Python Bytes 43

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Brian #1: future-fstrings

• A backport of fstrings to python < 3.6
• Include an encoding string the top of your file (this replaces the utf-8 line if you already have it)
• And then write python3.6 fstring code as usual!

      # -*- coding: future_fstrings -*-
      thing = 'world'
      print(f'hello {thing}')
  

• In action:

      $ python2.7 main.py
      hello world
  

• I’m still undecided if I like this sort of monkeying with the language through the encoding mechanism back door.

Michael #2: The Fun of Reinvention

• Keynote from PyCon Israel
• David Beazley rocks it again
• Let’s take Python 3.6 features and see how far we can push them
• Builds an aspect-oriented constraint system using just 3.6 features

Brian #3: Sound Pattern Recognition with Python

• Usingscipy.io.wavfile.read to read a .wav file.
• Looking for peaks (knocks).
• Using minimum values to classify peaks, and minimum distance between peaks.
• This is an interesting start into audio measurements using Python.
• Would be fun to extend to some basic scope measurements, like sampling with a resolution bandwidth, trigger thresholds, pre-trigger time guards, etc.

Michael #4: PEP 550: Execution Context

• From the guys at magic.io
• Adds a new generic mechanism of ensuring consistent access to non-local state in the context of out-of-order execution, such as in Python generators and coroutines.
• Thread-local storage, such as threading.local(), is inadequate for programs that execute concurrently in the same OS thread. This PEP proposes a solution to this problem.
• A few examples of where Thread-local storage (TLS) is commonly relied upon:
  • Context managers like decimal contexts,numpy.errstate, and warnings.catch_warnings.
  • Request-related data, such as security tokens and request data in web applications, language context forgettext etc.
  • Profiling, tracing, and logging in large code bases.
• The motivation from uvloop is obviously at work here.

Brian #5: Intro to Threads and Processes in Python

• Beginner’s guide to parallel programming
• Threads and processes are both useful for different kinds of problems.
• This is a good quick explanation of when and where to use either. With pictures!
• Threads
  • Like mini processes that live inside one process.
  • Share mem space with other threads.
  • Cannot run simultaneously in Python (there are some workarounds), due to GIL.
  • Good for tasks waiting on IO.
• Processes
  • Controlled by OS
  • Can run simultaneously
  • Good for CPU intensive work because you can use multiple cores.

Michael #6: Alternative filesystems for Python

• PyFilesystem: Filesystem Abstraction for Python.
• Work with files and directories in archives, memory, the cloud etc. as easily as your local drive.
• Uses
  • Write code now, decide later where the data will be stored
  • unit test without writing real files
  • upload files to the cloud without learning a new API
  • sandbox your file writing code
• File system backends
  • AppFS Filesystems for application data.
  • S3FS Amazon S3 Filesystem.
  • FTPFS File Transfer Protocol.
  • MemoryFS An in-memory filesystem.
  • MountFS A virtual filesystem that can mount other filesystems.
  • MultiFS A virtual filesystem that combines other filesystems.
  • OSFS OS Filesystem (hard-drive).
  • TarFS Read and write compressed Tar archives.
  • TempFS Contains temporary data.
  • ZipFS Read and write Zip files.
  • and more

Our news

Michael: switch statement extension to Python: github.com/mikeckennedy/python-switch