#215: A Visual Introduction to NumPy

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Special guest: Jason McDonald

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Michael #1: 5 ways I use code as an astrophysicist

• Video by Dr. Becky (i.e. Dr Becky Smethurst, an astrophysicist at the University of Oxford)
• She has a great YouTube channel to check out.
• #1: Image Processing (of galaxies from telescopes)
  • Noise removal
• #2: Data analysis
  • Image features (brightness, etc)
  • One example: 600k “rows” of galaxy properties
• #3: Model fitting
  • e.g. linear fit (visually as well through jupyter)
  • e.g. Galaxies and their black holes grow in mass together
  • Color of galaxies & relative star formation
• #4: Data visualization
• #5: Simulations
  • Beautiful example of galaxies colliding
  • Star meets black hole

Brian #2: A Visual Intro to NumPy and Data Representation

• Jay Alammar
• I’ve started using numpy more frequently in my own work.
• Problem: I think of np.array like a Python list. But that’s not right.
• This visualization guide helped me think of them differently.
• Covers:
  • arrays
    • creating arrays (I didn’t know about np.ones(), np.zeros(), or np.random.random(), so cool)
    • array arithmetic
    • indexing and slicing
    • aggregation with min, max, sum, mean, prod, etc.
  • matrices : 2D arrays
    • matrix arithmetic
    • dot product (with visuals, it takes seconds to understand)
    • matrix indexing and slicing
    • matrix aggregation (both all entries and column or row with axis parameter)
    • transposing and reshaping
  • ndarray: n-dimensional arrays
  • transforming mathematical formulas to numpy syntax
  • data representation
• All with excellent drawings to help visualize the concept.

Jason #3: Qt 6 release (including PySide2)

• Qt 6.0 released on December 8: https://www.qt.io/blog/qt-6.0-released
  • 3D Graphics abstraction layer called RHI (Rendering Hardware Interface), eliminating hard dependency on OpenGL, and adding support for DirectX, Vulkan, and Metal. Uses native 3D graphics on each device by default.
  • Property bindings: https://www.qt.io/blog/property-bindings-in-qt-6
  • A bunch of refactoring to improve performance.
  • QtQuick styling
  • CAUTION: Many Qt 5 add-ons not yet supported!! They plan to support by 6.2 (end of September 2021).
  • Pay attention to your 5.15 deprecation warnings; those things have now been removed in 6.0.

• PySide6/Shiboken6 released December 10: https://www.qt.io/blog/qt-for-python-6-released

  • New minimum version is Python 3.6, supported up to 3.9.
  • Uses properties instead of (icky) getters/setters now. (Combine with snake_case support from 5.15.2)

        from __feature__ import snake_case, true_property
    

• PyQt6 also just released, if you prefer Riverbank’s flavor. (I prefer official.)

Michael #4: Is your GC hyper active? Tame it!

• Let’s think about gc.get_threshold().
• Returns (700, 10, 10) by default. That’s read roughly as:
  • For every net 700 allocations of a collection type, a gen 0 collection runs
  • For every gen 0 collection run, 1/10 times it’ll be upgraded to gen 1.
  • For every gen 1 collection run, 1/10 times it’ll be upgraded to gen 2. Aka for every 100 gen 0 it’s upgraded to gen 2.

• Now consider this:

      query = PageView.objects(created__gte=yesterday).all()
      data = list(query)  # len(data) = 1,500
  

• That’s multiple GC runs. We’ve allocated at least 1,500 custom objects. Yet never ever will any be garbage.

• But we can adjust this. Observe with gc.set_debug(gc.DEBUG_STATS) and consider this ONCE at startup:

      # Clean up what might be garbage
      gc.collect(2)
      # Exclude current items from future GC.
      gc.freeze()
  
      allocs, gen1, gen2 = gc.get_threshold()
      allocs = 50_000  # Start the GC sequence every 10K not 700 class allocations.
      gc.set_threshold(allocs, gen1, gen2)
      print(f"GC threshold set to: {allocs:,}, {gen1}, {gen2}.")
  

• May be better, may be worse. But our pytest integration tests over at Talk Python Training run 10-12% faster and are a decent stand in for overall speed perf.

• Our sitemap was doing 77 GCs for a single page view (77!), now it’s 1-2.

Brian #5: Top 10 Python libraries of 2020

• tryolabs
• criteria
  • They were launched or popularized in 2020.
  • They are well maintained and have been since their launch date.
  • They are outright cool, and you should check them out.

General interest:

1. Typer : FastAPI for CLI applications
   • I can’t believe first commit was right before 2020.
   • Just about a year after the introduction of FastAPI, if you can believe it.
   • Sebastián Ramírez is on 🔥
2. Rich : rich text and beautiful formatting in the terminal.
   • Will McGugan
   • yep. showed up in 2020, amazing.
3. Dear PyGui : Python port of the popular Dear ImGui C++ project.
4. PrettyErrors : transforms stack traces into color coded, well spaced, easier to read stack traces.
5. Diagrams : lets you draw the cloud system architecture without any design tools, directly in Python code.

Machine Learning:

6. Hydra and OmegaConf
7. PyTorch Lightning
8. Hummingbird
9. HiPlot : plotting high dimensional data

Also general

10. Scalene : CPU and memory profiler for Python scripts capable of correctly handling multi-threaded code and distinguishing between time spent running Python vs. native code, without having to modify your code to use it.

Jason #6: Adoption of pyproject.toml — why is this so darned controversial?

The goal of this file is to have a single standard place for all Python tool configurations. It was introduced in PEP 518, but the community seems divided over standardizing.

A bunch of tools are lagging behind others in implementing. Tracked in this repo

A few of the bigger “sticking points”:

• setuptools is working on it: https://github.com/pypa/setuptools/issues/1688
• MyPy: GVR says it “doesn’t solve anything” and closed the PR. https://github.com/python/mypy/issues/5205
• Flake8 objections: https://gitlab.com/pycqa/flake8/-/issues/428#note_251982786
  • Lack of standard TOML parser.
  • “pip to change its behavior so mere presence of the file does not change functionality”
  • Flake9 already implemented what Flake8 wouldn’t. Is this political?
• Bandit is sitting on a PR since 2019: https://github.com/PyCQA/bandit/issues/550
• ReadTheDocs: It’s too much work? — https://github.com/readthedocs/readthedocs.org/issues/7065
• PyOxidizer (shockingly), silent on the topic since 2019: https://github.com/indygreg/PyOxidizer/issues/93

Extras:

Michael:

• PyXLL for Excel people, including Python Jupyter Notebooks in Excel.
• Django 3.1.5 Released
• Python 3.10.0a4 Is Now Available for Testing
• SciPy 1.6.0 Released
• M1 + PyCharm fast? Example.
• Flying solo with the M1 too - apparently 75% is shutdown time for my MBP!

Joke

“Why did the programmer always refuse to check his code into the repository? He was afraid to commit.”