Watch out for ‘const’ method in Python

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One thing I feel a little bit not quite as intuitive when I switch to Python is I constantly have to look up whether the method directly updates the contents or it’ll return a different object (of the same type) that I’ll have to overwrite the input variable myself.

An example would be strings and bytes object. replace() sounded like an updating method, but it’s actually a ‘const’ method (a term borrowed from C++ to say that the method does not have side-effects) that does not change the state of the object.

I initially thought this has to do with whether the object is immutable or not, but I tried it on bytearray objects (which is mutable), replace() behaves consistently with the identically named methods in other immutable objects (bytes object, string object): you’ll need to assign the output to self (basically bind the name to the temporary and throw away the original).

bts = b'test'
bts.replace('es', 'oas')       # dumps the output to workspace (can be accessed by _) and do nothing else
bts = bts.replace('es', 'oas') # actually updates bts

 

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Duracell leaks in original package before being used!

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I knew Duracell is known for leaking when left in equipment for too long (too numerous to count: I had it leaked in wireless mouse, remote control, clocks, etc), but I always thought it’s my fault for leaving them in my electronics for a long time.

Today I got my answer: it’s not my fault that the batteries leaked. I just opened a new box of 4 AAA Duracells, and one of the new unused battery (the marking says it expires in 2023. It’s 2019 at the time of writing). I bought them from Tigerdirect so it’s likely to be genuine (on 10/2015). Here’s the pictures:

Not only I am not going to get Duracell batteries even if they are free, I’m going to toss all Duracell I have. It’s nothing but a menace. It’s worse than white label brands as it’s known to leak. It has to be a design or chemical formula or manufacturing process problem they have. By no means it’s an isolated incident.

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Anonymous Functions (MATLAB) vs Lambdas (Python) Anonymous Functions in MATLAB is closure while Lambdas in Python are not

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Lambdas in Python does not play by the same rules as anonymous functions in MATLAB

  • MATLAB takes a snapshot of (capture) the workspace variables involved in the anonymous function AT the time the anonymous function handle is created, thus the captured values will live on afterwards (by definition a proper closure).
  • Lambda in Python is NOT closure! [EDIT: I’ll need to investigate the definition of closure more closely before I use the term here] The free variables involved in lambda expressions are simply read on-the-fly (aka, the last state) when the functor is executed.

It’s kind of a mixed love-and-hate situation for both. Either design choice will be confusing for some use cases. I was at first thrown off by MATLAB’s anonymous function’s full variable capture behavior, then after I get used to it, Python’s Lambda’s non-closure tripped me. Even in the official FAQ, it address the surprise that people are not getting what they expected creating lambdas in a for-loop.

To enable capture in Python, you assign the value you wanted to capture to a lambda input argument (aka, using a bound variable as an intermediary and initialize it with the free variable that needs to be captured), then use the intermediary in the expression. For example:

lambda: ser.close()      # does not capture 'ser'
lambda s=ser: s.close()  # 'ser' is captured by s.

I usually keep the usage of nested functions to the minimum, even in MATLAB, because effectively it’s kind of a compromised ‘global’ between nested levels, or a little bit like protected classes in C++. It breaks encapsulation (intentionally) for functions in your inner circle (nest).

It’s often useful for coding up GUI in MATLAB quick because you need to share access to the UI controls within the same group. For GUI that gets more complicated, I actually avoided nested functions altogether and used *appdata() to share UI object handles.

Functors of nested functions are closures in both MATLAB and Python! Only Lambdas in Python behave slightly differently.

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Python startup management

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The startup script is simply startup.m in whatever folder MATLAB start with.

Now how about Python? For plain Python (anything that you launch in command line, NOT Spyder though), you’ll need to ADD a new environment variable PYTHONSTARTUP to point to your startup script (same drill for Windows and Linux).

For Spyder, it’s Tools>Preferences>IPython console>Startup>”Run a file”:

but you don’t need that if you already have new environment variable PYTHONSTARTUP correctly setup.

 

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