refactored code in .py and abstracted code from jupyternotebook to .p…

…y file

.gitignore

@@ -1,3 +1,5 @@
 .ipynb_checkpoints 
 /*/.ipynb_checkpoints 
-.DS_Store
+.DS_Store
+__pycache__
+/*/__pycache__

scripts/plot_functions.py

@@ -0,0 +1,245 @@
+"""
+The functions from this file are reused to show plots in jupyternotebook abstracting code and only displaying plots in the notebook.
+"""
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+import numpy as np
+import plotly.express as px
+import os
+
+def plot_radar_chart(df, graph_name):
+    """
+    Plots a radar chart based on the input DataFrame and saves the plot as a PNG file.
+
+    Parameters:
+    df (pandas.DataFrame): The input DataFrame. It should contain the following columns:
+        'dlr_soft_class', 'howfairis_repository', 'howfairis_license',
+        'howfairis_registry', 'howfairis_citation', 'howfairis_checklist'
+    graph_name (str): The name of the output PNG file.
+
+    Returns:
+    None. A PNG file is saved in the current working directory.
+    """
+    # Check if necessary columns exist in the DataFrame
+    necessary_columns = ['dlr_soft_class', 'howfairis_repository', 'howfairis_license', 'howfairis_registry', 'howfairis_citation', 'howfairis_checklist']
+    for column in necessary_columns:
+        if column not in df.columns:
+            print(f"Column '{column}' not found in the DataFrame.")
+            return
+
+    # Rename the columns
+    df = df.rename(columns={
+        'dlr_soft_class': 'DLR Class',
+        'howfairis_repository': 'Opensource',
+        'howfairis_license': 'License',
+        'howfairis_registry': 'Registry',
+        'howfairis_citation': 'Citation',
+        'howfairis_checklist': 'Checklist'
+    })
+
+    # Convert boolean to int
+    df[['Opensource', 'License', 'Registry', 'Citation', 'Checklist']] = df[['Opensource', 'License', 'Registry', 'Citation', 'Checklist']].astype(int)
+
+    # Filter the DataFrame according to 'DLR Class' values
+    df = df[df['DLR Class'].isin([0.0, 1.0, 2.0])]
+
+    # Convert 'DLR Class' to integer
+    df['DLR Class'] = df['DLR Class'].astype(int)
+
+    # Select the specified columns and group by 'DLR Class'
+    columns = ['DLR Class', 'Opensource', 'License', 'Registry', 'Citation', 'Checklist']
+    df = df[columns].groupby('DLR Class').mean().reset_index()
+
+    # Plotting radar chart
+    labels = df.columns[1:]
+    num_vars = len(labels)
+
+    angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()
+    angles += angles[:1]
+
+    fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
+
+    for i, row in df.iterrows():
+        values = row.drop('DLR Class').tolist()
+        values += values[:1]
+        label = f"Class {int(row['DLR Class'])}"  # Use class as label
+        ax.plot(angles, values, label=label)
+        ax.fill(angles, values, alpha=0.25)
+
+    ax.set_yticklabels([])
+    ax.set_xticks(angles[:-1])
+    ax.set_xticklabels(labels)
+    plt.legend(loc='upper right')
+    plt.savefig(f'{graph_name}.png')
+    plt.show()
+
+def plot_documentation(df, file_path):
+    """
+    Plots a grouped bar chart based on the input DataFrame and saves the plot as a PNG file.
+
+    Parameters:
+    df (pandas.DataFrame): The input DataFrame. It should contain the following columns:
+        'dlr_soft_class', 'readme_content', 'quick_start_guide', 'help_commands'
+    file_path (str): The full path of the output PNG file.
+
+    Returns:
+    None. A PNG file is saved in the specified path.
+    """
+    # Filter the DataFrame according to 'dlr_soft_class' values
+    df = df[df['dlr_soft_class'].isin([0, 1, 2])]
+
+    # Calculate the total count for each 'dlr_soft_class'
+    total_counts = df['dlr_soft_class'].value_counts()
+
+    # Calculate the percentage of True values for each column and 'dlr_soft_class'
+    features = ['readme_content', 'quick_start_guide', 'help_commands']
+    feature_labels = ['Project Information', 'Installation Instructions', 'Usage Guide']
+    percentages = [[(df[(df['dlr_soft_class'] == dlr_class) & (df[feature])].shape[0] / total_counts[dlr_class]) * 100 for feature in features] for dlr_class in [0, 1, 2]]
+
+    # Bar width
+    bar_width = 0.2
+
+    # Positions of the bars on the x-axis
+    r = np.arange(len(features))
+
+    # Create the grouped bar chart
+    for i in range(3):
+        plt.bar(r + i*bar_width, percentages[i], color=plt.cm.viridis(i/2.5), width=bar_width, edgecolor='grey', label=f'Class {i}')
+
+    # Adding labels
+    plt.ylabel('Percentage (%)', fontweight='bold')
+    plt.xticks([r + bar_width for r in range(len(features))], feature_labels)
+
+    # Adding the legend
+    plt.legend()
+
+    # Save the plot
+    os.makedirs(os.path.dirname(file_path), exist_ok=True)
+    plt.savefig(file_path)
+
+    # Show the plot
+    plt.show()
+
+def plot_stacked_bar_reuse(df, column_name, file_path):
+    """
+    Plots a stacked bar chart based on the input DataFrame and saves the plot as a PNG file.
+
+    Parameters:
+    df (pandas.DataFrame): The input DataFrame. It should contain the following columns:
+        'dlr_soft_class' and the column specified in column_name.
+    column_name (str): The name of the column to calculate percentages for.
+    file_path (str): The full path of the output PNG file.
+
+    Returns:
+    None. A PNG file is saved in the specified path.
+    """
+    # Calculate percentages of column_name presence for each 'dlr_soft_class'
+    percentages = df.groupby('dlr_soft_class')[column_name].value_counts(normalize=True).unstack() * 100
+
+    # Plot the stacked bar chart
+    percentages.plot(kind='bar', stacked=True, cmap='viridis')
+
+    # Set labels and title
+    plt.xlabel('DLR Software Class')
+    plt.ylabel('Percentage of Repositories')
+
+    # Move legend outside the plot
+    plt.legend(title='Explicit requirement', labels=['No', 'Yes'], bbox_to_anchor=(1.05, 1), loc='upper left')
+
+    # Save the plot
+    os.makedirs(os.path.dirname(file_path), exist_ok=True)
+    plt.savefig(file_path, bbox_inches='tight')  # Use bbox_inches='tight' to prevent cutting off the legend
+
+    # Show the plot
+    plt.show()
+
+def plot_continuous_integration(df, file_path):
+    """
+    Plots a grouped bar chart based on the input DataFrame and saves the plot as a PNG file.
+
+    Parameters:
+    df (pandas.DataFrame): The input DataFrame. It should contain the following columns:
+        'language', 'dlr_soft_class', 'continuous_integration', 'add_test_rule', 'add_lint_rule'
+    file_path (str): The full path of the output PNG file.
+
+    Returns:
+    None. A PNG file is saved in the specified path.
+    """
+    # Filter the DataFrame according to 'language' and 'dlr_soft_class' values
+    df = df[df['language'].isin(['C++', 'Python', 'R']) & df['dlr_soft_class'].isin([0, 1, 2])]
+
+    # Calculate the total count for each 'dlr_soft_class'
+    total_counts = df['dlr_soft_class'].value_counts()
+
+    # Calculate the percentage of True values for each column and 'dlr_soft_class'
+    features = ['continuous_integration', 'add_test_rule', 'add_lint_rule']
+    percentages = [[(df[(df['dlr_soft_class'] == dlr_class) & (df[feature])].shape[0] / total_counts[dlr_class]) * 100 for feature in features] for dlr_class in [0, 1, 2]]
+
+    # Bar width
+    bar_width = 0.2
+
+    # Positions of the bars on the x-axis
+    r = np.arange(len(features))
+
+    # Create the grouped bar chart
+    for i in range(3):
+        plt.bar(r + i*bar_width, percentages[i], color=plt.cm.viridis(i/2.5), width=bar_width, edgecolor='grey', label=f'Class {i}')
+
+    # Adding labels
+    plt.ylabel('Percentage (%)', fontweight='bold')
+    updated_labels = ['Continuous Integration', 'Linters in CI', 'Automated Testing']
+    plt.xticks([r + bar_width for r in range(len(features))], updated_labels)
+
+    # Adding the legend
+    plt.legend()
+
+    # Save the plot
+    os.makedirs(os.path.dirname(file_path), exist_ok=True)
+    plt.savefig(file_path)
+
+    # Show the plot
+    plt.show()
+
+def plot_comment_start(df, file_path):
+    """
+    Plots a stacked bar chart based on the input DataFrame and saves the plot as a PNG file.
+
+    Parameters:
+    df (pandas.DataFrame): The input DataFrame. It should contain the following columns:
+        'language', 'dlr_soft_class', 'comment_category'
+    file_path (str): The full path of the output PNG file.
+
+    Returns:
+    None. A PNG file is saved in the specified path.
+    """
+    # Step 2: Filter Data by Language
+    df = df[df['language'].isin(['Python', 'C++', 'R'])]
+
+    # Replace 'none' with 'less' and ensure comment_at_start is a categorical type with the correct order
+    df['comment_category'] = df['comment_category'].replace('none', 'less')
+    comment_order = ['less', 'some', 'more', 'most']
+    df['comment_category'] = pd.Categorical(df['comment_category'], categories=comment_order, ordered=True)
+
+    # Step 3: Prepare Data for Visualization
+    # Create a pivot table for the stacked bar plot with percentages
+    pivot_table = df.pivot_table(index='dlr_soft_class', columns='comment_category', aggfunc='size', fill_value=0)
+
+    # Calculate the percentage
+    pivot_table = pivot_table.div(pivot_table.sum(axis=1), axis=0) * 100
+
+    # Step 4: Create Stacked Bar Plot
+    plt.figure(figsize=(10, 6))
+    pivot_table.plot(kind='bar', stacked=True, cmap='viridis')
+    plt.xlabel('DLR Application Class')
+    plt.ylabel('Percentage of Repositories')
+    plt.legend(title='Files with comment at start', bbox_to_anchor=(1.05, 1), loc='upper left')
+    plt.tight_layout()
+
+    # Save the plot
+    os.makedirs(os.path.dirname(file_path), exist_ok=True)
+    plt.savefig(file_path)
+
+    # Show the plot
+    plt.show()