automatic_weather_stations module¶
StationCollection
¶
A collection of automatic weather stations, including SNOTEL and CCSS stations.
This class manages a collection of weather stations, allowing for data retrieval, spatial subsetting, and various data processing operations. It supports both SNOTEL and CCSS station types.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
snotel_stations |
bool |
Whether to include SNOTEL stations in the collection. Default is True. |
'True' |
ccss_stations |
bool |
Whether to include CCSS stations in the collection. Default is False. |
'False' |
Examples:
Create a StationCollection with both SNOTEL and CCSS stations:
Attributes:
| Name | Type | Description |
|---|---|---|
all_stations |
geopandas.GeoDataFrame |
A GeoDataFrame containing all the stations in the collection. |
chosen_stations |
geopandas.GeoDataFrame |
A GeoDataFrame containing the subset of stations chosen for analysis. |
Source code in easysnowdata/automatic_weather_stations.py
class StationCollection:
"""
A collection of automatic weather stations, including SNOTEL and CCSS stations.
This class manages a collection of weather stations, allowing for data retrieval,
spatial subsetting, and various data processing operations. It supports both
SNOTEL and CCSS station types.
Parameters
----------
snotel_stations : bool, optional
Whether to include SNOTEL stations in the collection. Default is True.
ccss_stations : bool, optional
Whether to include CCSS stations in the collection. Default is False.
Attributes
----------
all_stations : geopandas.GeoDataFrame
A GeoDataFrame containing all the stations in the collection.
chosen_stations : geopandas.GeoDataFrame
A GeoDataFrame containing the subset of stations chosen for analysis.
Examples
--------
Create a StationCollection with both SNOTEL and CCSS stations:
>>> station_collection = StationCollection(snotel_stations=True, ccss_stations=True)
>>> print(station_collection.all_stations)
Create a StationCollection with only SNOTEL stations and choose stations within a specific bounding box:
>>> snotel_collection = StationCollection(snotel_stations=True, ccss_stations=False)
>>> snotel_collection.choose_stations_by_bbox((-121.94, 46.72, -121.54, 46.99))
>>> print(snotel_collection.chosen_stations)
Notes
-----
Data sources:
SNOTEL: https://www.nrcs.usda.gov/wps/portal/wcc/home/quicklinks/imap
CCSS: https://cdec.water.ca.gov/snow/current/snow/
"""
def __init__(
self,
data_available: bool = True,
#snotel_stations: bool = True,
#ccss_stations: bool = True,
sortby_dist_to_geom=None,
):
self.data_available = data_available
#self.snotel_stations = snotel_stations
#self.ccss_stations = ccss_stations
self.sortby_dist_to_geom = sortby_dist_to_geom
self.all_stations = None
self.stations = None
self.TAVG = None
self.TMIN = None
self.TMAX = None
self.SNWD = None
self.WTEQ = None
self.PRCPSA = None
self.data = None
self.entire_data_archive = None
self.get_all_stations()
def get_all_stations(self):
"""
Fetches all weather stations from a GeoJSON file hosted on the snotel_ccss_stations GitHub repository.
This method retrieves station data, filters based on data availability, and optionally sorts stations
by distance to a specified geometry.
Returns
-------
None
Updates the all_stations attribute of the class.
Notes
-----
The method prints information about the retrieved stations and available data access methods.
"""
# Read the GeoJSON file
all_stations_gdf = gpd.read_file(
"https://github.com/egagli/snotel_ccss_stations/raw/main/all_stations.geojson"
).set_index("code")
# # Filter based on data availability
if self.data_available:
all_stations_gdf = all_stations_gdf[all_stations_gdf["csvData"]]
# # Filter out SNOTEL stations if not required
# if not self.snotel_stations:
# all_stations_gdf = all_stations_gdf[all_stations_gdf["network"] != "SNOTEL"]
# # Filter out CCSS stations if not required
# if not self.ccss_stations:
# all_stations_gdf = all_stations_gdf[all_stations_gdf["network"] != "CCSS"]
# If a geometry is passed in, calculate the distance to this geometry for each station
if self.sortby_dist_to_geom is not None:
print(f"Sorting by distance to given geometry. See dist_km column.")
geom_gdf = convert_bbox_to_geodataframe(self.sortby_dist_to_geom)
proj = "EPSG:32611"
all_stations_gdf["dist_km"] = (
all_stations_gdf.to_crs(proj).distance(
geom_gdf.to_crs(proj).geometry[0]
)
/ 1000
)
all_stations_gdf = all_stations_gdf.sort_values("dist_km")
self.all_stations = all_stations_gdf
print(
f"Geodataframe with all stations has been added to the Station object. Please use the .all_stations attribute to access."
)
print(
f"Use the .get_data(stations=geodataframe/string/list,variables=string/list,start_date=str,end_date=str) method to fetch data for specific stations and variables."
)
def choose_stations(self, stations_gdf):
"""
Choose specific stations for further analysis.
This method allows selection of specific stations from the collection
for subsequent data processing and analysis.
Parameters
----------
stations_gdf : gpd.GeoDataFrame, str, or list
The name(s) or index(es) of the stations to be chosen.
Returns
-------
None
Updates the stations attribute of the class.
Examples
--------
Choose stations by name:
>>> station_collection.choose_stations('Paradise')
Choose multiple stations by index:
>>> station_collection.choose_stations([0, 1, 2])
"""
if type(stations_gdf) is str:
stations_gdf = self.all_stations.loc[[stations_gdf]]
if type(stations_gdf) is list:
stations_gdf = self.all_stations.loc[stations_gdf]
self.stations = stations_gdf
def get_data(self, stations="679_WA_SNTL", variables=None, start_date='1900-01-01', end_date=today):
"""
Retrieves data for the specified stations and variables.
This method fetches data for chosen stations and variables within a specified date range.
Parameters
----------
stations : geodataframe, str, or list, optional
The stations to retrieve data for. Default is '679_WA_SNTL'.
variables : str or list, optional
The variables to retrieve data for. Default is None.
start_date : str, optional
The start date for the data. Default is '1900-01-01'.
end_date : str, optional
The end date for the data. Default is today's date.
Returns
-------
None
Updates the data attribute of the class.
Notes
-----
The behavior of this method varies based on the number of stations chosen and the specified variables.
"""
self.choose_stations(stations)
if len(self.stations) == 1:
if variables is None:
print(
f"Only one station chosen with variables=None. Default behavior fetches all variables for this station."
)
self.get_single_station_data(start_date=start_date, end_date=end_date)
else:
self.get_single_station_data(variables=variables, start_date=start_date, end_date=end_date)
else:
if variables is None:
print(
f"Multiple stations chosen with variables=None. Default behavior fetches WTEQ for all stations."
)
self.get_multiple_station_data(start_date=start_date, end_date=end_date)
else:
self.get_multiple_station_data(variables=variables, start_date=start_date, end_date=end_date)
def get_single_station_data(
self, variables=["WTEQ", "SNWD", "PRCPSA", "TAVG", "TMIN", "TMAX"], start_date='1900-01-01', end_date=today
):
"""
Retrieves data for a single weather station.
This method fetches data for specified variables from a single station within a given date range.
Parameters
----------
variables : list, optional
List of variables to include in the data. Default is ['WTEQ','SNWD','PRCPSA','TAVG','TMIN','TMAX'].
start_date : str, optional
The start date for the data. Default is '1900-01-01'.
end_date : str, optional
The end date for the data. Default is today's date.
Returns
-------
None
Updates the data attribute of the class.
Notes
-----
The method prints a message indicating that the data has been added to the Station object.
"""
single_station_df = pd.read_csv(
f"https://raw.githubusercontent.com/egagli/snotel_ccss_stations/main/data/{self.stations.index.values[0]}.csv",
index_col="datetime",
parse_dates=True,
)
columns_to_drop = [
col for col in single_station_df.columns if col not in variables
]
single_station_df = single_station_df.drop(columns=columns_to_drop).loc[start_date:end_date]
self.data = single_station_df
print(
f"Dataframe has been added to the Station object. Please use the .data attribute to access."
)
def get_multiple_station_data(self, variables="WTEQ", start_date='1900-01-01', end_date=today):
"""
Fetches data for multiple stations and specified variables.
This method retrieves data for multiple stations and specified variables within a given date range.
Parameters
----------
variables : str or list, optional
The variable(s) to fetch. Default is 'WTEQ' (water equivalent of snow on the ground).
start_date : str, optional
The start date for the data. Default is '1900-01-01'.
end_date : str, optional
The end date for the data. Default is today's date.
Returns
-------
None
Updates the data attribute of the class with an xarray Dataset.
Notes
-----
The method prints messages indicating the progress of data retrieval and processing.
"""
dataarrays = []
if isinstance(variables, str):
variables = [variables]
for variable in variables:
self.station_dict = {}
for station in tqdm.tqdm(self.stations.index):
try:
tmp = pd.read_csv(
f"https://raw.githubusercontent.com/egagli/snotel_ccss_stations/main/data/{station}.csv",
index_col="datetime",
parse_dates=True,
)[variable]
self.station_dict[station] = tmp
except:
print(f"failed to retrieve {station}")
station_data_df = pd.DataFrame.from_dict(self.station_dict).loc[start_date:end_date]
setattr(self, f"{variable}", station_data_df)
print(
f"{variable} dataframe has been added to the Station object. Please use the .{variable} attribute to access the dataframe."
)
station_data_da = (
station_data_df.to_xarray()
.to_dataarray(dim="station")
.rename(f"{variable}")
.rename({"datetime": "time"})
)
dataarrays.append(station_data_da)
all_stations_ds = xr.merge(dataarrays)
for col in self.stations.columns:
all_stations_ds = all_stations_ds.assign_coords(
{f"{col}": ("station", self.stations[f"{col}"])}
)
all_stations_ds["time"] = pd.to_datetime(all_stations_ds.time)
water_year = pd.to_datetime(all_stations_ds.time).map(datetime_to_WY)
day_of_water_year = pd.to_datetime(all_stations_ds.time).map(datetime_to_DOWY)
all_stations_ds.coords["WY"] = ("time", water_year)
all_stations_ds.coords["DOWY"] = ("time", day_of_water_year)
self.data = all_stations_ds
print(
f"Full {variables} dataset has been added to the station object. Please use the .data attribute to access the dataset."
)
def get_entire_data_archive(self, refresh: bool = True, temp_dir: str = "/tmp/") -> xr.Dataset:
"""
Downloads, decompresses and processes the entire automatic weather station data archive.
This method retrieves a compressed file containing all station data, processes it, and creates an xarray Dataset.
Parameters
----------
refresh : bool, optional
If True, the compressed data file will be redownloaded. Default is True.
temp_dir : str, optional
The directory where the compressed data file will be downloaded and decompressed. Default is '/tmp/'.
Returns
-------
xarray.Dataset
An xarray Dataset containing the processed weather station data for all stations.
Notes
-----
The method prints messages indicating the progress of data retrieval, decompression, and processing.
"""
github_tar_file_path = "https://github.com/egagli/snotel_ccss_stations/raw/main/data/all_station_data.tar.lzma"
compressed_file_path = pathlib.Path(temp_dir, "all_station_data.tar.lzma")
decompressed_dir_path = pathlib.Path(temp_dir, "data")
if not compressed_file_path.exists() or refresh:
print(
f"Downloading compressed data to a temporary directory ({compressed_file_path})..."
)
subprocess.run(
["wget", "-q", "-P", temp_dir, github_tar_file_path], check=True
)
if not decompressed_dir_path.exists() or refresh:
print(f"Decompressing data...")
subprocess.run(
["tar", "--lzma", "-xf", str(compressed_file_path), "-C", temp_dir],
check=True,
)
print(f"Creating xarray.Dataset from the uncompressed data...")
list_of_csv_files = glob.glob(str(decompressed_dir_path / "*.csv"))
datasets = []
for csv_file in list_of_csv_files:
logging.info(f"Working on {csv_file}...")
# Extract station name from the csv file name
station_name = csv_file.split("/")[-1].split(".")[0]
# Load the CSV data into a pandas DataFrame
station_df = (
pd.read_csv(csv_file, parse_dates=True)
.rename(columns={"datetime": "time"})
.set_index("time")
.sort_index()
)
# Convert the DataFrame into an xarray DataSet and add station coordinate
station_ds = station_df.to_xarray()
station_ds = station_ds.assign_coords(station=station_name)
# Add other coordinates from all_stations_gdf
for col in self.all_stations.columns:
station_ds.coords[col] = self.all_stations.loc[station_name, col]
datasets.append(station_ds)
logging.info(f"Combining all dataarrays into one dataset...")
all_stations_ds = xr.concat(datasets, dim="station", coords="all")
all_stations_ds["time"] = pd.to_datetime(all_stations_ds.time)
water_year = pd.to_datetime(all_stations_ds.time).map(datetime_to_WY)
day_of_water_year = pd.to_datetime(all_stations_ds.time).map(datetime_to_DOWY)
all_stations_ds.coords["WY"] = ("time", water_year)
all_stations_ds.coords["DOWY"] = ("time", day_of_water_year)
self.entire_data_archive = all_stations_ds
print(
f"Done! Entire archive dataset has been added to the station object. Please use the .entire_data_archive attribute to access."
)
choose_stations(self, stations_gdf)
¶
Choose specific stations for further analysis.
This method allows selection of specific stations from the collection for subsequent data processing and analysis.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
stations_gdf |
gpd.GeoDataFrame, str, or list |
The name(s) or index(es) of the stations to be chosen. |
required |
Examples:
Choose stations by name:
Returns:
| Type | Description |
|---|---|
None |
Updates the stations attribute of the class. |
Source code in easysnowdata/automatic_weather_stations.py
def choose_stations(self, stations_gdf):
"""
Choose specific stations for further analysis.
This method allows selection of specific stations from the collection
for subsequent data processing and analysis.
Parameters
----------
stations_gdf : gpd.GeoDataFrame, str, or list
The name(s) or index(es) of the stations to be chosen.
Returns
-------
None
Updates the stations attribute of the class.
Examples
--------
Choose stations by name:
>>> station_collection.choose_stations('Paradise')
Choose multiple stations by index:
>>> station_collection.choose_stations([0, 1, 2])
"""
if type(stations_gdf) is str:
stations_gdf = self.all_stations.loc[[stations_gdf]]
if type(stations_gdf) is list:
stations_gdf = self.all_stations.loc[stations_gdf]
self.stations = stations_gdf
get_all_stations(self)
¶
Fetches all weather stations from a GeoJSON file hosted on the snotel_ccss_stations GitHub repository.
This method retrieves station data, filters based on data availability, and optionally sorts stations by distance to a specified geometry.
Returns:
| Type | Description |
|---|---|
None |
Updates the all_stations attribute of the class. |
Source code in easysnowdata/automatic_weather_stations.py
def get_all_stations(self):
"""
Fetches all weather stations from a GeoJSON file hosted on the snotel_ccss_stations GitHub repository.
This method retrieves station data, filters based on data availability, and optionally sorts stations
by distance to a specified geometry.
Returns
-------
None
Updates the all_stations attribute of the class.
Notes
-----
The method prints information about the retrieved stations and available data access methods.
"""
# Read the GeoJSON file
all_stations_gdf = gpd.read_file(
"https://github.com/egagli/snotel_ccss_stations/raw/main/all_stations.geojson"
).set_index("code")
# # Filter based on data availability
if self.data_available:
all_stations_gdf = all_stations_gdf[all_stations_gdf["csvData"]]
# # Filter out SNOTEL stations if not required
# if not self.snotel_stations:
# all_stations_gdf = all_stations_gdf[all_stations_gdf["network"] != "SNOTEL"]
# # Filter out CCSS stations if not required
# if not self.ccss_stations:
# all_stations_gdf = all_stations_gdf[all_stations_gdf["network"] != "CCSS"]
# If a geometry is passed in, calculate the distance to this geometry for each station
if self.sortby_dist_to_geom is not None:
print(f"Sorting by distance to given geometry. See dist_km column.")
geom_gdf = convert_bbox_to_geodataframe(self.sortby_dist_to_geom)
proj = "EPSG:32611"
all_stations_gdf["dist_km"] = (
all_stations_gdf.to_crs(proj).distance(
geom_gdf.to_crs(proj).geometry[0]
)
/ 1000
)
all_stations_gdf = all_stations_gdf.sort_values("dist_km")
self.all_stations = all_stations_gdf
print(
f"Geodataframe with all stations has been added to the Station object. Please use the .all_stations attribute to access."
)
print(
f"Use the .get_data(stations=geodataframe/string/list,variables=string/list,start_date=str,end_date=str) method to fetch data for specific stations and variables."
)
get_data(self, stations='679_WA_SNTL', variables=None, start_date='1900-01-01', end_date='2025-01-26')
¶
Retrieves data for the specified stations and variables.
This method fetches data for chosen stations and variables within a specified date range.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
stations |
geodataframe, str, or list |
The stations to retrieve data for. Default is '679_WA_SNTL'. |
'679_WA_SNTL' |
variables |
str or list |
The variables to retrieve data for. Default is None. |
None |
start_date |
str |
The start date for the data. Default is '1900-01-01'. |
'1900-01-01' |
end_date |
str |
The end date for the data. Default is today's date. |
'2025-01-26' |
Returns:
| Type | Description |
|---|---|
None |
Updates the data attribute of the class. |
Source code in easysnowdata/automatic_weather_stations.py
def get_data(self, stations="679_WA_SNTL", variables=None, start_date='1900-01-01', end_date=today):
"""
Retrieves data for the specified stations and variables.
This method fetches data for chosen stations and variables within a specified date range.
Parameters
----------
stations : geodataframe, str, or list, optional
The stations to retrieve data for. Default is '679_WA_SNTL'.
variables : str or list, optional
The variables to retrieve data for. Default is None.
start_date : str, optional
The start date for the data. Default is '1900-01-01'.
end_date : str, optional
The end date for the data. Default is today's date.
Returns
-------
None
Updates the data attribute of the class.
Notes
-----
The behavior of this method varies based on the number of stations chosen and the specified variables.
"""
self.choose_stations(stations)
if len(self.stations) == 1:
if variables is None:
print(
f"Only one station chosen with variables=None. Default behavior fetches all variables for this station."
)
self.get_single_station_data(start_date=start_date, end_date=end_date)
else:
self.get_single_station_data(variables=variables, start_date=start_date, end_date=end_date)
else:
if variables is None:
print(
f"Multiple stations chosen with variables=None. Default behavior fetches WTEQ for all stations."
)
self.get_multiple_station_data(start_date=start_date, end_date=end_date)
else:
self.get_multiple_station_data(variables=variables, start_date=start_date, end_date=end_date)
get_entire_data_archive(self, refresh=True, temp_dir='/tmp/')
¶
Downloads, decompresses and processes the entire automatic weather station data archive.
This method retrieves a compressed file containing all station data, processes it, and creates an xarray Dataset.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
refresh |
bool |
If True, the compressed data file will be redownloaded. Default is True. |
True |
temp_dir |
str |
The directory where the compressed data file will be downloaded and decompressed. Default is '/tmp/'. |
'/tmp/' |
Returns:
| Type | Description |
|---|---|
Dataset |
An xarray Dataset containing the processed weather station data for all stations. |
Source code in easysnowdata/automatic_weather_stations.py
def get_entire_data_archive(self, refresh: bool = True, temp_dir: str = "/tmp/") -> xr.Dataset:
"""
Downloads, decompresses and processes the entire automatic weather station data archive.
This method retrieves a compressed file containing all station data, processes it, and creates an xarray Dataset.
Parameters
----------
refresh : bool, optional
If True, the compressed data file will be redownloaded. Default is True.
temp_dir : str, optional
The directory where the compressed data file will be downloaded and decompressed. Default is '/tmp/'.
Returns
-------
xarray.Dataset
An xarray Dataset containing the processed weather station data for all stations.
Notes
-----
The method prints messages indicating the progress of data retrieval, decompression, and processing.
"""
github_tar_file_path = "https://github.com/egagli/snotel_ccss_stations/raw/main/data/all_station_data.tar.lzma"
compressed_file_path = pathlib.Path(temp_dir, "all_station_data.tar.lzma")
decompressed_dir_path = pathlib.Path(temp_dir, "data")
if not compressed_file_path.exists() or refresh:
print(
f"Downloading compressed data to a temporary directory ({compressed_file_path})..."
)
subprocess.run(
["wget", "-q", "-P", temp_dir, github_tar_file_path], check=True
)
if not decompressed_dir_path.exists() or refresh:
print(f"Decompressing data...")
subprocess.run(
["tar", "--lzma", "-xf", str(compressed_file_path), "-C", temp_dir],
check=True,
)
print(f"Creating xarray.Dataset from the uncompressed data...")
list_of_csv_files = glob.glob(str(decompressed_dir_path / "*.csv"))
datasets = []
for csv_file in list_of_csv_files:
logging.info(f"Working on {csv_file}...")
# Extract station name from the csv file name
station_name = csv_file.split("/")[-1].split(".")[0]
# Load the CSV data into a pandas DataFrame
station_df = (
pd.read_csv(csv_file, parse_dates=True)
.rename(columns={"datetime": "time"})
.set_index("time")
.sort_index()
)
# Convert the DataFrame into an xarray DataSet and add station coordinate
station_ds = station_df.to_xarray()
station_ds = station_ds.assign_coords(station=station_name)
# Add other coordinates from all_stations_gdf
for col in self.all_stations.columns:
station_ds.coords[col] = self.all_stations.loc[station_name, col]
datasets.append(station_ds)
logging.info(f"Combining all dataarrays into one dataset...")
all_stations_ds = xr.concat(datasets, dim="station", coords="all")
all_stations_ds["time"] = pd.to_datetime(all_stations_ds.time)
water_year = pd.to_datetime(all_stations_ds.time).map(datetime_to_WY)
day_of_water_year = pd.to_datetime(all_stations_ds.time).map(datetime_to_DOWY)
all_stations_ds.coords["WY"] = ("time", water_year)
all_stations_ds.coords["DOWY"] = ("time", day_of_water_year)
self.entire_data_archive = all_stations_ds
print(
f"Done! Entire archive dataset has been added to the station object. Please use the .entire_data_archive attribute to access."
)
get_multiple_station_data(self, variables='WTEQ', start_date='1900-01-01', end_date='2025-01-26')
¶
Fetches data for multiple stations and specified variables.
This method retrieves data for multiple stations and specified variables within a given date range.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
variables |
str or list |
The variable(s) to fetch. Default is 'WTEQ' (water equivalent of snow on the ground). |
'WTEQ' |
start_date |
str |
The start date for the data. Default is '1900-01-01'. |
'1900-01-01' |
end_date |
str |
The end date for the data. Default is today's date. |
'2025-01-26' |
Returns:
| Type | Description |
|---|---|
None |
Updates the data attribute of the class with an xarray Dataset. |
Source code in easysnowdata/automatic_weather_stations.py
def get_multiple_station_data(self, variables="WTEQ", start_date='1900-01-01', end_date=today):
"""
Fetches data for multiple stations and specified variables.
This method retrieves data for multiple stations and specified variables within a given date range.
Parameters
----------
variables : str or list, optional
The variable(s) to fetch. Default is 'WTEQ' (water equivalent of snow on the ground).
start_date : str, optional
The start date for the data. Default is '1900-01-01'.
end_date : str, optional
The end date for the data. Default is today's date.
Returns
-------
None
Updates the data attribute of the class with an xarray Dataset.
Notes
-----
The method prints messages indicating the progress of data retrieval and processing.
"""
dataarrays = []
if isinstance(variables, str):
variables = [variables]
for variable in variables:
self.station_dict = {}
for station in tqdm.tqdm(self.stations.index):
try:
tmp = pd.read_csv(
f"https://raw.githubusercontent.com/egagli/snotel_ccss_stations/main/data/{station}.csv",
index_col="datetime",
parse_dates=True,
)[variable]
self.station_dict[station] = tmp
except:
print(f"failed to retrieve {station}")
station_data_df = pd.DataFrame.from_dict(self.station_dict).loc[start_date:end_date]
setattr(self, f"{variable}", station_data_df)
print(
f"{variable} dataframe has been added to the Station object. Please use the .{variable} attribute to access the dataframe."
)
station_data_da = (
station_data_df.to_xarray()
.to_dataarray(dim="station")
.rename(f"{variable}")
.rename({"datetime": "time"})
)
dataarrays.append(station_data_da)
all_stations_ds = xr.merge(dataarrays)
for col in self.stations.columns:
all_stations_ds = all_stations_ds.assign_coords(
{f"{col}": ("station", self.stations[f"{col}"])}
)
all_stations_ds["time"] = pd.to_datetime(all_stations_ds.time)
water_year = pd.to_datetime(all_stations_ds.time).map(datetime_to_WY)
day_of_water_year = pd.to_datetime(all_stations_ds.time).map(datetime_to_DOWY)
all_stations_ds.coords["WY"] = ("time", water_year)
all_stations_ds.coords["DOWY"] = ("time", day_of_water_year)
self.data = all_stations_ds
print(
f"Full {variables} dataset has been added to the station object. Please use the .data attribute to access the dataset."
)
get_single_station_data(self, variables=['WTEQ', 'SNWD', 'PRCPSA', 'TAVG', 'TMIN', 'TMAX'], start_date='1900-01-01', end_date='2025-01-26')
¶
Retrieves data for a single weather station.
This method fetches data for specified variables from a single station within a given date range.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
variables |
list |
List of variables to include in the data. Default is ['WTEQ','SNWD','PRCPSA','TAVG','TMIN','TMAX']. |
['WTEQ', 'SNWD', 'PRCPSA', 'TAVG', 'TMIN', 'TMAX'] |
start_date |
str |
The start date for the data. Default is '1900-01-01'. |
'1900-01-01' |
end_date |
str |
The end date for the data. Default is today's date. |
'2025-01-26' |
Returns:
| Type | Description |
|---|---|
None |
Updates the data attribute of the class. |
Source code in easysnowdata/automatic_weather_stations.py
def get_single_station_data(
self, variables=["WTEQ", "SNWD", "PRCPSA", "TAVG", "TMIN", "TMAX"], start_date='1900-01-01', end_date=today
):
"""
Retrieves data for a single weather station.
This method fetches data for specified variables from a single station within a given date range.
Parameters
----------
variables : list, optional
List of variables to include in the data. Default is ['WTEQ','SNWD','PRCPSA','TAVG','TMIN','TMAX'].
start_date : str, optional
The start date for the data. Default is '1900-01-01'.
end_date : str, optional
The end date for the data. Default is today's date.
Returns
-------
None
Updates the data attribute of the class.
Notes
-----
The method prints a message indicating that the data has been added to the Station object.
"""
single_station_df = pd.read_csv(
f"https://raw.githubusercontent.com/egagli/snotel_ccss_stations/main/data/{self.stations.index.values[0]}.csv",
index_col="datetime",
parse_dates=True,
)
columns_to_drop = [
col for col in single_station_df.columns if col not in variables
]
single_station_df = single_station_df.drop(columns=columns_to_drop).loc[start_date:end_date]
self.data = single_station_df
print(
f"Dataframe has been added to the Station object. Please use the .data attribute to access."
)