regOct 1.1.1

A module implementing octrees for python

regOct

an implementation of an ordered octree system

Design principles

0. Provide an effective, comprehensive storage medium for cubic, three-dimensional data.
1. The order in which the commands are stored in the file imply the position at which they are executed.
2. State checks within the octree are linear and processor unintensive.
3. The Octree is a self-contained system. All information regarding the octree is stored in the file.

Use

1. General use

Every octree has an integer "level", which defines its size. For a given level, the octree takes the form of a cube with ranges of 0 - 2**level.

regoct.Octree(level) -> Octree

# setting a value:
octree[(x, y, z, level)] = new_value
# level may be omitted if it is 0:
octree[(x, y, z)] = new_value

# getting a value:
value = octree[(x, y, z)]

2. Basic octree IO

In case it is necessary to preserve an octree for later use, it may be saved and loaded to the filesystem via as shown below:

import regoct

original_octree = Octree(2)
# load information into octree

file_path = "file_path.onc"
regoct.save(octree, file_path)
loaded_octree = regoct.load(file_path)

assert original_octree == loaded_octree

This however has a major caveat: the octrees serialized this way may only have internal values of python's base data types:

None(wip), Ellipsis(wip), bool(wip), int, float, complex, str, list, dict, set 

This is addressed followingly.

3. Advanced octree IO

regoct provides a API to modify the serialization process. This is useful for implementing routines for class instances.

The ABC regoct.Interface supplies the tools for implementing the API. It requires the methods to_file(obj, saver: "regoct.SavingStream") and from_file(cls, loader: "regoct.LoadingStream"), where the params saver and loader provide access to the data using a set of methods. These methods in loader and saver mirror each other and are as follows:

regoct.SavingStream	regoct.LoadingStream	description
saver.convert(value)	loader.convert() -> any	dynamic conversion
saver.i8(value)	loader.i8() -> int	8-bit signed integer
saver.i16(value)	loader.i16() -> int	16-bit signed integer
saver.i32(value)	loader.i32() -> int	32-bit signed integer
saver.i64(value)	loader.i64() -> int	64-bit signed integer
saver.u8(value)	loader.u8() -> int	8-bit unsigned integer
saver.u16(value)	loader.u16() -> int	16-bit unsigned integer
saver.u32(value)	loader.u32() -> int	32-bit unsigned integer
saver.u64(value)	loader.u64() -> int	64-bit unsigned integer
saver.f32(value)	loader.f32() -> float	32-bit float
saver.f64(value)	loader.f64() -> float	64-bit float
saver.c64(value)	loader.c64() -> complex	2x 32-bit float complex number
saver.c128(value)	loader.c128() -> complex	2x 64-bit float complex number
saver.Str(value)	loader.Str() -> str	variable-length utf-8 string
saver.List(value)	loader.List() -> list	variable-length dynamic-type list
saver.Dict(value)	loader.Dict() -> dict	variable-length dynamic-type dict
saver.Set(value)	loader.Set() -> set	variable-length dynamic-type set

Be aware that the order in which these methods are called in the to_file and from_file methods should be equal to eachother.

import regoct
from regoct import Interface, SavingStream, LoadingStream

class DataPacket(Interface):
    def __init__(self, *args, **kwds):...

    def to_file(obj, saver: SavingStream):...

    def from_file(cls, loader: LoadingStream):...


original_octree = regoct.Octree(2)
# load information into octree

file_path = "file_path.onc"
DataPacket.save(octree, file_path)
loaded_octree = DataPacket.load(file_path)

assert original_octree == loaded_octree

Visualisation:

The module has an interactable viewer to render octrees in 3d. Currently, this is done with the statement regoct.Display(octree).main(), but since this is very verbose, expect it to change.

And now for something completely different