07.01.2021       Выпуск 368 (04.01.2021 - 10.01.2021)       Вопросы и обсуждения

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Special guest: Jason McDonald

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)
• #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
• 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.
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:

1. Hydra and OmegaConf
2. PyTorch Lightning
3. Hummingbird
4. HiPlot : plotting high dimensional data

Also general

1. 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.

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”:

Extras:

Michael:

Joke

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