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175 | class SparkWeatherCompanyForecastAPIV1Source(SparkWeatherCompanyBaseWeatherSource):
"""
The Weather Forecast API V1 Source is used to read 15 days forecast from the Weather API.
URL: <a href="https://api.weather.com/v1/geocode/32.3667/-95.4/forecast/hourly/360hour.json">
https://api.weather.com/v1/geocode/32.3667/-95.4/forecast/hourly/360hour.json</a>
Parameters:
spark (SparkSession): Spark Session instance
options (dict): A dictionary of ISO Source specific configurations (See Attributes table below).
Attributes:
lat (str): Latitude of the Weather Station.
lon (str): Longitude of the Weather Station.
api_key (str): Weather API key.
language (str): API response language. Defaults to `en-US`.
units (str): Unit of measurements. Defaults to `e`.
"""
spark: SparkSession
spark_schema = WEATHER_FORECAST_SCHEMA
options: dict
weather_url: str = "https://api.weather.com/v1/geocode/"
required_options = ["lat", "lon", "api_key"]
def __init__(self, spark: SparkSession, options: dict) -> None:
super(SparkWeatherCompanyForecastAPIV1Source, self).__init__(spark, options)
self.spark = spark
self.options = options
self.lat = self.options.get("lat", "").strip()
self.lon = self.options.get("lon", "").strip()
self.api_key = self.options.get("api_key", "").strip()
self.language = self.options.get("language", "en-US").strip()
self.units = self.options.get("units", "e").strip()
def _prepare_data(self, df: pd.DataFrame) -> pd.DataFrame:
"""
Prepares weather data for the use.
Args:
df: Data received after preparation.
Returns:
Final data after all the transformations.
"""
rename_cols = {
"latitude": "Latitude",
"longitude": "Longitude",
"class": "Class",
"expire_time_gmt": "ExpireTimeGmt",
"fcst_valid": "FcstValid",
"fcst_valid_local": "FcstValidLocal",
"num": "Num",
"day_ind": "DayInd",
"temp": "Temp",
"dewpt": "Dewpt",
"hi": "Hi",
"wc": "Wc",
"feels_like": "FeelsLike",
"icon_extd": "IconExtd",
"wxman": "Wxman",
"icon_code": "IconCode",
"dow": "Dow",
"phrase_12char": "Phrase12Char",
"phrase_22char": "Phrase22Char",
"phrase_32char": "Phrase32Char",
"subphrase_pt1": "SubphrasePt1",
"subphrase_pt2": "SubphrasePt2",
"subphrase_pt3": "SubphrasePt3",
"pop": "Pop",
"precip_type": "PrecipType",
"qpf": "Qpf",
"snow_qpf": "SnowQpf",
"rh": "Rh",
"wspd": "Wspd",
"wdir": "Wdir",
"wdir_cardinal": "WdirCardinal",
"gust": "Gust",
"clds": "Clds",
"vis": "Vis",
"mslp": "Mslp",
"uv_index_raw": "UvIndexRaw",
"uv_index": "UvIndex",
"uv_warning": "UvWarning",
"uv_desc": "UvDesc",
"golf_index": "GolfIndex",
"golf_category": "GolfCategory",
"severity": "Severity",
}
df = df.rename(columns=rename_cols)
fields = self.spark_schema.fields
str_cols = list(
map(
lambda x: x.name,
filter(lambda x: isinstance(x.dataType, StringType), fields),
)
)
double_cols = list(
map(
lambda x: x.name,
filter(lambda x: isinstance(x.dataType, DoubleType), fields),
)
)
int_cols = list(
map(
lambda x: x.name,
filter(lambda x: isinstance(x.dataType, IntegerType), fields),
)
)
df[str_cols] = df[str_cols].astype(str)
df[double_cols] = df[double_cols].astype(float)
df[int_cols] = df[int_cols].astype(int)
df.reset_index(inplace=True, drop=True)
return df
def _get_api_params(self):
params = {
"language": self.language,
"units": self.units,
"apiKey": self.api_key,
}
return params
def _pull_for_weather_station(self, lat: str, lon: str) -> pd.DataFrame:
response = json.loads(
self._fetch_from_url(f"{lat}/{lon}/forecast/hourly/360hour.json").decode(
"utf-8"
)
)
return pd.DataFrame(response["forecasts"])
def _pull_data(self) -> pd.DataFrame:
"""
Pulls data from the Weather API and parses the JSON file.
Returns:
Raw form of data.
"""
df = self._pull_for_weather_station(self.lat, self.lon)
df["latitude"] = self.lat
df["longitude"] = self.lon
return df
|