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import torch
import reverse_geocoder
import numpy as np
def haversine(pred, gt):
# expects inputs to be np arrays in (lat, lon) format as radians
# N x 2
# calculate the difference in latitude and longitude between the predicted and ground truth points
lat_diff = pred[:, 0] - gt[:, 0]
lon_diff = pred[:, 1] - gt[:, 1]
# calculate the haversine formula components
lhs = torch.sin(lat_diff / 2) ** 2
rhs = torch.cos(pred[:, 0]) * torch.cos(gt[:, 0]) * torch.sin(lon_diff / 2) ** 2
a = lhs + rhs
# calculate the final distance using the haversine formula
c = 2 * torch.arctan2(torch.sqrt(a), torch.sqrt(1 - a))
distance = 6371 * c
return distance
def haversine_np(pred, gt):
# expects inputs to be np arrays in (lat, lon) format as radians
# N x 2
# calculate the difference in latitude and longitude between the predicted and ground truth points
lat_diff = pred[0] - gt[0]
lon_diff = pred[1] - gt[1]
# calculate the haversine formula components
lhs = np.sin(lat_diff / 2) ** 2
rhs = np.cos(pred[0]) * np.cos(gt[0]) * np.sin(lon_diff / 2) ** 2
a = lhs + rhs
# calculate the final distance using the haversine formula
c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))
distance = 6371 * c
return distance
def reverse(pred, gt, area):
df = {}
gt_area = {}
nan_mask = {}
areas = ["_".join(["unique", ar]) for ar in area]
if "unique_continent" in areas:
areas.remove("unique_continent")
for ar in areas:
inter = np.array(gt[ar])
nan_mask[ar] = inter != "nan"
gt_area[ar] = inter[nan_mask[ar]]
location = reverse_geocoder.search(
[
(lat, lon)
for lat, lon in zip(
np.degrees(pred[:, 0].cpu()), np.degrees(pred[:, 1].cpu())
)
]
)
if "continent" in area:
continent = torch.load("continent.pt")
inter = np.array([l.get("cc", "") for l in location])[
nan_mask["unique_country"]
]
df["continent"] = np.array([continent[i] for i in inter])
gt_area["unique_continent"] = np.array(
[continent[i] for i in gt_area["unique_country"]]
)
if "country" in area:
df["country"] = np.array([l.get("cc", "") for l in location])[
nan_mask["unique_country"]
]
if "region" in area:
df["region"] = np.array(
["_".join([l.get("admin1", ""), l.get("cc", "")]) for l in location]
)[nan_mask["unique_region"]]
if "sub-region" in area:
df["sub-region"] = np.array(
[
"_".join([l.get("admin2", ""), l.get("admin1", ""), l.get("cc", "")])
for l in location
]
)[nan_mask["unique_sub-region"]]
if "city" in area:
df["city"] = np.array(
[
"_".join(
[
l.get("name", ""),
l.get("admin2", ""),
l.get("admin1", ""),
l.get("cc", ""),
]
)
for l in location
]
)[nan_mask["unique_city"]]
return df, gt_area
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