This README file is made for the Python version. All code is similar to the C# version however the folowing are different:

• dimention -- the number of dimensions (must be > 0) in C#
• iterator -- the number of iterations used in constructing the Hilbert curve (must be > 0) in C#
• Also, since this project was made before Python 3.11.0, no Iterable library and/or datatypes are not used in the script.
• They are here to help just to understand the code. In C#, Iterable are replaced with List as you have to select datatypes upfront.

This is a package to convert between one dimensional distance along a Hilbert curve_, h, and n-dimensional points, (x_0, x_1, ... x_n-1). There are two important parameters,

• n -- the number of dimensions (must be > 0)
• p -- the number of iterations used in constructing the Hilbert curve (must be > 0) *See notes for C# version

We consider an n-dimensional hypercube_ of side length 2^p. This hypercube contains 2^{n p} unit hypercubes (2^p along each dimension). The number of unit hypercubes determine the possible discrete distances along the Hilbert curve (indexed from 0 to 2^{n p} - 1).

Install the package with pip (this partis irrelevent for C#, but the way to use the library is similar),

pip install hilbertcurve

You can calculate points given distances along a hilbert curve,

.. code-block:: python

from hilbertcurve.hilbertcurve import HilbertCurve p=1; n=2 hilbert_curve = HilbertCurve(p, n) distances = list(range(4)) points = hilbert_curve.points_from_distances(distances) for point, dist in zip(points, distances): print(f'point(h={dist}) = {point}')

point(h=0) = [0, 0] point(h=1) = [0, 1] point(h=2) = [1, 1] point(h=3) = [1, 0]

You can also calculate distances along a hilbert curve given points,

.. code-block:: python

points = [[0,0], [0,1], [1,1], [1,0]] distances = hilbert_curve.distances_from_points(points) for point, dist in zip(points, distances): print(f'distance(x={point}) = {dist}')

distance(x=[0, 0]) = 0 distance(x=[0, 1]) = 1 distance(x=[1, 1]) = 2 distance(x=[1, 0]) = 3

The HilbertCurve class has four main public methods. *

• point_from_distance(distance: int) -> Iterable[int]
• points_from_distances(distances: Iterable[int], match_type: bool=False) -> Iterable[Iterable[int]]
• distance_from_point(point: Iterable[int]) -> int
• distances_from_points(points: Iterable[Iterable[int]], match_type: bool=False) -> Iterable[int]

Arguments that are type hinted with Iterable[int] have been tested with lists, tuples, and 1-d numpy arrays. Arguments that are type hinted with Iterable[Iterable[int]] have been tested with list of lists, tuples of tuples, and 2-d numpy arrays with shape (num_points, num_dimensions). The match_type key word argument forces the output iterable to match the type of the input iterable.

The HilbertCurve class also contains some useful metadata derived from the inputs p and n. For instance, you can construct a numpy array of random points on the hilbert curve and calculate their distances in the following way,

.. code-block:: python

from hilbertcurve.hilbertcurve import HilbertCurve p=1; n=2 hilbert_curve = HilbertCurve(p, n) num_points = 10_000
points = np.random.randint(
low=0,
high=hilbert_curve.max_x + 1,
size=(num_points, hilbert_curve.n)
) distances = hilbert_curve.distances_from_points(points) type(distances)

list

distances = hilbert_curve.distances_from_points(points, match_type=True) type(distances)

.. figure:: n2_p3.png

The figure above shows the first three iterations of the Hilbert curve in two (n=2) dimensions. The p=1 iteration is shown in red, p=2 in blue, and p=3 in black. For the p=3 iteration, distances, h, along the curve are labeled from 0 to 63 (i.e. from 0 to 2^{n p}-1). This package provides methods to translate between n-dimensional points and one dimensional distance. For example, between (x_0=4, x_1=6) and h=36. Note that the p=1 and p=2 iterations have been scaled and translated to the coordinate system of the p=3 iteration.