FlowSharpCode Gets DRAKON Shapes

drakon1.png

I’ve added some select DRAKON shapes for creating flowcharts.  The Python code in the lower right editor is generated from the flowchart, and the output from the run is shown on the left.

PyLint is also now integrated into FlowSharpCode’s PythonCompilerService.  This really improves the development process as many syntactical errors are detected before even running the code.

Also, the code generator creates an execution tree which independent of the language syntax, which means that support for other languages is easily added.  Now granted, the code itself in each of the DRAKON shapes is Python code, but I have some ideas of how to make that code agnostic as well.

1-Wire DS2482 over I2C reading DS199A ROM codes with a Beaglebone

1-wire i2c.png

If that was Greek, it means (probably more Greek) that I got a 1 Wire Pi Plus board[^] wired up to a BeagleboneBlack[^] and am talking to the DS2482 1-wire master[^] over the I2C bus[^] and reading the ROM code off of a DS1990 iButton[^] using a DS1402D iButton cable[^].

Using bottle, also implemented a lightweight server to get iButton codes back:

webpage

Fun stuff!

 

Spinning 3D Box with BeagleBoneBlack and L3G4200D Gyro

bbbgyro.png

Got a fun little project working this weekend.  I wired up an L3G4200D gyroscope module to a BeagleBoneBlack, wrote the Python code to read the sensor data and send it over using RabbitMQ to a C# app on Windows displaying a 3D box.  Now when I rotate the BeagleBoneBlack, the cube mirrors my movements!

Full article will be posted on Code Project in the next week.

 

The Dangers of Duck-Typed Languages

ducktyped.jpg

Try these examples yourself in repl.it

First, is the ambiguity of what something is.  For example, consider this Python example:

> a=[] 

We have no idea what “a” is an array of.  Now, many people will say, it doesn’t matter, you’re not supposed to know, you just operate on the array.  There is a certain point to that, which however can lead to trouble.

Let’s try this:

> a=[1, 2, 3]

Ah, so you think we have an array of integers?  Think again:

> a.append('4')
> a
[1, 2, 3, '4']

Whoa!  That’s an array of mixed types.  In some ways that’s cool, in other ways, that’s dangerous.  Let’s say we want to add one to each element in the array, and we trust that the programmer that created / modified the array knows that it is supposed to be an array of int’s.  But how would they know?  Someone else can come along and not realize that they’re appending the array with a string.  So now we come along, expecting a happy array of int’s, and do this:

> [x+1 for x in a]
TypeError: cannot concatenate 'str' and 'int' objects

Oops – we get a runtime error!

What happens in Ruby:

> a=[1, 2, 3, '4']
[1, 2, 3, "4"]
> a.map {|x| x+1}
no implicit conversion of Fixnum into String

What happens in Javascript:

> a=[1, 2, 3, '4']
[1, 2, 3, '4']
> a.map(function(x) {return x+1})
[2, 3, 4, '41']

Holy Cow, Batman!  In Javascript, the string element is concatenated!

What does this mean?

It means that, among other things, the programmer must be defensive against, not necessarily the errors (sorry, I meant “usage”) of other programmers, but certainly the lack of strong typing in the language.  Consider these “solutions”:

Python:

> [int(x)+1 for x in a]
[2, 3, 4, 5]

Ruby:

> a.map {|x| x.to_i + 1}
[2, 3, 4, 5]

Javascript:

> a.map(function(x) {return parseInt(x)+1})
[ 2, 3, 4, 5 ]

Of course, if you have a floating point number in the array, it’ll be converted to a integer, possibly an unintended side-effect.

Another “stronger” option is to create a class specifically for integer arrays:

Python:

class IntArray(object):
  def __init__(self, arry = []):
    self._verifyElementsAreInts(arry)
    self.arry = arry

  # support appending to array.
  def __add__(self, n):
    self._verify(n)
    self.arry.append(n)
    return self

  # support removing element from array.
  def __sub__(self, n):
    self._verify(n)
    self.arry.remove(n)
    return self

  def _verifyElementsAreInts(self, arry):
    for e in arry:
      self._verify(e)

  def _verify(self, e):
    if (not isinstance(e, int)):
      raise Exception("Array must contain only integers.")


# good array
a = IntArray([1, 2, 3])
a += 4
print(a.arry)
a -= 4
print(a.arry)

try:
  a += '4'
except Exception as e:
  print(str(e))

# bad array
try:
  IntArray([1, 2, 3, '4'])
except Exception as e:
  print(str(e))

With the results:

[1, 2, 3, 4]
[1, 2, 3]
Array must contain only integers.
Array must contain only integers.

What this accomplishes is:

  1. Creating a type checking system that a strongly typed language does for you at compile-time
  2. Inflicting a specific way for programmers to add and remove items from the array (what about inserting at a specific point?)
  3. Actually doesn’t prevent the programmer from manipulating arry directly.
  4. Javascript? It doesn’t have classes, unless you are using ECMAScript 6, in which case, classes are syntactical sugar over JavaScript’s existing prototype-based inheritance.sed inheritance.

The worst part about a duck-typed language is that the “mistake” can be made but not discovered until the program executes the code that expects certain types.  Would you use a duck-typed language as the programming language for, say, a Mars reconnaissance orbiter?  It’ll be fun (and costly) to discover an error in the type when the code executes that fires up the thrusters to do the orbital insertion!

Which is why developers who promote duck-typed languages also strongly promote unit testing.  Unit testing, particularly in duck-typed languages, is the “fix” for making sure you haven’t screwed up the type.

And of course the irony of it all is that underlying, the interpreter still knows the type.

It’s just that you don’t.