plotting pipelines

def main():
    # Use data reading pipeline
    csv_reader = CSVDataReader()
    data_file_path = "path/to/your_data.csv"
    data = csv_reader.read_data(data_file_path)

    # Use automated data reading pipeline
    dir_reader = DirectoryDataReader()
    directory_path = "path/to/directory_with_data_files"
    data_list = dir_reader.read_data(directory_path)

    # Use single plot data plotting pipeline
    single_plotter = SinglePlotDataPlotter()
    single_plotter.plot_data(data)

    # Use automated data plotting pipeline
    automated_plotter = AutomatedPlotDataPlotter()
    automated_plotter.plot_data(data_list)

if __name__ == "__main__":
    main()

In this example, we have separated the data reading and plotting functionalities into separate classes. The DataReader abstract class has two concrete subclasses, CSVDataReader for reading data from a CSV file and DirectoryDataReader for automated reading of data from a directory containing multiple files. The DataPlotter abstract class also has two concrete subclasses, SinglePlotDataPlotter for plotting a single dataset and AutomatedPlotDataPlotter for automated plotting of multiple datasets.

You can customize each subclass's logic to read specific data formats or implement different plotting styles based on your requirements.

Significance of `@abstractmethod` the Above Example

In simple terms

The @abstractmethod decorator in Python is used to define methods that must be implemented in the subclasses. It ensures that the subclasses provide specific functionality as required by the base class. If a subclass doesn't implement an abstract method, Python will raise an error, forcing developers to provide the necessary functionality for the method to work properly.