sw7 detect laps from coordinates #9
11
src/exercises/type_hints.py
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11
src/exercises/type_hints.py
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def load_scores(data: list[tuple[str, int | str]]) -> dict[str, int]:
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result: dict[str, int] = {}
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for name, points in data:
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result[name] = int(points)
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return result
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if __name__ == "__main__":
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print(load_scores([("Housi", 40), ("Theodolf", 45), ("Mehmet", 15)]))
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35
src/lap_detection/api.py
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35
src/lap_detection/api.py
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from dataclasses import field, dataclass
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from typing import List
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@dataclass
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class Coordinate:
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lon: float
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lat: float
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@dataclass
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class Geometry:
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coordinates: list[list[float]]
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type: str
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@dataclass
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class Feature:
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type: str
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properties: dict
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geometry: Geometry
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@dataclass
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class Collection:
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type: str
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features: list[Feature]
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@dataclass
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class Lap:
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start_index: int
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end_index: int
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distance_m: float
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coords: List[Coordinate] = field(default_factory=list)
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BIN
src/lap_detection/data/usecase1-luetzelsee.png
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BIN
src/lap_detection/data/usecase1-luetzelsee.png
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Binary file not shown.
|
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254
src/lap_detection/data/usecase1.json
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254
src/lap_detection/data/usecase1.json
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BIN
src/lap_detection/data/usecase2-la_stadion.png
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BIN
src/lap_detection/data/usecase2-la_stadion.png
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Binary file not shown.
|
After Width: | Height: | Size: 146 KiB |
230
src/lap_detection/data/usecase2.json
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src/lap_detection/data/usecase2.json
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@ -0,0 +1,230 @@
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|
||||
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|
||||
146
src/lap_detection/data/usecase3.json
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src/lap_detection/data/usecase3.json
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@ -0,0 +1,146 @@
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9.838105549447391,
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||||
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|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
BIN
src/lap_detection/data/usecase3_davos_gelaende.png
Normal file
BIN
src/lap_detection/data/usecase3_davos_gelaende.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 265 KiB |
BIN
src/lap_detection/data/usecase4-aarau_stadtkurs.png
Normal file
BIN
src/lap_detection/data/usecase4-aarau_stadtkurs.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 456 KiB |
150
src/lap_detection/data/usecase4.json
Normal file
150
src/lap_detection/data/usecase4.json
Normal file
@ -0,0 +1,150 @@
|
||||
{
|
||||
"type": "FeatureCollection",
|
||||
"features": [
|
||||
{
|
||||
"type": "Feature",
|
||||
"properties": {},
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"geometry": {
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||||
"coordinates": [
|
||||
[
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||||
8.04235788233035,
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[
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[
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||||
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|
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[
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|
||||
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|
||||
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|
||||
[
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|
||||
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|
||||
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|
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[
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||||
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|
||||
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|
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[
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||||
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||||
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||||
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[
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||||
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|
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[
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||||
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[
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||||
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||||
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|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
53
src/lap_detection/lap_detector.py
Normal file
53
src/lap_detection/lap_detector.py
Normal file
@ -0,0 +1,53 @@
|
||||
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
131
src/lap_detection/main.py
Normal file
@ -0,0 +1,131 @@
|
||||
# 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")
|
||||
57
tests/lap_detection/test_main.py
Normal file
57
tests/lap_detection/test_main.py
Normal file
@ -0,0 +1,57 @@
|
||||
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
|
||||
54
tests/test_lap_detector.py
Normal file
54
tests/test_lap_detector.py
Normal file
@ -0,0 +1,54 @@
|
||||
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
|
||||
Loading…
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Reference in New Issue
Block a user