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

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 is Python is NOT closure! The local variables involved in the expression 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 closure, then after I get used to it (how else are you going to curry?), 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 argument (intermediary), then use the intermediary in the expression.

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

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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Handling resources that needs to be cleaned up in Python and MATLAB Files, sockets, ports, etc.

Using try/catch to handle resource (files, ports, etc) cleanup is out of fashion in both MATLAB and Python. In MATLAB, it uses a very slick idea based on closures to let you sneak your cleanup function (as a function object) into an onCleanup object that will be executed (in destructor) when that object is cleaned up by being out of scope.

Python does not provide the same mechanism. Instead, it relies on the resource class (like file IO or PySerial) to implement as a Context Manager (has __enter__) and provide the cleanup in the manager’s __exit__ method. Then you use the with keyword with the returned resource object put after as keyword, like this:

with File('test.txt', 'w') as f:
    f.write('SPFCCsMfT!')

The body of with-block will not run (and therefore object f won’t be created) if the with-statement throws an exception. Unfortunately, it’s a fill-or-kill (or try/finally) instead of try/catch. So if the resource failed to open, resource object f is simply not created. No other clue is generated. This is what I hate about the with-statement. There are two ways to kind of get around it, but they are not reliable and might cause other bugs if you don’t keep track of the variable names in the local context:

  1. Check for the existence of the resource object
    if 'f' in dir(): del f   # Avoid name conflicts
    with File() as f
        print("Done");
    if not 'f' in dir():
        print('Cannot create file');
  2. Use the body code to indicate that the with-block is executed
    isSuccess = false     # Signaling
    with File() as f
        isSuccess = true
        print("Done')
    if not isSuccess
        print("Cannot create file");
    
    
  3. Back to the old way
    try:
        f = File();
    except:
        print("Cannot open file")
    else:
        cleanup_obj = onCleanup(lambda x = f: x.custom_cleanup())
        # run core code that uses resource f
    

Actually there’s another mess here. In PySerial, creating the Serial object with a wrong port string will throw an exception as well, which with-as statements cannot handle. Therefore you’ll need to do both:

try:
    ser = serial.Serial(dev_str)
except:
    print(dev_str + " not accessible (either the wrong port of something else opened it)");
else:
    with ser:
        # meat

If your resource initializer does not have context manager built in, and you want a quick-and-dirty solution (given your cleanup is a one-liner). Use my library (lang.py) that recreates onCleanup():

"""
@author: [2019-04-23] Hoi Wong 
"""
class onCleanup:
    '''make sure you 'capture' the lambdas by initializing an intermediate running variable
       e.g. lambda s=ser: s.close()
       lambda: ser.close() will NOT work as ser is not 'captured''''
    def __init__(self, functor):
        self.task = functor;
    def __del__(self):
        self.task()

Then you can use the old way without nesting try/except:

try:
    f = File()
except:
    print("Cannot open file")
else:
    cleanup_obj = onCleanup(lambda x = f: x.custom_cleanup())
    # run core code that uses resource f

Check with the provider of your resource initializer to see if context manager is already implemented. Use onCleanup() only when you don’t have this facility and you don’t want to build a whole context manager (even with decorators) for a one-liner cleanup.

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Python startup management

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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MATLAB and Python paths

MATLAB’s path() is equal to Python’s sys.path().


To add paths in MATLAB, use the obviously named function addpath(). Supply the optional -end argument if you don’t want any potential shadowing (i.e. the folder to import has lower priority if there’s an existing function with the same name).

I generally avoid userpath() or the graphical tools because the results are sticky (persists between sessions). The best way is to exclusively manage your paths with startup.m so you always know what you are getting. If you want full certainty, you can start with restoredefaultpath() in MATLAB.


Python’s suggested these as equivalents of MATLAB’s addpath():

sys.path.insert(0, folder_to_add_to_path)
sys.path.append(folder_to_add_to_path)

but just like MATLAB’s addpath() which works with strings only (not cellstr), these Python options do not work correctly  with Python lists because the methods in sys.path are as primitive as doing [sys.path, new_stuff]:

  1. This means you’ll end up with list of lists if you supplied Python lists as inputs to the above
    (MATLAB will throw an exception if you try to feed it with cellstr instead of polluting your path space with garbage)
  2. This also means it doesn’t check for duplicates! It’ll keep stacking entries!

To address the first problem, we use sys.path.extend() instead. It’s like doing addpath(..., '-end') in MATLAB. If you want it to be inserted at the front (higher priority, shadows existing), you’ll need sys.path = list_of_new_paths + sys.path. For MATLAB, you can make a path string like DOS by using pathsep:

addpath(strjoin(cellstr_of_paths, pathsep)))

Note that  sys.path.extend() is still not polymorphic: it expect iterables so if you feed it a string, which Python will consider it a list of characters, you will get a bunch of one character paths inserted!

On the other hand, DO NOT TRY to get around it in Python with the same trick like MATLAB by doing sys.path.append( ';'.join(path_list)). Python recognize sys.path as a list, NOT a one long string like MATLAB/Windows path, despite insert() and append() accepts only strings!

Aargh!

The second problem (which does NOT happen in MATLAB) is slightly more work. You’ll need to subtract out the existing paths before you add to it so that you won’t drag your system down by casually adding paths as you see fit. One way to do it:

def keep_only_new_set_of_paths(p):
    return set(p)-set(sys.path)

You should organize your programs and libraries in a directory tree structure and use code to crawl the right branch into a path list! Don’t let the lack of built-in support to tempt you to organize files in a mess. Keep the visuals clean as mental gymnastics/overheads can seriously distract you from the real work such as thinking through the requirements and coming up with the right architecture and data structures. If you constantly need to jump a few hoops to do something, do it only once or twice using the proper way (aka, NOT copying-and-pasting boilerplate code), and reuse the infrastructure.

At my previous workplaces, they had dozens and dozens of MATLAB files including all laying flat in one folder. The first thing I did when I join a new team is showing everybody this idiom that recursively adds everything under the folder into MATLAB paths:

addpath(genpath())

Actually the built-in support for recursive directory search sucks for both MATLAB and Python.  Most often what we need is just a list of full paths for a path pattern that we search recursively, basically dir/w/s *. None of them has this right out of the box. They both make you go through the comprehensive data structure returned (let it be tuples from os.walk() in Python or dir() in MATLAB) and so some manipulations to get to this form.

genpath() itself is slow and ugly. It’s basically a recursive wrapper around dir() that cleans up garbage like '.' and '..'.  Instead of getting a newline character, a different row (as a char array) or a different cell (as cellstr), you get semi-colons (;) as pathsep in between. Nonetheless, I still use it because despite I have recursive path tools in my own libraries, I’ll need to load the library first in my startup file, which requires a recursive path tool like genpath(). This bootstraps me out of a chicken-and-egg problem without too much ugly syntax.


Most people will tell you to do a os.walk() and use listcomp to get it in the typical full path form, but I’m not settling for distracting syntax like this. People in the community suggested using glob for a relatively simple alternative to genpath()

Here’s a cleaner way:

def list_subfolders_recursively(p):
    p = p + '/**/' 
    return glob.glob(p, recursive=True);

It’s also worth noting that Python follows Linux’s file search pattern where directory terminates with a filesep (/) while MATLAB’s dir() command follows the OS, which in Windows, it’s *..

Both MATLAB and Python uses ** to mean regardless of levels, but you’ll have to turn on the recursive=True in glob manually. ** is already implied to be recursive in MATLAB’s dir() command.


Considering there’s quite a bit of plumbing associated with weak set of sys.path methods provided in Python, I created a qpath.py next to my startup.py:

''' This is the quick and dirty version to bootstrap startup.py
Should use files.py that issue direct OS calls for speed'''

import sys
import glob

def list_subfolders_recursively(p):
    p = p + '/**/' 
    return glob.glob(p, recursive=True);

def keep_only_new_set_of_paths(p):
    return set(p)-set(sys.path)

def set_of_new_subfolders_recursively(p):
    return keep_only_new_set_of_paths( list_subfolders_recursively(p) )

def add_paths_recursively_bottom(p):
    sys.path.extend(set_of_new_subfolders_recursively(p));

def add_paths_recursively_top(p):
    # operator+() does not take sets
    sys.path = list(set_of_new_subfolders_recursively(p)) + sys.path;

In order to be able to import my qpath module at startup.py before it adds the path, I’ll have put qpath.py in the same folder as startup.py, and request startup.py to add the folder where it lives to the system path (because your current Python working folder might be different from PYTHONSTARTUP) so it recognizes qpath.py.

This is the same technique I came up with for managing localized dependencies in MATLAB: I put the dependencies under the calling function’s folder, and use the path of the .m file for the function as the anchor-path to add paths inside the function. In MATLAB, it’s done this way:

function varargout = f(varargin)
  anchor_path = fileparts( mfilename('fullpath') );
  addpath( genpath(fullfile(anchor_path, 'dependencies')) );
  % Body code goes here

Analogously,

  • Python has __file__ variable (like the good old preprocessor days in C) in place of mfilename().
  • MATLAB’s  mfilename('fullpath') always gives the absolute path, but Python’s  __file__ is absolute if it’s is not in sys.path yet, and relative if it’s already in it.
  • So to ensure absolute path in Python, apply os.path.realpath(__file__). Actually this is a difficult feature to implement in MATLAB. It’s solved by a MATLAB FEX entry called GetFullPath().
  • Python os.path.dirname is the direct equivalent of fileparts() if you just take the first argument.

and in my startup.py (must be in the same folder as pathtools.py):

import os
import sys

sys.path.append(os.path.dirname(os.path.realpath(__file__)))

import pathtool

user_library_path = 'D:/Python/Libraries';
pathtool.add_paths_recursively_bottom(user_library_path)

This way I can make sure all the paths are deterministic and none of the depends on where I start Python.


Now I feel like Python is as mature as Octave. It’s usable, but it’s missing a lot of thoughtful features compared to MATLAB. Python’s entire ecosystem like at least 10 years behind MATLAB in terms of user friendliness. However, Python made it up with some pretty advanced language features that MATLAB doesn’t have, but nonetheless, we are still stuck with quite a bit of boilerplate code in Python, which decreases the expressiveness of the language (I’m a proponent of self-documenting code: variable and function names and their organization should be carefully designed to tell the story; comments are reserved for non-obvious tricks)

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Getting pyinstaller 3.4 to work with Python 3.7

Python is an excellent language, but given that it’s free, it also comes with a lot of conspicuous loose-ends that you will not expect in commercially supported platforms like MATLAB.

Don’t expect everything to work right out of the box in Python. Everything is like 98% there, with the last 2% frustrate the heck out of you when you are rushing to get from point A to point B and you have to iron out a few dozen kinks before you can really start working.

When I tried use pyinstaller (v3.4) to compile my Python (v3.7) program into an executable, I ended up having to jump through a bunch of hoops:

  • pip install pyinstaller gives:
    ModuleNotFoundError: No module named 'cffi'
  • Then I looked up and installed cffi
    pip install cffi
  • After the dependency was addressed manually (it shouldn’t )  pip install pyinstaller worked
  • Then I tried to compile my first Python executable with pyinstaller, and I got this exception:
    File "C:\Python37\lib\site-packages\win32ctypes\core\cffi\_advapi32.py", line 198
    
            ^
        SyntaxError: invalid syntax
  • I searched the exact string and learned that pyinstaller (v3.4) is not ready for Python 3.7 yet! How come pip installer didn’t check for it? I opened up the offending file and looked for line 198 and saw this:
    c_creds.CredentialBlobSize = \
    
        ffi.sizeof(blob_data) - ffi.sizeof('wchar_t')

    It’s a freaking line continuation character \ (actually the extraneous CR before CRLF) that rooster-blocked it.

  • I just deleted the line continuation and merged the two lines, and saved _advapi32.py, then I was able to compile my Python v3.7 code (using pyinstaller 3.4) with no issues.

This is not something you’ll experience as a MATLAB user. The same company, TMW, wrote the MATLAB compiler as well as the rest. The toolbox/packages are released together in one piece so breaking changes that causes failure for the most obvious use case are caught before they get out of the door.

Another example of breaking changes that I ran into: ipdb does not allow you to move cursor backward.

Again, this is the cost associated with free software and access to the latest updates and new features without waiting for April/October (it’s the MATLAB regular release cycle). If hassle and the extra engineering time far exceed licensing MATLAB licensing costs, MATLAB is a better choice, especially if software is just a chore to get your company from point A to point B, and you are willing to pay big bucks to get there quickly and reliably.

Even with free software on the table, your platform choice is always determined by:

  • how much your time is worth wrestling problems
  • how much flexibility do you need (for customizing to your needs)
  • how much you are willing to pay for the licenses and support

In any case, the community did good work. Please consider sponsoring PyInstaller and PSF if you profit immensely from their work.

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Picking an IDE for Python

The native features in MATLAB are often very good most of the time, as I’ve yet to hear anybody spending time to shop for a IDE outside the official one.

Atom has the feel of Maple/MathCAD, and Jupyter Notebook has the feel of Mathematica. Spyder feels like MATLAB the most, but it’s hugely primitive.

IDLE is more miserable than a command prompt. It doesn’t even have the decency to recall command history with up arrow. It’s like freaking DOS before loading doskey.com. Not to mention that single clicking on the window won’t set the cursor to the active command line, which you have to scroll all the way down to click on the bottom line. WTF! I’d rather use the command prompt and give up meaningless syntax coloring.

IPython (in Spyder) is unbearably slow (compare to MATLAB’s editor which I consider slow to the extent that it’s marginally bearable for the interactive features it offers), but at least usable unlike IDLE, and most importantly the output display is pprint (pretty printer) formatted so it’s legible. Just type locals() and see what kind of sh*t Python spits out in IDLE/cmd.exe and you’ll see what I meant.

I simply cannot live without who/whos provided in IPython, but I still don’t like it showing the accessible functions/modules along with the variables (I know, Python doesn’t tell them apart). Nonetheless it’s still weak because these are automagics that doesn’t return the results as Python data (just print). Spyder’s ‘variable explorer’ is the only place I can find that doesn’t include loaded functions/modules. Python should have provided facilities to get the user-introduced variables exclusively and leave the modules to a different function like MATLAB’s import command that shows imported packages/classes.

However, pretty printer doesn’t even come close to MATLAB in terms of the amount of dirty work disp() did to format the text to make it easy to read. Keys in the dictionary shown in pretty printer in Python are not right-aligned like MATLAB struct so we can easily tell keys and values apart. For example:

MATLAB struct shows:
          name: 'S'
          size: [9 1]
         bytes: 7765
         class: 'struct'
        global: 0
        sparse: 0
       complex: 0
       nesting: [1×1 struct]
    persistent: 0

Python with Pretty Printer shows:
{'__name__': '__main__',
 '__doc__': 'Automatically created module for IPython interactive environment',
 '__package__': None,
 '__loader__': None,
 '__spec__': None,
 '__builtin__': <module 'builtins' (built-in)>,
 '__builtins__': <module 'builtins' (built-in)>,
 '_ih': ['', 'locals()'],
 '_oh': {},
 '_dh': ['C:\\Users\\Administrator'],
 'In': ['', 'locals()'],
 'Out': {},
 'get_ipython': <bound method InteractiveShell.get_ipython of <ipykernel.zmqshell.ZMQInteractiveShell object at 0x00000000059B7828>>,
 'exit': <IPython.core.autocall.ZMQExitAutocall at 0x5a3b198>,
 'quit': <IPython.core.autocall.ZMQExitAutocall at 0x5a3b198>,
 '_': '',
 '__': '',
 '___': '',
 '_i': '',
 '_ii': '',
 '_iii': '',
 '_i1': 'locals()'}

I often convert things to MATLAB dataset() because the disp() method is excellent, such as struct2dataset(ver()). table/disp() is nice, but I think they overdid it by defaulting to fancy rich-text that bold the header, which makes it a magnitude of orders slower, and it’s not using the limited visual space effectively to show more data. Python still has a lot more to do in the user-friendly department.

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Implicit ways to store data in a program

The most obvious way to store data is in plain data structures such as arrays and queues, or even a hashtable, but don’t forget these implicit ones:

  • Call stack. As the name say, it’s a stack data structure. It saves the local variables before making another function call. This is often exploited in recursion to avoid passing around an explicit data structure
  • Closures. What closure means is that when you create an anonymous function, the variables involved (other than the arguments) that is saved along (captured) with the function object you created. This can be exploited to make forward iterators or generators
  • Functions. You can make a function that does nothing other than returning a certain piece of data. It’s an excellent way to make avoid the overhead of managing (reading, updating promptly) a config file. Works best when your programming language requires so little typing to specify data (such as MATLAB) that your code is almost as short as a plain text config file.

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不是懶趴是卵脬,不是雞是膣

 

屄/閪 膣 (誤讀 雞)
屌/𨳒 姦 (誤讀 幹) 肏 (誤讀 操)
𡳞/𨶙,㞗/𨳊,杘/𨳍,
脧脧/JerJer
𡳞/卵鳥 (誤讀 懶鳥) チンポ (珍宝)
春袋 蛋蛋 卵脬 きんたま(金玉)
儸柚 屁股 尻(kha)川(tshng) おしり (お尻)

 

 

真趣味的台灣俗諺(尻川): http://www.tma.tw/ltk/107611213.pdf

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Obscure differences between Kanji and Chinese characters

People who already know Chinese characters are often said to have the advantage of being able to pick up Japanese quickly. However, to learn it properly, in addition to the  difference between infix (English, Chinese) and reverse polish (Japanese) notations, it also comes with quite a bit of baggage. It’s the differences that requires work to observe, such as:

  • some made up ‘Chinese’ characters (和製漢語),
  • some are written slightly differently, including artistic variations
  • some has a completely different meaning,
  • some has opposite preferences for using which character in the pair when simplifying
  • and some has drastically different overtones despite they technically mean the same thing
  • the mixture of simplified and traditional characters, occasionally a character written like simplified Chinese means something totally different from traditional Chinese, such as 机(つくえ)which means desk vs 機(キ)which means machines or chances depending on the context.
  • the roles of historical and modern writings are randomly reversed

学習 is a good example. Modern Chinese considers 学 to be more colloquial (e.g. 学武功)and 習 to be more formal (e.g. 習武). Japanese is the other way round for 学ぶ and 習う。学ぶ has a more serious tone.


Actually, the kinds of variations mentioned above applies to regional differences in Chinese languages (such as Taiwanese, Cantonese and Mandarin). Most places agree to write Chinese in a way that can be read directly using Mandarin so that we can at least communicate on paper. So as time goes by, we lost the ability to write in Taiwanese and Cantonese. I hope it’ll change as both dialects are very colorful. Re-expressing them in Mandarin will take away all the flavors in them.

It’s evident that humans can pick up more than one language, so there is no reason to compromise dialects in the process of standardization. People advocating to kill other languages are simpletons who believe in the kind of logic supporting a competitive system: you find ways to make your peers do worse to stay ahead, instead of improving yourself.

Different regions occasionally have different preferences for character order in phrases. Basically we have to watch out for all kinds of combinations. Like 介紹 is used in the same order for Taiwanese/Cantonese/Mandarin to mean introduction, but it’s reversed 紹介(しょうかい) in Japanese. To make it a total mindfuck, Mandarin sticks with 客人 for guests, which is used the same way as Japanese’s 客人(きゃくにん), Taiwanese mostly says 人客, while Cantonese uses both with slight overtones: 客人 is usually used as a particular noun (e.g. 呢位客人) while 人客 is often used as a collective noun (e.g. 人客嚟齊未?), most likely because 客人 sounds more formal than 人客.


Putting traditional and simplified Chinese aside, different regions have different preferences for Chinese characters. I couldn’t tell the difference between traditional Chinese characters used in Hongkong/Macau (港澳繁體) and Taiwan (台灣正體) on Wikipedia, and later learned that it was because I’ve been randomly mixing both all along and nobody ever pointed it out.

裏/着 (Hongkong) vs 裡/著 (Taiwan) are good examples. For these two, modern Japanese sided with Hongkong in the character choices for 裏(うら) and 着(ちゃく). On the other hand, 峰(みね) in Japanese sided with the Taiwanese’s preferred writing 峰, while the 峯 is the ‘officially’ preferred writing in Hongkong.

I remember writing 峰 most of the time even when I was a kid and only used 峯 for names that specifically calls for it. We respect the original writing for names. This is the similar situation as in Japanese: 沢(さわ/たく) is used in most cases and reserve 澤(サワ) for names that specifically requests to be written in this form. The only difference is that I used the official character 峯 exclusively for names, while using the off-label 峰 for the rest.

Speaking of names, there are some similar-looking characters that has the same Japanese sound (かな) but are actually different in both writing and meaning. 斉藤 and 斎藤 are different, but they are easily confused for native Japanese speakers who don’t have any Chinese language background. Here’s the table for comparison:

齊/齐・斉 齋/齋・
Meaning Gathered, organized Plain, house, recitations
Cantonese chai (cai4) jaai (zaai1)
Taiwanese tsè tsai
Mandarin qi2 zhai1
Japanese (音読み:さい) 斉しい・等しく いつき・(潔斎)物忌み

The bottom line is: as language evolves, different regions have different preferences about what can they be sloppy about and what they must be meticulous about. They also reorder/tweak things to make them flow smoothly with their dialect. This means traps for for those learning a new language that are close to what they’ve already mastered.

I came across a document called 常用漢字表 released by the Agency for Cultural Affairs (文化庁) that explains all the quirks of Kanji that was carefully collecting on my own while taking the classes. Wish I had it back in the days. Here’s the link, but I also saved a local copy of 常用漢字表 just in case if their website moves around in the future.

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