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Use library classes instead of namedtuple in ipma tests (#115372)

pull/115404/head^2
Sid 2024-04-12 00:03:10 +02:00 committed by GitHub
parent 137514edb7
commit a093f943d7
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3 changed files with 83 additions and 131 deletions
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149
tests/components/ipma/__init__.py
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@ -1,8 +1,12 @@
"""Tests for the IPMA component."""
from collections import namedtuple
from datetime import UTC, datetime
from pyipma.forecast import Forecast, Forecast_Location, Weather_Type
from pyipma.observation import Observation
from pyipma.rcm import RCM
from pyipma.uv import UV
from homeassistant.const import CONF_LATITUDE, CONF_LONGITUDE, CONF_MODE, CONF_NAME
ENTRY_CONFIG = {
@ -18,109 +22,90 @@ class MockLocation:
async def fire_risk(self, api):
"""Mock Fire Risk."""
RCM = namedtuple(
"RCM",
[
"dico",
"rcm",
"coordinates",
],
)
return RCM("some place", 3, (0, 0))
async def uv_risk(self, api):
"""Mock UV Index."""
UV = namedtuple(
"UV",
["idPeriodo", "intervaloHora", "data", "globalIdLocal", "iUv"],
)
return UV(0, "0", datetime.now(), 0, 5.7)
return UV(0, "0", datetime(2020, 1, 16, 0, 0, 0), 0, 5.7)
async def observation(self, api):
"""Mock Observation."""
Observation = namedtuple(
"Observation",
[
"accumulated_precipitation",
"humidity",
"pressure",
"radiation",
"temperature",
"wind_direction",
"wind_intensity_km",
],
return Observation(
precAcumulada=0.0,
humidade=71.0,
pressao=1000.0,
radiacao=0.0,
temperatura=18.0,
idDireccVento=8,
intensidadeVentoKM=3.94,
intensidadeVento=1.0944,
timestamp=datetime(2020, 1, 16, 0, 0, 0),
idEstacao=0,
)
return Observation(0.0, 71.0, 1000.0, 0.0, 18.0, "NW", 3.94)
async def forecast(self, api, period):
"""Mock Forecast."""
Forecast = namedtuple(
"Forecast",
[
"feels_like_temperature",
"forecast_date",
"forecasted_hours",
"humidity",
"max_temperature",
"min_temperature",
"precipitation_probability",
"temperature",
"update_date",
"weather_type",
"wind_direction",
"wind_strength",
],
)
WeatherType = namedtuple("WeatherType", ["id", "en", "pt"])
if period == 24:
return [
Forecast(
None,
datetime(2020, 1, 16, 0, 0, 0),
24,
None,
16.2,
10.6,
"100.0",
13.4,
"2020-01-15T07:51:00",
WeatherType(9, "Rain/showers", "Chuva/aguaceiros"),
"S",
"10",
utci=None,
dataPrev=datetime(2020, 1, 16, 0, 0, 0),
idPeriodo=24,
hR=None,
tMax=16.2,
tMin=10.6,
probabilidadePrecipita=100.0,
tMed=13.4,
dataUpdate=datetime(2020, 1, 15, 7, 51, 0),
idTipoTempo=Weather_Type(9, "Rain/showers", "Chuva/aguaceiros"),
ddVento="S",
ffVento=10,
idFfxVento=0,
iUv=0,
intervaloHora="",
location=Forecast_Location(0, "", 0, 0, 0, "", (0, 0)),
),
]
if period == 1:
return [
Forecast(
"7.7",
datetime(2020, 1, 15, 1, 0, 0, tzinfo=UTC),
1,
"86.9",
12.0,
None,
80.0,
10.6,
"2020-01-15T02:51:00",
WeatherType(10, "Light rain", "Chuva fraca ou chuvisco"),
"S",
"32.7",
utci=7.7,
dataPrev=datetime(2020, 1, 15, 1, 0, 0, tzinfo=UTC),
idPeriodo=1,
hR=86.9,
tMax=12.0,
tMin=None,
probabilidadePrecipita=80.0,
tMed=10.6,
dataUpdate=datetime(2020, 1, 15, 2, 51, 0),
idTipoTempo=Weather_Type(
10, "Light rain", "Chuva fraca ou chuvisco"
),
ddVento="S",
ffVento=32.7,
idFfxVento=0,
iUv=0,
intervaloHora="",
location=Forecast_Location(0, "", 0, 0, 0, "", (0, 0)),
),
Forecast(
"5.7",
datetime(2020, 1, 15, 2, 0, 0, tzinfo=UTC),
1,
"86.9",
12.0,
None,
80.0,
10.6,
"2020-01-15T02:51:00",
WeatherType(1, "Clear sky", "C\u00e9u limpo"),
"S",
"32.7",
utci=5.7,
dataPrev=datetime(2020, 1, 15, 2, 0, 0, tzinfo=UTC),
idPeriodo=1,
hR=86.9,
tMax=12.0,
tMin=None,
probabilidadePrecipita=80.0,
tMed=10.6,
dataUpdate=datetime(2020, 1, 15, 2, 51, 0),
idTipoTempo=Weather_Type(1, "Clear sky", "C\u00e9u limpo"),
ddVento="S",
ffVento=32.7,
idFfxVento=0,
iUv=0,
intervaloHora="",
location=Forecast_Location(0, "", 0, 0, 0, "", (0, 0)),
),
]

57
tests/components/ipma/snapshots/test_diagnostics.ambr
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@ -1,15 +1,10 @@
# serializer version: 1
# name: test_diagnostics
dict({
'current_weather': list([
0.0,
71.0,
1000.0,
0.0,
18.0,
'NW',
3.94,
]),
'current_weather': dict({
'__type': "<class 'pyipma.observation.Observation'>",
'repr': 'Observation(intensidadeVentoKM=3.94, temperatura=18.0, radiacao=0.0, idDireccVento=8, precAcumulada=0.0, intensidadeVento=1.0944, humidade=71.0, pressao=1000.0, timestamp=datetime.datetime(2020, 1, 16, 0, 0), idEstacao=0)',
}),
'location_information': dict({
'global_id_local': 1130600,
'id_station': 1200545,
@ -19,42 +14,14 @@
'station': 'HomeTown Station',
}),
'weather_forecast': list([
list([
'7.7',
'2020-01-15T01:00:00+00:00',
1,
'86.9',
12.0,
None,
80.0,
10.6,
'2020-01-15T02:51:00',
list([
10,
'Light rain',
'Chuva fraca ou chuvisco',
]),
'S',
'32.7',
]),
list([
'5.7',
'2020-01-15T02:00:00+00:00',
1,
'86.9',
12.0,
None,
80.0,
10.6,
'2020-01-15T02:51:00',
list([
1,
'Clear sky',
'Céu limpo',
]),
'S',
'32.7',
]),
dict({
'__type': "<class 'pyipma.forecast.Forecast'>",
'repr': "Forecast(tMed=10.6, tMin=None, ffVento=32.7, idFfxVento=0, dataUpdate=datetime.datetime(2020, 1, 15, 2, 51), tMax=12.0, iUv=0, intervaloHora='', idTipoTempo=Weather_Type(id=10, en='Light rain', pt='Chuva fraca ou chuvisco'), hR=86.9, location=Forecast_Location(globalIdLocal=0, local='', idRegiao=0, idDistrito=0, idConcelho=0, idAreaAviso='', coordinates=(0, 0)), probabilidadePrecipita=80.0, idPeriodo=1, dataPrev=datetime.datetime(2020, 1, 15, 1, 0, tzinfo=datetime.timezone.utc), ddVento='S', utci=7.7)",
}),
dict({
'__type': "<class 'pyipma.forecast.Forecast'>",
'repr': "Forecast(tMed=10.6, tMin=None, ffVento=32.7, idFfxVento=0, dataUpdate=datetime.datetime(2020, 1, 15, 2, 51), tMax=12.0, iUv=0, intervaloHora='', idTipoTempo=Weather_Type(id=1, en='Clear sky', pt='Céu limpo'), hR=86.9, location=Forecast_Location(globalIdLocal=0, local='', idRegiao=0, idDistrito=0, idConcelho=0, idAreaAviso='', coordinates=(0, 0)), probabilidadePrecipita=80.0, idPeriodo=1, dataPrev=datetime.datetime(2020, 1, 15, 2, 0, tzinfo=datetime.timezone.utc), ddVento='S', utci=5.7)",
}),
]),
})
# ---

8
tests/components/ipma/snapshots/test_weather.ambr
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@ -83,7 +83,7 @@
dict({
'condition': 'rainy',
'datetime': datetime.datetime(2020, 1, 16, 0, 0),
'precipitation_probability': '100.0',
'precipitation_probability': 100.0,
'temperature': 16.2,
'templow': 10.6,
'wind_bearing': 'S',
@ -121,7 +121,7 @@
dict({
'condition': 'rainy',
'datetime': datetime.datetime(2020, 1, 16, 0, 0),
'precipitation_probability': '100.0',
'precipitation_probability': 100.0,
'temperature': 16.2,
'templow': 10.6,
'wind_bearing': 'S',
@ -160,7 +160,7 @@
dict({
'condition': 'rainy',
'datetime': '2020-01-16T00:00:00',
'precipitation_probability': '100.0',
'precipitation_probability': 100.0,
'temperature': 16.2,
'templow': 10.6,
'wind_bearing': 'S',
@ -173,7 +173,7 @@
dict({
'condition': 'rainy',
'datetime': '2020-01-16T00:00:00',
'precipitation_probability': '100.0',
'precipitation_probability': 100.0,
'temperature': 16.2,
'templow': 10.6,
'wind_bearing': 'S',