Dynamically Compiling and Implementing a Function

Python allows you to dynamically compile things at the module-level. That’s why the compile() builtin keyword accepts sourcecode and dictionaries of locals and globals, and doesn’t provide a direct way to call a fragment of dynamic (read: textual) sourcecode with arguments (“xyz(arg1, arg2)”). You also can’t directly invoke compiled-code as a generator (where a function that uses “yield” is interpreted as a generator rather than just a function).

However, there’s a loophole, and it’s very elegant and consistent to Python. You simply have to wrap your code in a function definition, and then pull the function from the local scope. You can then call it as desired:

import hashlib
import random

def _compile(arg_names, code):
    name = "(lambda compile)"
    # Needs to start with a letter.
    id_ = 'a' + str(random.random())
    code = "def " + id_ + "(" + ', '.join(arg_names) + "):n" + 
           'n'.join(('  ' + line) for line in code.replace('r', '').split('n')) + 'n'

    c = compile(code, name, 'exec')
    locals_ = {}
    exec(c, globals(), locals_)
    return locals_[id_]

code = """
return a * b * c
"""

c = _compile(['a', 'b', 'c'], code)
print(c(1, 2, 3))

Serialize a Generator in Python

2 Comments

Simply inherit from the list class, and override the __iter__ method. A great example, from SO:

import json

def gen():
    yield 20
    yield 30
    yield 40

class StreamArray(list):
    def __iter__(self):
        return gen()

a = [1,2,3]
b = StreamArray()

print(json.dumps([1,a,b]))

SSL for Python (M2Crypto) on Windows

M2Crypto is the most versatile and popular SSL library for Python. Naturally, it takes a predictable amount of burden getting it to work under Windows.

If you’re lucky, you can find a precompiled binary online, and circumvent the heartache. Though many pages have come and gone, here is one that works, courtesy of the grr project: M2Crypto.

Not only do they provide a [non-trivial] set of instructions on how to build the binaries yourself, but they present binaries, as well. Though the binaries are hosted on Google Code (and unlikely to go away), I’ve hosted them, too, for brevity:

M2CryptoWindows

Note that these binaries, as given, are not installable Python packages. I have produced and published two such packages to PyPI, for your convenience:

M2CryptoWin32
M2CryptoWin64

SQLAlchemy and MySQL Encoding

I recently ran into an issue with the encoding of data coming back from MySQL through sqlalchemy. This is the first time that I’ve encountered such issues since this project first came online, months ago.

I am using utf8 encoding on my database, tables, and columns. I just added a new column, and suddenly my pages and/or AJAX calls started failing with one of the following two messages, respectively:

  • UnicodeDecodeError: ‘ascii’ codec can’t decode byte 0x96 in position 5: ordinal not in range(128)
  • UnicodeDecodeError: ‘utf8’ codec can’t decode byte 0x96 in position 5: invalid start byte

When I tell the stored procedure to return an empty string for the new column instead of its data, it works. The other text columns have an identical encoding.

It turns out that SQLAlchemy defaults to the latin1 encoding. If you need something different, than you’re in for a surprise. The official solution is to pass the “encoding” parameter to create_engine. This is the example from the documentation:

engine = create_engine("mysql://scott:tiger@hostname/dbname", encoding='latin1', echo=True)

In my case, I tried utf8. However, it still didn’t work. I don’t know if that ever works. It wasn’t until I uncovered a StackOverflow entry that I found the answer. I had to append “?charset=utf8” to the DSN string:

mysql+mysqldb://username:password@hostname:port/database_name?charset=utf8

The following are the potential explanations:

  • Since I copy and pasted values that were set into these columns, I accidentally introduced a character that was out of range.
  • The two encodings have an overlapping set of codes, and I finally introduced a character that was supported by one but not the other.

Whatever the case, it’s fixed and I’m a few hours older.

Using ctypes to Read Binary Data from a Double-Pointer

This is a sticky and exotic use-case of ctypes. In the example below, we make a call to some library function that treats ptr like a double-pointer, and sets ptr to point to a buffer and sets count with the number of bytes that are available there. The data at the pointer may have one or more NULL bytes that should not be interpreted as terminators.

from ctypes import *

ptr = ctypes.c_char_p()
count = ctypes.c_size_t()

r = library.some_call(
        ctypes.cast(ctypes.byref(ptr), 
                    ctypes.POINTER(ctypes.c_void_p)), 
        ctypes.byref(count))

if r != 0:
    raise ValueError("Library call failed.")

data = ctypes.string_at(ptr, count.value)

Snappy for Very Easy Compression

Snappy is a fast compression algorithm by Google. When I’ve used it, it’s been for socket compression, though it can be used for file compression, too.

For socket compression in Python, the examples are embarrassingly simple. First, import the module:

import snappy

When you establish the socket (which we’ll refer to as s), create the compressor and decompressor:

c = snappy.StreamCompressor()
d = snappy.StreamDecompressor()

From that point on, just pass all of your outgoing data through c.add_chunk(), and all of your read data through d.decompress(). add_chunk() and decompress() may return an empty string from time-to-time (obviously).

Beautiful Flowcharts and UML Without Installation

Unfortunately, there are no Google Searches that render good suggestions for cheap sites that create beautiful flowcharts. The recommended products seem to either be locally installable or exorbitantly priced.

I now present two. They can both import from Visio, can both integrate with Confluence, and both cost between $5 and $10 a month.

Gliffy

  • Slighty more “comical” and flowing, but the difference may be negligible
  • Google Documents integration available with all paid accounts
  • Has an academic discount

Gliffly Flowchart Example

Lucidchart

  • Less effort and less clicks to control alignment and establish links than most services
  • You can leave comments on components
  • Revision history
  • Integrates with Google Documents, but only with team accounts
  • You can do Hangouts and chat from the design screen.

Lucidchart Flowchart Example

Edit: I also received a last-minute mentioned for draw.io from @DiegoTerzano. Though the designs are thinner/leaner, it’s not a shoddy interface.