traceback_with_variables

Adds variables to python traceback. Simple, lightweight, controllable. Debug reasons of exceptions by logging or pretty printing colorful variable contexts for each frame in a stacktrace, showing every value. Dump locals environments after errors to console, files, and loggers. Works in Jupyter and IPython. Install with pip or conda.

Installation

pip install traceback-with-variables
conda install -c conda-forge traceback-with-variables

🚀 Quick Start

Using without code editing, running your script/command/module:

traceback-with-variables ...script/tool/module with arguments...

Simplest usage in regular Python, for the whole program:

    from traceback_with_variables import activate_by_import

Simplest usage in Jupiter or IPython, for the whole program:

    from traceback_with_variables import activate_in_ipython_by_import

Decorator, for a single function:

    @prints_tb
    # def main(): or def some_func(...):

Context, for a single code block:

    with printing_tb():

Work with traceback lines in a custom manner:

    return '\n'.join(iter_tb_lines(e))

Using a logger [with a decorator, with a context]:

    with printing_tb(file_=LoggerAsFile(logger)):
    # or
    @prints_tb(file_=LoggerAsFile(logger)): 

Colors

Example

How does it save my time?

  • Turn a code totally covered by debug formatting from this:

      def main():
          sizes_str = sys.argv[1]
          h1, w1, h2, w2 = map(int, sizes_str.split())
    -     try:
              return get_avg_ratio([h1, w1], [h2, w2])
    -     except:
    -         logger.error(f'something happened :(, variables = {locals()[:1000]}')
    -         raise
    -         # or
    -         raise MyToolException(f'something happened :(, variables = {locals()[:1000]}')
              
      def get_avg_ratio(size1, size2):
    -     try:
              return mean(get_ratio(h, w) for h, w in [size1, size2])
    -     except:
    -         logger.error(f'something happened :(, size1 = {size1}, size2 = {size2}')
    -         raise
    -         # or
    -         raise MyToolException(f'something happened :(, size1 = {size1}, size2 = {size2}')
    
      def get_ratio(height, width):
    -     try:
              return height / width
    -     except:
    -         logger.error(f'something happened :(, width = {width}, height = {height}')
    -         raise
    -         # or
    -         raise MyToolException(f'something happened :(, width = {width}, height = {height}')
    

    into this (zero debug code):

    + from traceback_with_variables import activate_by_import
    
      def main():
          sizes_str = sys.argv[1]
          h1, w1, h2, w2 = map(int, sizes_str.split())
          return get_avg_ratio([h1, w1], [h2, w2])
              
      def get_avg_ratio(size1, size2):
          return mean(get_ratio(h, w) for h, w in [size1, size2])
    
      def get_ratio(height, width):
          return height / width
    

    And obtain total debug info spending 0 lines of code:

    Traceback with variables (most recent call last):
      File "./temp.py", line 7, in main
        return get_avg_ratio([h1, w1], [h2, w2])
          sizes_str = '300 200 300 0'
          h1 = 300
          w1 = 200
          h2 = 300
          w2 = 0
      File "./temp.py", line 10, in get_avg_ratio
        return mean([get_ratio(h, w) for h, w in [size1, size2]])
          size1 = [300, 200]
          size2 = [300, 0]
      File "./temp.py", line 10, in <listcomp>
        return mean([get_ratio(h, w) for h, w in [size1, size2]])
          .0 = <tuple_iterator object at 0x7ff61e35b820>
          h = 300
          w = 0
      File "./temp.py", line 13, in get_ratio
        return height / width
          height = 300
          width = 0
    builtins.ZeroDivisionError: division by zero
    
  • Automate your logging too:

    logger = logging.getLogger('main')
    
    def main():
        ...
        with printing_tb(file_=LoggerAsFile(logger))
            ...
    
    2020-03-30 18:24:31 main ERROR Traceback with variables (most recent call last):
    2020-03-30 18:24:31 main ERROR   File "./temp.py", line 7, in main
    2020-03-30 18:24:31 main ERROR     return get_avg_ratio([h1, w1], [h2, w2])
    2020-03-30 18:24:31 main ERROR       sizes_str = '300 200 300 0'
    2020-03-30 18:24:31 main ERROR       h1 = 300
    2020-03-30 18:24:31 main ERROR       w1 = 200
    2020-03-30 18:24:31 main ERROR       h2 = 300
    2020-03-30 18:24:31 main ERROR       w2 = 0
    2020-03-30 18:24:31 main ERROR   File "./temp.py", line 10, in get_avg_ratio
    2020-03-30 18:24:31 main ERROR     return mean([get_ratio(h, w) for h, w in [size1, size2]])
    2020-03-30 18:24:31 main ERROR       size1 = [300, 200]
    2020-03-30 18:24:31 main ERROR       size2 = [300, 0]
    2020-03-30 18:24:31 main ERROR   File "./temp.py", line 10, in <listcomp>
    2020-03-30 18:24:31 main ERROR     return mean([get_ratio(h, w) for h, w in [size1, size2]])
    2020-03-30 18:24:31 main ERROR       .0 = <tuple_iterator object at 0x7ff412acb820>
    2020-03-30 18:24:31 main ERROR       h = 300
    2020-03-30 18:24:31 main ERROR       w = 0
    2020-03-30 18:24:31 main ERROR   File "./temp.py", line 13, in get_ratio
    2020-03-30 18:24:31 main ERROR     return height / width
    2020-03-30 18:24:31 main ERROR       height = 300
    2020-03-30 18:24:31 main ERROR       width = 0
    2020-03-30 18:24:31 main ERROR builtins.ZeroDivisionError: division by zero
    
  • Free your exceptions of unnecessary information load:

    def make_a_cake(sugar, eggs, milk, flour, salt, water):
        is_sweet = sugar > salt
        is_vegan = not (eggs or milk)
        is_huge = (sugar + eggs + milk + flour + salt + water > 10000)
        if not (is_sweet or is_vegan or is_huge):
            raise ValueError('This is unacceptable, guess why!')
        ...
    
  • — Should I use it after debugging is over, i.e. in production?

    Yes, of course! That way it might save you even more time. (watch out if your variables have personal data, passwords etc. Filters with be introduced in next versions very soon)


  • Stop this tedious practice in production:

    step 1: Notice some exception in a production service.
    step 2: Add more printouts, logging, and exception messages.
    step 3: Rerun the service.
    step 4: Wait till (hopefully) the bug repeats.
    step 5: Examine the printouts and possibly add some more info (then go back to step 2).
    step 6: Erase all recently added printouts, logging and exception messages.
    step 7: Go back to step 1 once bugs appear.

GitHub