def _compute_distance(df, x, y, p, x_range, y_range, as_series=False):
"""
Compute an array of Hilbert distances from a pandas dataframe
Parameters
----------
df: pd.DataFrame
pandas dataframe containing the coordinate columns
x
Label of the column containing the x-coordinates
y
Label of the column containing the y-coordinates
p: int
Hilbert-curve p value
x_range: tuple
Start (x_range[0]) and stop (x_range[1]) range of Hilbert x
scale in data coordinates
y_range: tuple
Start (y_range[0]) and stop (y_range[1]) range of Hilbert y
scale in data coordinates
as_series: bool (default False)
Whether to return results as a pandas series with index matching df
Returns
-------
np.ndarray
Of Hilbert distances
"""
side_length = 2**p
x_coords = _data2coord(df[x], x_range, side_length)
y_coords = _data2coord(df[y], y_range, side_length)
res = hc.distance_from_coordinates(p, x_coords, y_coords)
if as_series:
res = pd.Series(res, index=df.index)
return res
def _build_distance_grid(p):
"""
Build a (2 ** p) x (2 ** p) array containing the Hilbert distance of
each discrete location in the grid
Parameters
----------
p: int
Hilbert curve order
Returns
-------
np.ndarray
2D array containing the Hilbert curve distances for a curve with
order p
"""
side_length = int(2**p)
distance_grid = np.zeros((side_length, side_length), dtype=np.int64)
for i in range(side_length):
for j in range(side_length):
distance_grid[i, j] = (hc.distance_from_coordinates(p, i, j))
return distance_grid