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Author SHA1 Message Date
Michael Schären
eb6a242257 Merge pull request 'sw7 detect laps from coordinates' (#9) from sw7_lap_detection into master 
Reviewed-on: #9
 2026-05-08 15:45:24 +02:00
Michael Schären
f03c720435 Merge remote-tracking branch 'origin/master' into sw7_lap_detection 2026-04-11 23:10:28 +02:00
schaermicha1
45921bfe17 sw7 detect laps from coordinates 2026-04-11 15:51:34 +02:00
14 changed files with 1121 additions and 0 deletions
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11
src/exercises/type_hints.py Normal file
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def load_scores(data: list[tuple[str, int | str]]) -> dict[str, int]:
result: dict[str, int] = {}
for name, points in data:
result[name] = int(points)
return result
if __name__ == "__main__":
print(load_scores([("Housi", 40), ("Theodolf", 45), ("Mehmet", 15)]))

35
src/lap_detection/api.py Normal file
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from dataclasses import field, dataclass
from typing import List
@dataclass
class Coordinate:
lon: float
lat: float
@dataclass
class Geometry:
coordinates: list[list[float]]
type: str
@dataclass
class Feature:
type: str
properties: dict
geometry: Geometry
@dataclass
class Collection:
type: str
features: list[Feature]
@dataclass
class Lap:
start_index: int
end_index: int
distance_m: float
coords: List[Coordinate] = field(default_factory=list)

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src/lap_detection/data/usecase1.json Normal file
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146
src/lap_detection/data/usecase3.json Normal file
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src/lap_detection/data/usecase4.json Normal file
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53
src/lap_detection/lap_detector.py Normal file
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from haversine import haversine, Unit
from lap_detection.api import Coordinate
def dist(a: Coordinate, b: Coordinate):
return haversine((a.lat, a.lon), (b.lat, b.lon), Unit.METERS)
def count_laps(coords: list[Coordinate], start_coord: Coordinate, radius: int) -> int:
"""Count the number of laps completed
by tracking the number of times that the starting coordinate was passed by the given coordinate list
Args:
coords (list[Coordinate]): the list of coordinates
start_coord (Coordinate): the coordinate to compare to
radius (int): the radius around the starting coordinate
Returns:
int: the number of laps completed
"""
if not coords:
return 0
laps = 0
distance_since_last = 0
# Do we start inside or outside the start/finish area?
d_to_first = dist(coords[0], start_coord)
inside = d_to_first < radius
for i in range(1, len(coords)):
prev = coords[i - 1]
curr = coords[i]
# Don't compare the starting coordinate with itself
if curr == start_coord:
continue
d = dist(prev, curr)
distance_since_last += d
# Did we enter the start/finish area?
d_to_start = dist(curr, start_coord)
now_inside = d_to_start < radius
if not inside and now_inside:
# Ignore possible gps stutters with minimum lap distance threshold
if distance_since_last > 100:
laps += 1
distance_since_last = 0
inside = now_inside
return laps

131
src/lap_detection/main.py Normal file
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# TASK: Rundenzählung Stoppuhr
# Hintergrund: Sportuhren zeichnen oft mittels GPS alle x Sekunden die Koordination (lat/lon) des jeweiligen Standorts auf.
# Ein automatischer Modus detektiert, wenn der User eine Runde (Lap) gelaufen/gefahren ist und nimmt
# automatisch die Zwischenzeit.
#
# Aufgabe: Unsere Aufgabe ist es nun diese Rundendetektion selbst in Python zu schreiben. Der Programmierer bekommt
# eine .json-File mit Koordinaten. Diese sind immer als Liste [lon, lat], also z.B. [[8.5417, 47.3769],
# [8.5434, 47.3772], [8.5446, 47.3783], ...]. Unser Programm soll als Antwort einen Integer mit der
# ermittelten Anzahl Runden zurückgeben, also z.B. 2.
#
# - Die Aufgabe ist bewusst so offen gestellt, damit eure Kreativität nicht eingeschränkt ist. Ihr könnt den
# Schwierigkeitsgrad selbst steuern, wie stark ihr ins Detail wollt, was ihr berücksichtigen wollt und was
# nicht etc. Es geht damit auch nicht so sehr um die technischen Details, sondern darum, denn Code mithilfe
# von Hints und Docstrings möglichst aussagekräftig und selbsterklärend zu machen. Nutzt auch 2-3 Tests mit
# Pytest, um eueren Code 'sicherer' zu machen und zu validieren.
#
# Denkanstösse: - Was macht eine Runde aus?
# - Wie entscheide ich, was eine Runde ist (geometrisch, Randbedingungen) -> 'Kochrezept' machen
# - Wo schränke ich mich ein, wo mache ich Annahmen, ...
#
# Ziel: - Ein anderer User/Programmierer kann eure Funktionen und die darin hints/docstrings/tests lesen und
# versteht was in eurem Code passiert, ohne den eigentlichen Code anschauen zu müssen.
# - Es muss keine Perfektion angestrebt werden, es geht um die praktische Umsetzung der Theorie in einer
# Praxisanwendung! Macht Annahmen/Vereinfachungen!
# - Diejenigen, welche das Beispiel abgeben wollen, machen am besten einen
# - neuen Branch (Aufgabe Rundenzählung)
# - main-Funktion mit der Hauptfunktion, allfällige Helferfunktionen in einem separaten Modul
# - test-Ordner mit 2-3 Pytests
# - ein kurzer Readme mit Erklärung der Funktionsweise eures Rundenzählers und allfällige
# Einschränkungen/Annahmen
# - alle Funktionen mit sauberen Typehints und docstrings (Validation falls nötig)
# -> commit und PR
#
# Hilfsmittel: - für benötigte geometrische Berechnungen dürfen auch KI-Hilfsmittel (Prompt Engineering) genutzt werden!
# - Bitte nützt aber die KI nicht, um das ganze Programm zu erstellen, dokumentieren und testen :-)
#
# Hints: - für eine allfällige Distanz zwischen 2 Punkten -> haversine
#
# Usecases: - Im Ordner 'data' befinden sich 4 usecases mit Koordinaten.
# - Lösungen für die usecases:
# - usecase1: 3
# - usecase2: 3 (3 3/4)
# - usecase3: 0
# - usecase4: 2
# - Schreibt aber auch noch 1-2 eigene Testcases für allfällige wichtige Hilfsfunktionen.
from pathlib import Path
import json
from dacite import from_dict
from lap_detection.api import Collection, Coordinate
from lap_detection.lap_detector import count_laps, dist
def _check_path(path: str) -> Path:
"""Checks the existence of the provided path
Args:
path (str): The path
Returns:
a Path object
Raises:
ValueError: If the path is not present
"""
path = Path(path)
if not path.exists():
raise FileNotFoundError(f"{path} does not exist")
return path
def _read_file(path: Path) -> Collection:
"""Reads the provided file
Args:
path (str): The path of the file
Returns:
an object of type Collection
"""
with path.open("r", encoding="utf-8") as f:
loaded_file = json.load(f)
collection = from_dict(data_class=Collection, data=loaded_file)
return collection
def _build_coordinates(collection: Collection) -> list[Coordinate]:
"""Build a list of Coordinate objects from a Collection
Args:
collection (Collection): the feature collection
Returns:
a list of Coordinate objects
"""
return [
Coordinate(coordinate[0], coordinate[1])
for coordinate in collection.features[0].geometry.coordinates
]
def get_coordinates(path: str) -> list[Coordinate]:
"""Validate the path, read the file and extract the coordinates
Args:
path (str): The path of the file
Returns:
a list of Coordinate objects
"""
validated_path = _check_path(path)
data = _read_file(validated_path)
coordinates = _build_coordinates(data)
return coordinates
def main(path: str) -> None:
coords = get_coordinates(path=path)
# detect laps -> business logic (build functions)
# Set the radius of the start/finish area based of the minimum distance of two following points
radius = min([dist(coords[i - 1], coords[i]) for i in range(len(coords))])
# Count laps from each coordinate
# The maximum indicates that the starting point is in a high traffic area
n_rounds = max([count_laps(coords, start, radius) for start in coords])
print(f"Anzahl Runden: {n_rounds}")
if __name__ == "__main__":
main("data/usecase1.json")
main("data/usecase2.json")
main("data/usecase3.json")
main("data/usecase4.json")

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tests/lap_detection/test_main.py Normal file
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import pytest
from lap_detection.api import Collection, Feature, Geometry, Coordinate
import lap_detection.main as main
# arrange
@pytest.fixture
def sample_collection():
return Collection(
"FeatureCollection",
[
Feature(
"Feature",
{},
Geometry([[123.456, 654.321], [987.654, 101.010]], "LineString"),
)
],
)
# arrange
@pytest.mark.parametrize(
"path,expected_laps",
[
("src/lap_detection/data/usecase1.json", 3),
("src/lap_detection/data/usecase2.json", 3),
("src/lap_detection/data/usecase3.json", 0),
("src/lap_detection/data/usecase4.json", 2),
],
)
def test_main(path, expected_laps, capsys):
# act
main.main(path)
# assert
captured = capsys.readouterr()
assert captured.out.strip() == f"Anzahl Runden: {expected_laps}"
def test_check_invalid_path():
# assert
with pytest.raises(FileNotFoundError, match="invalid_path.json does not exist"):
# act
main._check_path("invalid_path.json")
def test_build_coordinates(sample_collection):
# act
coordinates = main._build_coordinates(sample_collection)
# assert
assert isinstance(coordinates, list)
assert len(coordinates) == 2
assert isinstance(coordinates[1], Coordinate)
assert coordinates[1].lon == 987.654
assert coordinates[1].lat == 101.010

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from lap_detection.api import Coordinate
from lap_detection.lap_detector import count_laps
def test_no_laps():
# arrange
start = Coordinate(lat=47.0, lon=8.0)
coords = [
Coordinate(lat=47.0001, lon=8.0001),
Coordinate(lat=47.0002, lon=8.0002),
Coordinate(lat=47.0003, lon=8.0003),
]
# act
result = count_laps(coords, start, radius=20)
# assert
assert result == 0
def test_single_lap():
# arrange
start = Coordinate(lat=47.0, lon=8.0)
coords = [
Coordinate(lat=47.0030, lon=8.0000), # far from start
Coordinate(lat=47.0020, lon=8.0010),
Coordinate(lat=47.0010, lon=8.0020),
Coordinate(lat=47.0001, lon=8.0001), # back near start -> lap counted
Coordinate(lat=47.0030, lon=8.0000), # far again -> no second lap
]
# act
result = count_laps(coords, start, radius=20)
# assert
assert result == 1
def test_two_laps():
# arrange
start = Coordinate(lat=47.0, lon=8.0)
coords = [
Coordinate(lat=47.0030, lon=8.0000), # far from start
Coordinate(lat=47.0001, lon=8.0001), # back near start -> lap 1
Coordinate(lat=47.0002, lon=8.0002), # still near start -> don't count twice
Coordinate(lat=47.0030, lon=8.0000), # far again
Coordinate(lat=47.0001, lon=8.0001), # back near start -> lap 2
]
# act
result = count_laps(coords, start, radius=20)
# assert
assert result == 2