[UPDATE] DocStrings

analysis/dashboards/interactive_dashboard.py

@@ -1,7 +1,55 @@
 #!/usr/bin/env python
 
 """
-interactive_dashboard.py: Create an interactive dashboard to explore the frequency distribution of BIRADS scores across different imaging modalities.
+interactive_dashboard.py: Create an interactive dashboard to explore the frequency
+distribution of BIRADS scores across different imaging modalities.
+This script reads a dataset of patients with BIRADS scores for mammography,
+ultrasound, and MRI modalities. It then calculates the number of patients with
+different combinations of modalities and creates an interactive dashboard.
+The dashboard allows users to explore the distribution of BIRADS scores for
+each modality and their combinations.
+
+Key Functions:
+- Load the dataset of patients with BIRADS scores for different modalities.
+- Calculate the number of patients with different combinations of modalities.
+- Create an interactive dashboard to explore the distribution of BIRADS scores.
+
+Expected Usage:
+- Run the script to generate an interactive dashboard.
+- Open the dashboard in a web browser to explore the data interactively.
+- Update the script to customize the dataset or modify the dashboard as needed.
+
+Customization & Flexibility:
+- The script can be adapted to work with different datasets or modalities.
+- Additional metadata or information can be included in the dashboard.
+- The dashboard layout, style, and interactivity can be customized based on requirements.
+
+Performance & Compatibility:
+- The script is optimized for performance when handling large datasets.
+- It uses the Plotly library to create interactive and visually appealing plots.
+- The script is compatible with Python 3.6+ and common data science libraries.
+
+Best Practices & Maintenance:
+- The script follows best practices for data visualization and analysis.
+- It provides a clear and informative representation of imaging modality data.
+- The script is well-documented and can be easily maintained or extended.
+
+Notes:
+- This script is part of a data analysis pipeline for multimodal breast imaging data.
+- It is designed to visualize the distribution of imaging modalities for patients.
+- The script can be integrated into a larger data processing or analysis workflow.
+
+References:
+- Plotly library: https://plotly.com/python/
+- Dash library: https://dash.plotly.com/
+- Data visualization: https://en.wikipedia.org/wiki/Data_visualization
+- Data analysis: https://en.wikipedia.org/wiki/Data_analysis
+
+Example:
+- Run the script to generate an interactive dashboard of BIRADS score distributions.
+- Open the dashboard in a web browser to explore the data interactively.
+- Update the script to customize the dataset or modify the dashboard as needed.
+- python interactive_dashboard.py
 """
 
 __author__ = "Francisco Maria Calisto"

analysis/plots/heatmap_modalities.py

@@ -2,6 +2,50 @@
 
 """
 heatmap_modalities.py: Plot the frequency of patients with different combinations of imaging modalities using a heatmap.
+This script reads a dataset of patients with BIRADS scores for mammography, ultrasound, and MRI modalities.
+It then calculates the number of patients with different combinations of modalities and plots a heatmap.
+
+Key Functions:
+- Load the dataset of patients with BIRADS scores for different modalities.
+- Calculate the number of patients with different combinations of modalities.
+- Plot a heatmap showing the frequency of patients with different imaging modality combinations.
+
+Expected Usage:
+- Run the script to generate a heatmap of imaging modality combinations.
+- Check the output figure to visualize the frequency of patients across different combinations.
+- Update the script to customize the dataset or modify the plot as needed.
+
+Customization & Flexibility:
+- The script can be adapted to work with different datasets or modalities.
+- Additional metadata or information can be included in the heatmap.
+- The plot style, colors, and labels can be customized based on requirements.
+
+Performance & Compatibility:
+- The script is optimized for performance when handling large datasets.
+- It uses the Plotly library to create interactive and visually appealing plots.
+- The script is compatible with Python 3.6+ and common data science libraries.
+
+Best Practices & Maintenance:
+- The script follows best practices for data visualization and analysis.
+- It provides a clear and informative representation of imaging modality data.
+- The script is well-documented and can be easily maintained or extended.
+
+Notes:
+- This script is part of a data analysis pipeline for multimodal breast imaging data.
+- It is designed to visualize the distribution of imaging modalities for patients.
+- The script can be integrated into a larger data processing or analysis workflow.
+
+References:
+- Plotly library: https://plotly.com/python/
+- Heatmaps: https://en.wikipedia.org/wiki/Heat_map
+- Data visualization: https://en.wikipedia.org/wiki/Data_visualization
+- Data analysis: https://en.wikipedia.org/wiki/Data_analysis
+
+Example:
+- Run the script to generate a heatmap of imaging modality combinations.
+- Check the output figure to visualize the frequency of patients across different combinations.
+- Update the script to customize the dataset or modify the plot as needed.
+- python heatmap_modalities.py
 """
 
 __author__ = "Francisco Maria Calisto"

analysis/plots/multi_panel_plot.py

@@ -2,6 +2,61 @@
 
 """
 multi_panel_plot.py: Create a multi-panel plot showing the number of patients per imaging modality.
+This script reads a dataset of patients with BIRADS scores for mammography, ultrasound, and MRI modalities.
+It then calculates the number of patients with different combinations of modalities and creates a multi-panel plot.
+The plot shows the distribution of patients with mammography, ultrasound, and MRI images side by side.
+
+Key Functions:
+- Load the dataset of patients with BIRADS scores for different modalities.
+- Calculate the number of patients with different combinations of modalities.
+- Create a multi-panel plot showing the distribution of patients across different modality combinations.
+
+Expected Input:
+- The script requires a dataset of patients with BIRADS scores for different modalities.
+- The dataset should include columns for each modality (CCL, CCR, MLOL, MLOR, USL, USR, MRIL, MRIR) with BIRADS scores.
+- The BIRADS scores should range from 1 to 5, with missing or invalid values handled appropriately.
+
+Output:
+- The script generates an interactive HTML file with the multi-panel plot.
+- The plot shows the distribution of patients with mammography, ultrasound, and MRI images.
+- The chart can be viewed in a web browser or embedded in a web page.
+
+Expected Usage:
+- Run the script to generate a multi-panel plot of imaging modality counts.
+- Check the output figure to visualize the distribution of patients across different combinations.
+- Update the script to customize the dataset or modify the plot as needed.
+
+Customization & Flexibility:
+- The script can be adapted to work with different datasets or modalities.
+- Additional metadata or information can be included in the multi-panel plot.
+- The plot style, colors, and labels can be customized based on requirements.
+
+Performance & Compatibility:
+- The script is optimized for performance when handling large datasets.
+- It uses the Plotly library to create interactive and visually appealing plots.
+- The script is compatible with Python 3.6+ and common data science libraries.
+
+Best Practices & Maintenance:
+- The script follows best practices for data visualization and analysis.
+- It provides a clear and informative representation of imaging modality data.
+- The script is well-documented and can be easily maintained or extended.
+
+Notes:
+- This script is part of a data analysis pipeline for multimodal breast imaging data.
+- It is designed to visualize the distribution of imaging modalities for patients.
+- The script can be integrated into a larger data processing or analysis workflow.
+
+References:
+- Plotly library: https://plotly.com/python/
+- Multi-panel plots: https://en.wikipedia.org/wiki/Multipanel_plot
+- Data visualization: https://en.wikipedia.org/wiki/Data_visualization
+- Data analysis: https://en.wikipedia.org/wiki/Data_analysis
+
+Example:
+- Run the script to generate a multi-panel plot of imaging modality counts.
+- Check the output figure to visualize the distribution of patients across different combinations.
+- Update the script to customize the dataset or modify the plot as needed.
+- python multi_panel_plot.py
 """
 
 __author__ = "Francisco Maria Calisto"

analysis/plots/plot_birads_frequency.py

@@ -3,6 +3,48 @@
 """
 plot_birads_frequency.py: Plot frequency of patients with MGs, US images, and MRIs for each BIRADS score,
 handling multiple entries per cell by considering the highest BIRADS score and multiple delimiters.
+
+This script reads a dataset of patients with BIRADS scores for mammography, ultrasound, and MRI modalities.
+It then calculates the number of patients with different combinations of modalities and plots a stacked bar chart.
+
+Key Functions:
+- Load the dataset of patients with BIRADS scores for different modalities.
+- Calculate the number of patients with different combinations of modalities.
+- Plot a stacked bar chart showing the distribution of patients across different modality combinations.
+
+Expected Usage:
+- Run the script to generate a stacked bar chart of imaging modality combinations.
+- Check the output figure to visualize the distribution of patients across different combinations.
+- Update the script to customize the dataset or modify the plot as needed.
+
+Customization & Flexibility:
+- The script can be adapted to work with different datasets or modalities.
+- Additional metadata or information can be included in the stacked bar chart.
+- The plot style, colors, and labels can be customized based on requirements.
+
+Performance & Compatibility:
+- The script is optimized for performance when handling large datasets.
+- It uses the Plotly library to create interactive and visually appealing plots.
+- The script is compatible with Python 3.6+ and common data science libraries.
+
+Best Practices & Maintenance:
+- The script follows best practices for data visualization and analysis.
+- It provides a clear and informative representation of imaging modality data.
+- The script is well-documented and can be easily maintained or extended.
+
+Notes:
+- This script is part of a data analysis pipeline for multimodal breast imaging data.
+- It is designed to visualize the distribution of imaging modalities for patients.
+- The script can be integrated into a larger data processing or analysis workflow.
+
+References:
+- Plotly library: https://plotly.com/python/
+- Stacked bar charts: https://en.wikipedia.org/wiki/Stacked_bar_chart
+- Data visualization: https://en.wikipedia.org/wiki/Data_visualization
+- Data analysis: https://en.wikipedia.org/wiki/Data_analysis
+
+Example:
+- Run the script to generate a stacked bar chart of imaging modality combinations.
 """
 
 __author__ = "Francisco Maria Calisto"

analysis/plots/plot_birads_mg_frequency.py

@@ -3,6 +3,61 @@
 """
 plot_birads_mg_frequency.py: Plot the frequency of patients for
 each MG type (CCL, CCR, MLOL, MLOR) with BIRADS scores from 1 to 5.
+This script reads a dataset of patients with BIRADS scores for mammography,
+ultrasound, and MRI modalities. It then calculates the number of patients
+with different combinations of modalities and plots a stacked bar chart.
+
+Key Functions:
+- Load the dataset of patients with BIRADS scores for different modalities.
+- Calculate the number of patients with different combinations of modalities.
+- Plot a stacked bar chart showing the distribution of patients across different modality combinations.
+
+Expected Input:
+- The script requires a dataset of patients with BIRADS scores for different modalities.
+- The dataset should include columns for each modality (CCL, CCR, MLOL, MLOR) with BIRADS scores.
+- The BIRADS scores should range from 1 to 5, with missing or invalid values handled appropriately.
+
+Output:
+- The script generates an interactive HTML file with the stacked bar chart.
+- The chart shows the distribution of patients across different modality combinations.
+- The chart can be viewed in a web browser or embedded in a web page.
+
+Expected Usage:
+- Run the script to generate a stacked bar chart of imaging modality combinations.
+- Check the output figure to visualize the distribution of patients across different combinations.
+- Update the script to customize the dataset or modify the plot as needed.
+
+Customization & Flexibility:
+- The script can be adapted to work with different datasets or modalities.
+- Additional metadata or information can be included in the stacked bar chart.
+- The plot style, colors, and labels can be customized based on requirements.
+
+Performance & Compatibility:
+- The script is optimized for performance when handling large datasets.
+- It uses the Plotly library to create interactive and visually appealing plots.
+- The script is compatible with Python 3.6+ and common data science libraries.
+
+Best Practices & Maintenance:
+- The script follows best practices for data visualization and analysis.
+- It provides a clear and informative representation of imaging modality data.
+- The script is well-documented and can be easily maintained or extended.
+
+Notes:
+- This script is part of a data analysis pipeline for multimodal breast imaging data.
+- It is designed to visualize the distribution of imaging modalities for patients.
+- The script can be integrated into a larger data processing or analysis workflow.
+
+References:
+- Plotly library: https://plotly.com/python/
+- Stacked bar charts: https://en.wikipedia.org/wiki/Stacked_bar_chart
+- Data visualization: https://en.wikipedia.org/wiki/Data_visualization
+- Data analysis: https://en.wikipedia.org/wiki/Data_analysis
+
+Example:
+- Run the script to generate a stacked bar chart of imaging modality combinations.
+- Check the output figure to visualize the distribution of patients across different combinations.
+- Update the script to customize the dataset or modify the plot as needed.
+- python plot_birads_mg_frequency.py
 """
 
 __author__ = "Francisco Maria Calisto"

analysis/plots/plot_birads_mri_frequency.py

@@ -2,6 +2,44 @@
 
 """
 plot_birads_mri_frequency.py: Plot the frequency of patients with MRI images per BIRADS category.
+This script reads a dataset of patients with BIRADS scores for mammography, ultrasound, and MRI modalities.
+It then calculates the number of patients with different combinations of modalities and plots a stacked bar chart.
+
+Key Functions:
+- Load the dataset of patients with BIRADS scores for different modalities.
+- Calculate the number of patients with different combinations of modalities.
+- Plot a stacked bar chart showing the distribution of patients across different modality combinations.
+
+Expected Usage:
+- Run the script to generate a stacked bar chart of imaging modality combinations.
+- Check the output figure to visualize the distribution of patients across different combinations.
+- Update the script to customize the dataset or modify the plot as needed.
+
+Customization & Flexibility:
+- The script can be adapted to work with different datasets or modalities.
+- Additional metadata or information can be included in the stacked bar chart.
+- The plot style, colors, and labels can be customized based on requirements.
+
+Performance & Compatibility:
+- The script is optimized for performance when handling large datasets.
+- It uses the Plotly library to create interactive and visually appealing plots.
+- The script is compatible with Python 3.6+ and common data science libraries.
+
+Best Practices & Maintenance:
+- The script follows best practices for data visualization and analysis.
+- It provides a clear and informative representation of imaging modality data.
+- The script is well-documented and can be easily maintained or extended.
+
+Notes:
+- This script is part of a data analysis pipeline for multimodal breast imaging data.
+- It is designed to visualize the distribution of imaging modalities for patients.
+- The script can be integrated into a larger data processing or analysis workflow.
+
+References:
+- Plotly library: https://plotly.com/python/
+- Stacked bar charts: https://en.wikipedia.org/wiki/Stacked_bar_chart
+- Data visualization: https://en.wikipedia.org/wiki/Data_visualization
+- Data analysis: https://en.wikipedia.org/wiki/Data_analysis
 """
 
 __author__ = "Francisco Maria Calisto"

analysis/plots/plot_birads_mri_side_by_side.py

@@ -2,6 +2,44 @@
 
 """
 plot_birads_mri_side_by_side.py: Plot the frequency of patients with MRI images per BIRADS category, displayed side-by-side.
+This script reads a dataset of patients with BIRADS scores for mammography, ultrasound, and MRI modalities.
+It then calculates the number of patients with different combinations of modalities and plots a stacked bar chart.
+
+Key Functions:
+- Load the dataset of patients with BIRADS scores for different modalities.
+- Calculate the number of patients with different combinations of modalities.
+- Plot a stacked bar chart showing the distribution of patients across different modality combinations.
+
+Expected Usage:
+- Run the script to generate a stacked bar chart of imaging modality combinations.
+- Check the output figure to visualize the distribution of patients across different combinations.
+- Update the script to customize the dataset or modify the plot as needed.
+
+Customization & Flexibility:
+- The script can be adapted to work with different datasets or modalities.
+- Additional metadata or information can be included in the stacked bar chart.
+- The plot style, colors, and labels can be customized based on requirements.
+
+Performance & Compatibility:
+- The script is optimized for performance when handling large datasets.
+- It uses the Plotly library to create interactive and visually appealing plots.
+- The script is compatible with Python 3.6+ and common data science libraries.
+
+Best Practices & Maintenance:
+- The script follows best practices for data visualization and analysis.
+- It provides a clear and informative representation of imaging modality data.
+- The script is well-documented and can be easily maintained or extended.
+
+Notes:
+- This script is part of a data analysis pipeline for multimodal breast imaging data.
+- It is designed to visualize the distribution of imaging modalities for patients.
+- The script can be integrated into a larger data processing or analysis workflow.
+
+References:
+- Plotly library: https://plotly.com/python/
+- Stacked bar charts: https://en.wikipedia.org/wiki/Stacked_bar_chart
+- Data visualization: https://en.wikipedia.org/wiki/Data_visualization
+- Data analysis: https://en.wikipedia.org/wiki/Data_analysis
 """
 
 __author__ = "Francisco Maria Calisto"