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Add doc for using cuDF & cuSpatial to calculate Haversine Distance #2

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Aug 3, 2023
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Add doc for using cuDF & cuSpatial to calculate Haversine Distance #2

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42 changes: 42 additions & 0 deletions python/cuspatial/cuspatial/core/spatial/distance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -109,6 +109,48 @@ def haversine_distance(p1: GeoSeries, p2: GeoSeries):
-------
result : cudf.Series
The distance between pairs of points between `p1` and `p2`
Examples
--------
You can calculate the Haversine Distance from a cuDF dataframe or from a GeoSeries list of points

>>> import cudf
>>> import cuspatial
>>> a = {"latitude":[17.1167, 17.1333, 25.333, 25.255, 24.433, 24.262, 35.317, 34.21, 34.566, 31.5, 36.7167, 30.5667, 28.05, 22.8, 35.7297, 36.97, 36.78, 36.8, 36.8, 36.72],
"longitude": [-61.7833, -61.7833, 55.517, 55.364, 54.651, 55.609, 69.017, 62.228, 69.212, 65.85, 3.25, 2.8667, 9.6331, 5.4331, 0.65, 7.79, 3.07, 3.03, 3.04, 4.05]}
>>> df = cudf.DataFrame(data=a)

>>> # Create cuSpatial GeoSeries from cuDF Dataframe
>>> cuGeoSeries = cuspatial.GeoSeries.from_points_xy(df[['longitude', 'latitude']].interleave_columns())

>>> # Create Comparator cuSpatial GeoSeries from a comparator point
>>> df['atlanta_lat'] = 33.7490
>>> df['atlanta_lng'] = -84.3880
>>> atlGeoSeries = cuspatial.GeoSeries.from_points_xy(df[['atlanta_lat', 'atlanta_lng']].interleave_columns())

>>> # Calculate Haversine Distance of cuDF dataframe to comparator point
>>> df['atlanta_dist'] = cuspatial.haversine_distance(cuGeoSeries, atlGeoSeries)
>>> print(df)
latitude longitude atlanta_lat atlanta_lng atlanta_dist
0 17.1167 -61.7833 33.749 -84.388 11961.556540
1 17.1333 -61.7833 33.749 -84.388 11963.392729
2 25.3330 55.5170 33.749 -84.388 12243.126130
3 25.2550 55.3640 33.749 -84.388 12233.867463
4 24.4330 54.6510 33.749 -84.388 12139.822218
5 24.2620 55.6090 33.749 -84.388 12124.483127
6 35.3170 69.0170 33.749 -84.388 13418.538383
7 34.2100 62.2280 33.749 -84.388 13258.725239
8 34.5660 69.2120 33.749 -84.388 13336.375942
9 31.5000 65.8500 33.749 -84.388 12976.749248
10 36.7167 3.2500 33.749 -84.388 13547.245294
11 30.5667 2.8667 33.749 -84.388 12866.528267
12 28.0500 9.6331 33.749 -84.388 12554.544289
13 22.8000 5.4331 33.749 -84.388 11990.825098
14 35.7297 0.6500 33.749 -84.388 13451.775999
15 36.9700 7.7900 33.749 -84.388 13553.372737
16 36.7800 3.0700 33.749 -84.388 13555.211584
17 36.8000 3.0300 33.749 -84.388 13557.641136
18 36.8000 3.0400 33.749 -84.388 13557.588738
19 36.7200 4.0500 33.749 -84.388 13543.496327
"""

if any([not contains_only_points(p1), not contains_only_points(p2)]):
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