#175 Python string theory with superstring.py

Sponsored by Datadog: pythonbytes.fm/datadog

Special Guest: Matt Harrison

Topic #0: Quick chat about COVID 19.

• What does your world look like?
• Amusing to see news channels, daily shows, etc, learning what we podcasters have figured out years ago

Brian #1: Dictionary Merging and Updating in Python 3.9

• Yong Cui, Ph.D.
• Python 3.9, scheduled for Oct release, will introduce new merge (|) and update (|=) operators, a.k.a. union operators
• Available in alpha 4 and later
• see also pep 584

    # merge
    d1 = {'a': 1, 'b': 2}
    d2 = {'c': 3, 'd': 4}
    d3 = d1 | d2
    # d3 is now {'a': 1, 'b': 2, 'c': 3, 'd': 4}

    # update
    d1 = {'a': 1, 'b': 2}
    d1 |= {'c': 3, 'd': 4}
    # d1 is now {'a': 1, 'b': 2, 'c': 3, 'd': 4}

    # last one wins if contention for both | and |=
    d1 = {'a': 1, 'b': 2}
    d1 |= {'a': 10, 'c': 3, 'd': 4}
    # d1 is now {'a': 10, 'b': 2, 'c': 3, 'd': 4}

Matt #2: superstring

• An efficient library for heavy-text manipulation in Python, that achieves a remarkable memory and CPU optimization.
• Uses Rope (data structure) and optimization techniques.
• Performance comparisons for 50,000 char text
  • memory: 1/20th
  • speed: 1/5th
• Features
  • Fast and Memory-optimized
  • Rich API
    • concatenation (a + b)
    • len() and .length()
    • indexing
    • slicing
    • strip
    • lower
    • upper
• Similar functionalities to python built-in string
• Easy to embed and use.
• I wonder if any of these optimizations could be brought into CPython
• Beware, it’s lacking tests

Michael #3: New pip resolver to roll out this year

• via PyCoders
• The developers of pip are in the process of developing a new resolver for pip (as announced on the PSF blog last year).
• As part of that work, there will be some major changes to how pip determines what to install, based on package requirements.
• What will change:
  • It will reduce inconsistency: it will no longer install a combination of packages that is mutually inconsistent.
  • It will be stricter - if you ask pip to install two packages with incompatible requirements, it will refuse (rather than installing a broken combination, like it does now).
• What you can do to help
  • First and most fundamentally, please help us understand how you use pip by talking with our user experience researchers.
  • Even before we release the new resolver as a beta, you can help by running **pip check** on your current environment.
  • Please make time to test the new version of pip, probably in May.
  • Spread the word!
  • And if you develop or support a tool that wraps pip or uses it to deliver part of your functionality, please make time to test your integration with our beta in May

Matt #4: Covid-19 Data

• Think global act local
• Problem - No local data
• Made my own plots - current status no predictions
• ML works ok for basic model
• Implementing SIR Model with ordinary differential equations scipy odeint function

Brian #5: Why does all() return True if the iterable is empty?

• Carl Johnson
• Q: “Why does all() return True if the iterable is empty? Shouldn’t it return False just like if my_list:would evaluate to False if the list is empty? What’s the thinking behind it returning True?”
• Lesson 1: "… basically doesn’t matter. The Python core team chose to make all([])return True, and whatever their reasons, you can program your way around by adding wrapper functions or if tests. ”
• Lesson 2: “all unicorns are blue”
• Lesson 3: “This is literally a 2,500 year old debate in philosophy. The ancients thought “all unicorns are blue” should be false because there are no unicorns, but modern logic says it is true because there are no unicorns that aren’t blue. Python is just siding with modern predicate logic, but your intuition is also quite common and was the orthodox position until the last few hundred years.”
• Blog post goes into teaching about predicate logic, Socrates, Aristotelean syllogisms, and such.
• And, really, no answer to why. But now, I’ll never forget that all([]) == True.

Michael #6: pytest-monitor

• written by Jean-Sébastien Dieu
• pytest plugin for analyzing resource usage during test sessions
• Analyze your resources consumption through test functions:
  • memory consumption
  • time duration
  • CPU usage
• Keep a history of your resource consumption measurements.
• Compare how your code behaves between different environments.
• Usage: Simply run pytest as usual: pytest-monitor is active by default as soon as it is installed.
• After running your first session, a .pymon sqlite database will be accessible in the directory where pytest was run.
• You will need a valid Python 3.5+ interpreter. To get measures, we rely on:
  • psutil to extract CPU usage
  • memory_profiler to collect memory usage
  • and pytest (obviously!)

Extras:

Michael:

• switchlang is now on pypi : pip install switchlang
• markdown-subtemplate is now on pypi: pip install markdown-subtemplate

Joke:

Light timer fix: https://twitter.com/Sarcastic_Pharm/status/1238060786658009089