Motivations:

- Went through all that trouble to rebuild my blog, still nothing on my mind. Let's make something up !
- Holiday cheer ! Since I am nothing if not cheery.

The exercise below uses a database to make a turkey. It does so via an entirely convoluted and pointless series of steps, persisting and restoring a needlessly esoteric set of data with a SQLite database through a series of obnoxious arithmetical translations, however delicious the final answer may be.

It does, at least, illustrate some query/domain model techniques I've been
using on the job as of late. The `@classmethod` constructs you see below are a slimmed
down version of SQLAlchemy's "hybrid attrbutes" example, attribute helpers
that are going to play a much more prominent role in 0.7, where we begin to
de-emphasize `synonym` and `comparable_property` in favor of the hybrid, a
simpler and more versatile component.

In their full form, these
components allow domain classes to define transformational expressions
that work both as SQL expressions as well as instance attributes. Below,
a more succinct version of them is used to associate composed SQL
elements with a domain model. Ramped up to scale in a real application,
I'm able to generate large paginated displays,
containing a wide range of values mathemetically derived from others,
all evaluated in SQL and pulled straight from a single `Query` object
composed using this technique.
In other contexts, when I load individual objects from the database, those
same derivation methods are available to me at the instance level, pulling
their components from the in-memory state of the object and interpreted
by Python, instead of by the relational engine.

Surmounting the barrier of absurdity inherent in this exercise did yield
a small bounty - writing this example uncovered some surprisingly
glaring expression language bugs I have to fix
(see all those `self_group()` calls ? Those shouldn't have to be
there...ouch).

Hope you enjoy it:

packed_turkey = \ '789c2d954b121d350c45e7bd12129cc292fc4d522936c18c82193b604465ef9cd'\ '3c9c0cf7eb66c5d5d5da97f8fbbeaf92f67f59655a3e5aef1fc737a3d1f224f0b'\ 'f6479b6d8cdb7271bc66bb31db1ad9ee1e8d65ac16bb05b6b7c560ea839dd89cc'\ 'c6ca3df36a2b77d3783d339a2ade3cbdcadd576659bc73792516d7b7b3b3676d9'\ '3281509dc11ceb0eb0700284d5d66e031fc969f6c42200e573b719cb8976f8054'\ '84436362fe72c165ef01c9793ee58c44e8c9c8d5b0c7c70561b87f88a7d5f7280'\ '36f143e431011dc9d50444dde7978c013917ec153c9f9b832452e8e5a717ec754'\ '19d36f56aa0ebc7e2e56a7b1707975b93abfbdee7d70941333ba82f335948ff6b'\ 'd192d06092d90103109887572476810d7e92c7a2f2c5407050c5336413a070b40'\ 'f4e00631c84961054c3b908b8782a481a6100182d7c3cd03bcd741a6719d33577'\ '6c5ed78dc82fe9004b60c633cca70db01637a05a80984be59598b1fbf349ae025'\ '905c90caeafebbd002ab16019a41f7750898858c5a807c38edbf75160ae838a38'\ 'e27692b0816986ec1073cce737f83c686020a6970a00aa40c2ac05ec3e4ca98c9'\ '3ec7d84a2e3fe728161caf29ccf9f5b2590f64b8812cb400e5b46bbd7e18767e6'\ '3095f82811e31333c20e44380f1eae95b25e9f2ad310b1bc3243e91c221be7150'\ '37a57a66c6e4b0fe1a422721fc9148a295298aa7fd6f3174f3732a828246733b8'\ 'd94e333b4d3ec404e4a680140a4e9a9515adac1cd2d4ac01cbc18a0ca4d6a6b01'\ 'a4c923db89eaab781114dc323f88804cee7f14cf88d150bb95c78219bd4123a47'\ '2b446ef977a084778544c2811b2009ff8322c0f053820c1b8ae897954282c3def'\ '0d6cdee926bca1c64cd760321d42a04b367b28ff93004b5627fb20ab77965a85e'\ 'd672516a4c59db07f84f852f756a7ad42d712fa5b04c026adc47c05d0acd95b35'\ 'cc2a44da89728589081a23a2cc142c6de2ceb0197c7466198dd36658b287bd6c6'\ '92da2ceaaa08aa08ea22ae0be86b8de1e2027ef07fe07e0274582deebdd5b6189'\ 'ca56bc99b324c479b0e8c2c0a22727fc0ceb8969a33ad60cce7df89b80748969a'\ 'b5d10de7f1fcc167219f2f748a7c3e23bafb7ca7cdcfe7eb78054a72415cf3f93'\ 'b793951482e05e44045047ebb45884cd2a08745e3b7810312316d406f43826493'\ '07dcb4dd2f8585711ebf387e6ac20cd073cdc09b67aba028e2e1f48ab72014e4c'\ '7ef001b9674d91615e5b505e078beada0f9541a2e0a29081b641ace6dc75da5d1'\ '80f17171211f775a25169c7dc9a806e5908af25a667ce4545a7443b42ba95a375'\ 'e69aa63bbaa969ad9d88491e3f9365e7cf5fc0f2961791f' from sqlalchemy import create_engine, Integer, Float, \ CHAR, Column, ForeignKey, \ func, cast, case, and_ from sqlalchemy.orm import Session from sqlalchemy.ext.declarative import declarative_base import math, binascii, zlib img = dict( (line[0], [int(x) for x in line[1:].split(",")]) for line in zlib.decompress(binascii.unhexlify(packed_turkey)).\ split("\n") if line) class classproperty(property): """Class level @property.""" def __get__(desc, self, cls): return desc.fget(cls) Base = declarative_base() class SpiralPoint(Base): """Store a character and its position along a spiral.""" __tablename__ = 'spiral_point' def __init__(self, character, value): self.value = value self.character = character self.sqrt = math.sqrt(value) value = Column(Integer, primary_key=True) """The value.""" character = Column(CHAR(1)) """The character.""" sqrt = Column(Float, nullable=False) """Store the square root of the value, SQLite doesn't have sqrt() built in. (custom SQLite functions are beyond the scope of this "exercise", as it were) """ @classproperty def nearest_odd(cls): """Return the nearest odd number below this SpiralPoint's sqrt.""" return (func.round(cls.sqrt / 2) * 2 - 1).\ self_group().label('nearest_odd') @classproperty def nearest_even(cls): """Return the nearest even number below this SpiralPoint's sqrt.""" return (cls.nearest_odd + 1).\ self_group().label('nearest_even') @classproperty def center_distance(cls): """How far from the 'center' is this value ?""" return (cls.nearest_even / 2).label('center_distance') @classproperty def quadrant(cls): """Which side of the 'center' is this value part of ?""" return (cls.value - ( cls.nearest_odd * cls.nearest_odd )).label('quadrant') e = create_engine('sqlite://', echo=True) Base.metadata.create_all(e) session = Session(e) session.add_all([SpiralPoint(char, val) for char in img for val in img[char]]) session.commit() total_x = 60 total_y = 24 # load our turkey ! turkey = session.query( SpiralPoint.character, cast(case([ ( SpiralPoint.quadrant < SpiralPoint.nearest_even, (total_x / 2) + SpiralPoint.center_distance ), ( and_( SpiralPoint.nearest_even <= SpiralPoint.quadrant, SpiralPoint.quadrant < SpiralPoint.nearest_even * 2 ), ( (total_x / 2) + SpiralPoint.center_distance - 1) - (SpiralPoint.quadrant - SpiralPoint.nearest_even).self_group() ), ( and_( SpiralPoint.nearest_even * 2 <= SpiralPoint.quadrant, SpiralPoint.quadrant < SpiralPoint.nearest_even *3 ), (total_x / 2) - SpiralPoint.center_distance ) ], else_ = ( (total_x / 2) - SpiralPoint.center_distance + 1) + (SpiralPoint.quadrant - (SpiralPoint.nearest_even * 3) ) ), Integer).label('x'), cast(case([ ( SpiralPoint.quadrant < SpiralPoint.nearest_even, (total_y / 2) - SpiralPoint.center_distance + 1 + SpiralPoint.quadrant ), ( and_( SpiralPoint.nearest_even <= SpiralPoint.quadrant, SpiralPoint.quadrant < SpiralPoint.nearest_even * 2 ), (total_y / 2) + SpiralPoint.center_distance), ( and_( SpiralPoint.nearest_even * 2 <= SpiralPoint.quadrant, SpiralPoint.quadrant < SpiralPoint.nearest_even *3 ), (total_y / 2) + SpiralPoint.center_distance - 1 - (SpiralPoint.quadrant - (SpiralPoint.nearest_even * 2)).self_group() ) ], else_ = (total_y / 2) - SpiralPoint.center_distance ), Integer).label('y') ) # serve our turkey ! grid = [ [' ' for x in xrange(total_x)] for y in xrange(total_y) ] for char, x, y in turkey: grid[y][x] = char for g in grid: print "".join([c for c in g])