Satelitendaten Geostandorten zuordnen¶

Beispiel: Tracking der Internationalen Raumstation mit Dask¶

In diesem Notebook werden wir zwei APIs verwenden:

Wir werden sie verwenden, um den ISS-Standort und die nächste Durchlaufzeit in Bezug auf eine Liste von Städten zu verfolgen. Um unsere Diagramme zu erstellen und Daten intelligent zu parallelisieren, verwenden wir Dask, insbesondere Dask Delayed.

1. Importe¶

[1]:

import datetime
import logging

from operator import itemgetter
from time import sleep

import httpx
import numpy as np

from dask import delayed
from sklearn.metrics import DistanceMetric

2. Logger¶

[2]:

logger = logging.getLogger()

3. Latitude- und Longitude-Paare aus einer Liste von Städten¶

s.a. Location APIs

[3]:

from geopy.geocoders import Nominatim


def get_lat_long(address):
    loc = Nominatim(user_agent="Geopy Library")
    get_loc = loc.geocode(address)
    return {
        "name": get_loc.address,
        "lat": get_loc.latitude,
        "long": get_loc.longitude,
    }

[4]:

get_lat_long("Berlin, Germany")

[4]:

{'name': 'Berlin, Deutschland', 'lat': 52.5173885, 'long': 13.3951309}

[5]:

locations = []
for city in [
    "Seattle, Washington",
    "Miami, Florida",
    "Berlin, Germany",
    "Singapore",
    "Wellington, New Zealand",
    "Beirut, Lebanon",
    "Beijing, China",
    "Nairobi, Kenya",
    "Cape Town, South Africa",
    "Buenos Aires, Argentina",
]:
    locations.append(get_lat_long(city))
    sleep(2)

[6]:

locations

[6]:

[{'name': 'Seattle, King County, Washington, United States',
  'lat': 47.6038321,
  'long': -122.330062},
 {'name': 'Miami, Miami-Dade County, Florida, United States',
  'lat': 25.7741566,
  'long': -80.1935973},
 {'name': 'Berlin, Deutschland', 'lat': 52.5173885, 'long': 13.3951309},
 {'name': 'Singapore', 'lat': 1.357107, 'long': 103.8194992},
 {'name': 'Wellington, Wellington City, Wellington, 6011, New Zealand / Aotearoa',
  'lat': -41.2887953,
  'long': 174.7772114},
 {'name': 'بيروت, البسطة التحتا, باشورة, محافظة بيروت, لبنان',
  'lat': 33.8892265,
  'long': 35.5025585},
 {'name': '北京市, 中国', 'lat': 39.9057136, 'long': 116.3912972},
 {'name': 'Nairobi, Zimmerman, Soko ya Mawe, Nairobi, Kenya',
  'lat': -1.2890006,
  'long': 36.8172812},
 {'name': 'Cape Town, City of Cape Town, Western Cape, 8001, South Africa',
  'lat': -33.9288301,
  'long': 18.4172197},
 {'name': 'Buenos Aires, Comuna 1, Ciudad Autónoma de Buenos Aires, Argentina',
  'lat': -34.6095579,
  'long': -58.3887904}]

4. ISS-Daten abrufen und Durchlaufzeiten der Städte ermitteln¶

[7]:

def get_spaceship_location():
    resp = httpx.get("http://api.open-notify.org/iss-now.json")
    location = resp.json()["iss_position"]
    return {
        "lat": float(location.get("latitude")),
        "long": float(location.get("longitude")),
    }

[8]:

def great_circle_dist(lon1, lat1, lon2, lat2):
    dist = DistanceMetric.get_metric("haversine")
    lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])

    ll = [[lat1, lon1], [lat2, lon2]]
    kms = 6367
    return (kms * dist.pairwise(ll)).max()

[9]:

def iss_dist_from_loc(issloc, loc):
    distance = great_circle_dist(
        issloc.get("long"),
        issloc.get("lat"),
        loc.get("long"),
        loc.get("lat"),
    )
    logger.info(f"ISS is {int(distance)}km from {loc.get('name')}")
    return distance

[10]:

def iss_pass_near_loc(loc):
    resp = httpx.get(
        "http://api.open-notify.org/iss-pass.json",
        params={"lat": loc.get("lat"), "lon": loc.get("long")},
    )
    data = resp.json().get("response")[0]
    td = datetime.datetime.fromtimestamp(
        data.get("risetime"),
        tz=datetime.timezone.utc,
    ) - datetime.datetime.now(tz=datetime.timezone.utc)
    m, s = divmod(int(td.total_seconds()), 60)
    h, m = divmod(m, 60)
    logger.info(
        "ISS will pass near %s in %02d:%02d:%02d",
        loc.get("name"),
        h,
        m,
        s,
    )
    return td.total_seconds()

[11]:

iss_dist_from_loc(get_spaceship_location(), locations[2])

INFO:root:ISS is ~12639km from Berlin, 10117, Germany

[11]:

12639.759939298825

[12]:

iss_pass_near_loc(locations[2])

INFO:root:ISS will pass near Berlin, 10117, Germany in 00:25:14

[12]:

1514.253889

5. Erstellen einer `delayed`-Pipeline¶

[13]:

output = []

for loc in locations:
    issloc = delayed(get_spaceship_location)()
    dist = delayed(iss_dist_from_loc)(issloc, loc)
    output.append((loc.get("name"), dist))

closest = delayed(lambda x: sorted(x, key=itemgetter(1))[0])(output)

[14]:

closest

[14]:

Delayed('lambda-6e51d667-459d-449d-8f2f-e9c7cdbf6b43')

6. DAG anzeigen¶

[15]:

closest.visualize()

[15]:

../_images/clean-prep_dask-pipeline_22_0.png

7. `compute()`¶

[16]:

closest.compute()

INFO:root:ISS is ~4685km from Miami, Miami-Dade County, Florida, USA
INFO:root:ISS is ~15205km from Beirut, Beirut Governorate, Lebanon
INFO:root:ISS is ~5919km from Seattle, King County, Washington, USA
INFO:root:ISS is ~6279km from Autonomous City of Buenos Aires, Comuna 6, Autonomous City of Buenos Aires, Argentina
INFO:root:ISS is ~12625km from Berlin, 10117, Germany
INFO:root:ISS is ~13137km from Cape Town, City of Cape Town, Western Cape, 8001, South Africa
INFO:root:ISS is ~16194km from Singapore
INFO:root:ISS is ~16298km from Nairobi, Kenya
INFO:root:ISS is ~13905km from Beijing, Dongcheng District, Beijing, 100010, China
INFO:root:ISS is ~8405km from Wellington, Wellington City, Wellington, 6011, New Zealand

[16]:

('Miami, Miami-Dade County, Florida, USA', 4685.887400314564)