memtab_visualizers package

Submodules

Module contents

Memtab Visualizers - A collection of report generators for memory analysis.

class memtab_visualizers.CategoryMemmapReport

Bases: MemmapReport

Category Memory Map Report.

This class extends MemmapReport to provide a category-based memory map visualization, where memory regions are grouped by category.

The report shows memory usage organized by memory region categories, helping users understand how memory is allocated across different types of regions.

See Also

MemmapReport : Base class that provides memory map reporting functionality.

report_name = 'categorymemmap'
class memtab_visualizers.ExcelReport

Bases: object

An Excel report generator for Memtab data.

This class creates Excel workbooks from memory table data, with separate worksheets for symbols and regions.

Note

The output Excel file will be named after the input ELF file, with the extension changed from .elf to .xlsx, and will be saved in the current working directory.

Output Format

The generated Excel file contains:

  • A “Symbols” worksheet containing all symbol information from the Memtab

  • A “Regions” worksheet containing all memory region information from the Memtab

Example

>>> report = ExcelReport()
>>> report.generate_report(memtab, "output.xlsx")
# Creates an Excel file in the current directory with the Memtab data
generate_report(memtab, filename)

creates an excel workbook from the symbols and regions dataframes

Return type:

None

Args:

memtab (Memtab): the memory table to write to excel excel (str): the excel filename

report_name = 'excel'
class memtab_visualizers.MemmapReport

Bases: object

A class that generates memory map visualizations based on analysis results.

The MemmapReport class creates interactive memory map visualizations using the Plotly library, showing the memory layout of code from a Memtab object. It can display memory maps based on either categories or ELF sections.

generate_report(memtab, filename)

This function generates a memory map visualization based on the analysis results. It uses the Plotly library to create the memory map and saves it as an HTML file. The memory map provides a visual representation of the memory layout of the code. The function first determines whether to use category-based or section-based memory mapping. It then iterates through the unique categories or sections and creates scatter plots for each region. The scatter plots represent the memory regions, symbols, and extra space between symbols.

Return type:

None

Args:

response (DataFrame): _description_ html (str): the html filename. this gets prepended with category_memmap_ or section_memmap_ depending on the category_based flag. category_based (bool, optional): _description_. Defaults to False. If True, the memory map is based on categories. If False, it is based on sections.

report_name = 'memmap'
class memtab_visualizers.MemoryProfilerReport

Bases: object

A memory profiler report class for memtab visualizers.

generate_report(memtab, filename)

Generate a text based memory profile, showing overall RAM and ROM, and then the different categories as a total in KB, and then % of RAM/ROM total

Return type:

None

report_name = 'memoryprofiler'
class memtab_visualizers.SummaryReport

Bases: object

A command line summary

generate_report(memtab, filename)

Write a simple, couple-line summary out to stdout. This should be similar to the gnu binutils size command.

Return type:

None

Args:

memtab (Any): the memory table to summarize

report_name = 'summary'
class memtab_visualizers.TreemapReport

Bases: object

A Treemap Report

generate_report(memtab, filename)

This function generates a treemap visualization of the ROM items by category, subcategory, and filename. It uses the Plotly library to create the treemap and saves it as an HTML file. The treemap provides a visual representation of the memory usage by different categories and subcategories. The function first filters the data to include only items with “region”: “ROM”. It then groups the data by category, subcategory, and filename or symbol, and calculates the size of each item.

Return type:

None

Args:

response (DataFrame): _description_ html (str): the filename

report_name = 'treemap'