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Add converter between AV2 city coordinate systems, and WGS84 and UTM #28

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merged 14 commits into from
Apr 12, 2022
Merged

Add converter between AV2 city coordinate systems, and WGS84 and UTM #28

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094616d
add gps/utm converter
Mar 26, 2022
c844ffd
add missing type hints
Mar 26, 2022
d690272
add pyproj dependency
Mar 26, 2022
fd6e0cc
improve docstrings
Mar 26, 2022
817a098
reformat black
Mar 26, 2022
925ab94
add missing imports
Mar 26, 2022
33741b4
reformat python black
Mar 26, 2022
af63ba1
add Final type annotations and @unique decorator
Mar 27, 2022
afd0c12
add NDArrayFloat annotations on test cases
Mar 27, 2022
918c421
improve typing and docstrings
Mar 27, 2022
5d29a1d
remove type: ignore, as not needed in CI
Mar 27, 2022
f811951
Fix pytype
Mar 27, 2022
eecae32
fix CI
Mar 27, 2022
117f44f
switch from long -> longitude
Mar 27, 2022
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improve typing and docstrings
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John Lambert committed Mar 27, 2022
commit 918c4214bb236b286363ddf9b86b8157950185a3
14 changes: 7 additions & 7 deletions src/av2/geometry/utm.py
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Expand Up @@ -2,7 +2,7 @@

"""Utilities for converting AV2 city coordinates to UTM or WGS84 coordinate systems."""
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Could we add some information about these two coordinate systems here?

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Sure, I added some references.


from enum import Enum
from enum import unique, Enum
from typing import Dict, Final, Tuple, Union

import numpy as np
Expand Down Expand Up @@ -69,29 +69,29 @@ def convert_city_coords_to_utm(points_city: Union[NDArrayFloat, NDArrayInt], cit
"""Convert city coordinates to UTM coordinates.

Args:
points_city: 2d coordinates, representing query points in the city coordinate frame.
points_city: (N,2) array, representing 2d query points in the city coordinate frame.
city_name: name of city, where query points are located.

Returns:
Points in the UTM coordinate system, as (easting, northing).
Array of shape (N,2), representing points in the UTM coordinate system, as (easting, northing).
"""
lat, long = CITY_ORIGIN_LATLONG_DICT[city_name]
# get (easting, northing) of origin
origin_utm = convert_gps_to_utm(lat=lat, long=long, city_name=city_name)

points_utm: NDArrayFloat = points_city + origin_utm
points_utm: NDArrayFloat = points_city.astype(float) + np.array(origin_utm, dtype=float) # type: ignore
return points_utm


def convert_city_coords_to_wgs84(points_city: Union[NDArrayFloat, NDArrayInt], city_name: CityName) -> NDArrayFloat:
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Suggested change
def convert_city_coords_to_wgs84(points_city: Union[NDArrayFloat, NDArrayInt], city_name: CityName) -> NDArrayFloat:
def convert_city_coords_to_wgs84(points_xy_city: Union[NDArrayFloat, NDArrayInt], city_name: CityName) -> NDArrayFloat:

Prefer xy to make it clear these points are 2D.

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I'm a bit biased towards what we have, since I think points_xy_city is maybe getting a bit too long for a name.

"""Convert city coordinates to WGS84 coordinates.

Args:
points_city: 2d coordinates, representing query points in the city coordinate frame.
points_city: (N,2) array, representing 2d query points in the city coordinate frame.
city_name: name of city, where query points are located.

Returns:
Points in the WGS84 coordinate system, as (lat, long).
Array of shape (N,2), representing points in the WGS84 coordinate system, as (lat, long).
"""
points_utm = convert_city_coords_to_utm(points_city, city_name)

Expand Down