metloom.pointdata package

Submodules

metloom.pointdata.base module

exception metloom.pointdata.base.DataValidationError[source]

Bases: RuntimeError

class metloom.pointdata.base.GenericPoint(station_id, name, metadata=None)[source]

Bases: object

Class for storing metadata. and defining the expected data format returned from get_data methods

ALLOWED_VARIABLES

alias of VariableBase

DATASOURCE = None
EXPECTED_COLUMNS = ['geometry', 'datasource']
EXPECTED_INDICES = ['datetime', 'site']
ITERATOR_CLASS

alias of PointDataCollection

NON_VARIABLE_COLUMNS = ['datetime', 'site', 'geometry', 'datasource']
property metadata

metadata property Returns:

shapely.point.Point object in Longitude, Latitude with z in ft

property tzinfo

tzinfo that pandas can use for tz_localize

classmethod validate_sensor_df(gdf: GeoDataFrame)[source]

Validate that the GeoDataFrame returned is formatted correctly. The goal of this method is to ensure base classes are returning a consistent format of dataframe

class metloom.pointdata.base.PointData(station_id, name, metadata=None)[source]

Bases: GenericPoint

Extend GenericPoint and add functions for finding data from geometry and for gettings daily, hourly, or snow course data

POINTS_FROM_GEOM_DEFAULTS = {'buffer': 0.0, 'filter_to_active': False, 'snow_courses': False, 'within_geometry': True}
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get daily measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get hourly measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_snow_course_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get snow course data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

classmethod points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], snow_courses=False, within_geometry=True, buffer=0.0)[source]

Find a collection of points with measurements for certain variables contained within a shapefile. Any point in the shapefile with measurements for any of the variables should be included Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription snow_courses: boolean for including only snowcourse data or no

snowcourse data

within_geometry: filter the points to within the shapefile

instead of just the extents. Default True

buffer: buffer added to search box

Returns:

PointDataCollection

classmethod validate_sensor_df(gdf: GeoDataFrame)[source]

Validate that the GeoDataFrame returned is formatted correctly. The goal of this method is to ensure base classes are returning a consistent format of dataframe

class metloom.pointdata.base.PointDataCollection(points: List[object] | None = None)[source]

Bases: object

Iterator class for a collection of PointData objects. This allows conversion to a GeoDataFrame

add_point(point)[source]

Append point to collection of PointData objects

Args:

point: PointData object

to_dataframe()[source]
Returns:

GeoDataFrame of points. Columns are [‘name’, ‘id’, ‘geometry’]

metloom.pointdata.cdec module

class metloom.pointdata.cdec.CDECPointData(station_id, name, metadata=None)[source]

Bases: PointData

Implement PointData methods for CDEC data source API documentation here https://cdec.water.ca.gov/dynamicapp/

ALLOWED_VARIABLES

alias of CdecStationVariables

CDEC_URL = 'http://cdec.water.ca.gov/dynamicapp/req/JSONDataServlet'
DATASOURCE = 'CDEC'
META_URL = 'https://cdec.water.ca.gov/dynamicapp/req/CSVMetaDataServlet'
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_daily_data Example query: https://cdec.water.ca.gov/dynamicapp/req/JSONDataServlet? Stations=TNY&SensorNums=3&dur_code=D&Start=2021-05-16&End=2021-05-16

get_event_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]
get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_hourly_data

get_snow_course_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_snow_course_data

is_only_monthly()[source]

determine if all sensors for a station are on a monthly interval

is_only_snow_course(variables: List[SensorDescription])[source]

Determine if a station only has snow course measurements

is_partly_snow_course()[source]

Determine if any of the snow sensors at a station are on a monthly interval Assumption: Monthly snow sensor measurements are snow courses

classmethod points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], **kwargs)[source]

See docstring for PointData.points_from_geometry

Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription snow_courses: Boolean for including only snowcourse data or no snowcourse data within_geometry: filter the points to within the shapefile instead of just the extents. Default True buffer: buffer added to search box

Returns:

PointDataCollection

metloom.pointdata.csas module

Data reader for the Center for Snow and Avalanche Studies

class metloom.pointdata.csas.CSASMet(station_id, name=None, metadata=None, cache='./cache')[source]

Bases: CSVPointData

ALLOWED_STATIONS

alias of CSASStationInfo

ALLOWED_VARIABLES

alias of CSASVariables

CURRENT_AVAILABLE_YEAR = 2024
DATASOURCE = 'CSAS'
DOI = ''
URL = 'https://snowstudies.org/wp-content/uploads/'
UTC_OFFSET_HOURS = -7
class metloom.pointdata.csas.CSASStationInfo(value)[source]

Bases: StationInfo

An enumeration.

PUTNEY = ('Putney Study Plot', 'PTSP', 37.89233, -107.69577, 12323, '2025/05/PTSP_1hr.csv')
SENATOR_BECK = ('Senator Beck Study Plot', 'SBSP', 37.90688, -107.72627, 12186, '2023/11/SBSP_1hr_2003-2009.csv')
SENATOR_BECK_STREAM_GAUGE = ('Senator Beck Stream Gauge', 'SBSG', 37.90678, -107.70943, 11030, '2025/05/SBSG_1hr.csv')
SWAMP_ANGEL = ('Swamp Angel Study Plot', 'SASP', 37.90691, -107.71132, 11060, '2023/11/SASP_1hr_2003-2009.csv')
exception metloom.pointdata.csas.InvalidDateRange[source]

Bases: Exception

Exception to indicate there is no know data for the available date range

metloom.pointdata.cues module

A reader for the Mammoth CUES site https://snow.ucsb.edu/index.php/description/

class metloom.pointdata.cues.CuesLevel1(station_id, name, metadata=None)[source]

Bases: PointData

Implement PointData methods for CUES level 1 data https://snow.ucsb.edu/index.php/description/ https://snow.ucsb.edu/index.php/query-db/ https://doi.org/10.21424/R4159Q

ALLOWED_VARIABLES

alias of CuesLevel1Variables

DATASOURCE = 'UCSB CUES'
URL = 'https://snow.ucsb.edu/index.php/query-db/'
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get daily measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get hourly measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_snow_course_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get snow course data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

property metadata

Hardcode the metadata

points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], snow_courses=False, within_geometry=True, buffer=0.0)[source]

Find a collection of points with measurements for certain variables contained within a shapefile. Any point in the shapefile with measurements for any of the variables should be included Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription snow_courses: boolean for including only snowcourse data or no

snowcourse data

within_geometry: filter the points to within the shapefile

instead of just the extents. Default True

buffer: buffer added to search box

Returns:

PointDataCollection

metloom.pointdata.files module

Location to keep readers that pull in a flat file like a csv.

class metloom.pointdata.files.CSVPointData(station_id, name=None, metadata=None, cache='./cache')[source]

Bases: PointData

Some met station data is stored off in flat csv files. This class enables the management of downloading those files while still allowing a similar interface

ALLOWED_STATIONS

alias of StationInfo

UTC_OFFSET_HOURS = 0
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get daily measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get hourly measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

classmethod points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], within_geometry=True, buffer=0.0, **kwargs)[source]

Find a collection of points with measurements for certain variables contained within a shapefile. Any point in the shapefile with measurements for any of the variables should be included Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription snow_courses: boolean for including only snowcourse data or no

snowcourse data

within_geometry: filter the points to within the shapefile

instead of just the extents. Default True

buffer: buffer added to search box

Returns:

PointDataCollection

class metloom.pointdata.files.StationInfo(value)[source]

Bases: Enum

Since there is not enough info via an API, csv readers rely on a list of stations and info associated with them to build the calls. This should allow for a common dataset with multiple stations to be verified and isolated

classmethod all_points()[source]
classmethod all_station_ids()[source]
property elevation
classmethod from_station_id(station_id)[source]
property latitude
property longitude
property path
property point
property station_id
property station_name

metloom.pointdata.geosphere_austria module

class metloom.pointdata.geosphere_austria.GeoSphereCurrentPointData(station_id, name, metadata=None)[source]

Bases: GeoSpherePointDataBase

Implement PointData methods for GeoSphere Austria data source API documentation here https://dataset.api.hub.geosphere.at/v1/docs/index.html https://dataset.api.hub.geosphere.at/v1/docs/user-guide/resource.html

Datasets available here https://data.hub.geosphere.at/dataset/

We use tawes-v1 data which consists of data from the last 3 months in 10 minute increment

ALLOWED_VARIABLES

alias of GeoSphereCurrentVariables

DATASOURCE = 'GEOSPHERE'
META_EXTENSION = '/v1/station/current/tawes-v1-10min/metadata'
URL = 'https://dataset.api.hub.geosphere.at'
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_daily_data Example query: https://dataset.api.hub.geosphere.at/v1/station/current/ tawes-v1-10min?parameters=TL&station_ids=11035

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_hourly_data

class metloom.pointdata.geosphere_austria.GeoSphereHistPointData(station_id, name, metadata=None)[source]

Bases: GeoSpherePointDataBase

Implement PointData methods for GeoSphere Austria data source API documentation here https://dataset.api.hub.geosphere.at/v1/docs/index.html https://dataset.api.hub.geosphere.at/v1/docs/user-guide/resource.html

Datasets available here https://data.hub.geosphere.at/dataset/

We use klima-v1-1d data which consists of historical daily data. There is historical hourly data, but the parameter names are different and as such this has not bee implemented

ALLOWED_VARIABLES

alias of GeoSphereHistVariables

DATASOURCE = 'GEOSPHERE'
META_EXTENSION = '/v1/station/historical/klima-v1-1d/metadata'
URL = 'https://dataset.api.hub.geosphere.at'
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_daily_data Example query: https://dataset.api.hub.geosphere.at/v1/station/historical/klima-v1-1d ?station_ids=11401&start=2023-04-12&end=2023-04-14&parameters=schnee

class metloom.pointdata.geosphere_austria.GeoSpherePointDataBase(station_id, name, metadata=None)[source]

Bases: PointData

Implement PointData methods for GeoSphere Austria data source API documentation here https://dataset.api.hub.geosphere.at/v1/docs/index.html https://dataset.api.hub.geosphere.at/v1/docs/user-guide/resource.html

Datasets available here https://data.hub.geosphere.at/dataset/

We could either use the verified klima-v1 data or the raw tawes-v1 data. Kilma has hourly and daily, taws is 10minute and most current

ALLOWED_VARIABLES = None
DATASOURCE = 'GEOSPHERE'
META_EXTENSION = None
URL = 'https://dataset.api.hub.geosphere.at'
classmethod points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], **kwargs)[source]

See docstring for PointData.points_from_geometry

The Austria Geosphere API does not allow filtering by variable. As a result, we do not filter according to which points have specific variables. The function arguments allow variables to be passed in to keep consistency with the same function from other classes.

Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription. NOT USED FOR THIS CLASS within_geometry: filter the points to within the shapefile instead of just the extents. Default True buffer: buffer added to search box, filter_to_active: filter to active stations

Returns:

PointDataCollection

metloom.pointdata.mesowest module

class metloom.pointdata.mesowest.MesowestPointData(station_id, name, token_json='~/.synoptic_token.json', metadata=None)[source]

Bases: PointData

ALLOWED_VARIABLES

alias of MesowestVariables

DATASOURCE = 'Mesowest'
MESO_URL = 'https://api.synopticdata.com/v2/stations/timeseries'
META_URL = 'https://api.synopticdata.com/v2/stations/metadata'
NO_DATA_MESSAGE = 'No stations found for this request.'
POINTS_FROM_GEOM_DEFAULTS = {'buffer': 0.0, 'token_json': '~/.synoptic_token.json', 'within_geometry': True}
classmethod create_token_json(token)[source]

Creates the neccessary synoptic token json for mesowest requests. To get public token visit: https://synopticdata.com/mesonet-api Args:

token: Syntoptic Lab’s public token.

Returns:

None

get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get daily measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get hourly measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

classmethod get_token(token_json)[source]

Return the token stored in the json

classmethod points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], **kwargs)[source]

See docstring for PointData.points_from_geometry

Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription within_geometry: filter the points to within the shapefile

instead of just the extents. Default True

buffer: buffer added to search box token_json: Path to the public token for the mesowest api

default = “~/.synoptic_token.json”

Returns:

PointDataCollection

property token

metloom.pointdata.norway module

class metloom.pointdata.norway.MetNorwayPointData(station_id, name, token_json='~/.frost_token.json', metadata=None)[source]

Bases: PointData

Class for the Norway Frost API https://frost.met.no/index.html

To create a user, go here https://frost.met.no/auth/requestCredentials.html

Data is provided by MET Norway, see license for details https://www.met.no/en/free-meteorological-data

Element (variable) information can be found here https://frost.met.no/elementtable

Observations/AvailableTimeSeries/ can be used to find out what types elements are available for a station or time range - we can use this to filter

The Sources endpoint returns metadata. It can be used to filter based on geometry and variables

For this class we will use default levels and timeoffsets. See more info here https://frost.met.no/concepts2.html#level-offset-filter

It is important to note that this class does NOT implement all of the functionality of the frost API. The frost documentation is extensive and worth looking through.

Read more about data quality codes here https://frost.met.no/dataclarifications.html

Read more about general concepts: https://frost.met.no/concepts2.html

ALLOWED_VARIABLES

alias of MetNorwayVariables

DATASOURCE = 'MET Norway'
POINTS_FROM_GEOM_DEFAULTS = {'buffer': 0.0, 'token_json': '~/.frost_token.json', 'within_geometry': True}
URL = 'https://frost.met.no/'
property auth_header

Get the auth header and set the expiration time

get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_daily_data

get_event_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get data in original frequency from API

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_hourly_data

classmethod points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], **kwargs)[source]

See docstring for PointData.points_from_geometry

Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription within_geometry: filter the points to within the shapefile

instead of just the extents. Default True

buffer: buffer added to search box token_json: Path to the public token for the mesowest api

default = “~/.frost_token.json”

Returns:

PointDataCollection

metloom.pointdata.nws_forecast module

class metloom.pointdata.nws_forecast.NWSForecastPointData(station_id, name, initial_metadata=None, metadata=None)[source]

Bases: GenericPoint

Implementation for NWS forecast API https://www.weather.gov/documentation/services-web-api

We can call the points api to get the URL for the forecast, i.e. https://api.weather.gov/points/42,-119

In this example, 3 forecast URLs are available

“forecast”: “https://api.weather.gov/gridpoints/BOI/28,28/forecast”, “forecastHourly”: “https://api.weather.gov/gridpoints/BOI/28,28/forecast/hourly”, “forecastGridData”: “https://api.weather.gov/gridpoints/BOI/28,28”,

forecast can be used to return the 12 hour increments

forecastHourly returns hourly data (hourly in local tz)

forecastGridData returns the ‘raw’ grid data (hourly UTC).

We will use the forecastGridData endpoint for this implementation

The API returns data from a grid, meaning the geometry column in the returned geodataframes will represent the CENTER of the forecast grid cell.

ALLOWED_VARIABLES

alias of NWSForecastVariables

DATASOURCE = 'NWS Forecast'
POINTS_FROM_GEOM_DEFAULTS = {'buffer': 0.0, 'token_json': '~/.frost_token.json', 'within_geometry': True}
URL = 'https://api.weather.gov'
get_daily_forecast(variables: List[SensorDescription])[source]

Get a geopandas dataframe with daily results for a 7 day forecast. The geometry column will be the center of the forecast gridcell

Args:

variables: list of variables to return

get_forecast(variables: List[SensorDescription])[source]

Get a geopandas dataframe with hourly results for a 7 day forecast. The geometry column will be the center of the forecast gridcell

Args:

variables: list of variables to return

get_hourly_forecast(variables: List[SensorDescription])[source]

Get a geopandas dataframe with hourly results for a 7 day forecast. The geometry column will be the center of the forecast gridcell

Args:

variables: list of variables to return

metloom.pointdata.sail module

class metloom.pointdata.sail.SAILPointData(station_id: str, metadata: dict | None = None, cache: str | Path = PosixPath('.cache'), token_json: str | Path = PosixPath('~/.arm_token.json'))[source]

Bases: PointData

https://adc.arm.gov/discovery/#/results/site_code::guc

ALLOWED_VARIABLES

alias of SAILStationVariables

DATASOURCE = 'SAIL'
get_daily_data(start_date: datetime, end_date: datetime, variables: list[SensorDescription])[source]

Get daily measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_hourly_data(start_date: datetime, end_date: datetime, variables: list[SensorDescription])[source]

Get hourly measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

static get_location(station_id: str, variable: SensorDescription | None = None) → tuple[float, float, float][source]

Get the location of the site and facility code.

The Gunnison SAIL site has 3 supplemental sites (S1, S2, S3, S4) and one main site (M1). The S4 site is atmospheric measurements made with a teathered balloon, thus the location is not constant and it is excluded from the hard-coded locations.

Returns a tuple of (latitude, longitude, elevation [m]) https://www.arm.gov/capabilities/observatories/guc/locations

get_snow_course_data(start_date: date, end_date: date, variables: list[SensorDescription])[source]

Get snow course data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

classmethod points_from_geometry(geometry: GeoDataFrame, variables: list[SensorDescription], snow_courses=None, within_geometry=True, buffer=0.0)[source]

Find a collection of points with measurements for certain variables contained within a shapefile. Any point in the shapefile with measurements for any of the variables should be included Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription snow_courses: boolean for including only snowcourse data or no

snowcourse data

within_geometry: filter the points to within the shapefile

instead of just the extents. Default True

buffer: buffer added to search box

Returns:

PointDataCollection

metloom.pointdata.snotel module

class metloom.pointdata.snotel.SnotelPointData(station_id, name, metadata=None)[source]

Bases: PointData

Implement PointData methods for SNOTEL data source API documentation here: https://www.nrcs.usda.gov/wps/portal/wcc/home/dataAccessHelp/webService/webServiceReference/ Website has variable and network codes Possible testing resource: https://wcc.sc.egov.usda.gov/awdbWebService/webservice/testwebservice.jsf?webserviceName=/awdbWebService

ALLOWED_VARIABLES

alias of SnotelVariables

DATASOURCE = 'NRCS'
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_daily_data

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_hourly_data

get_snow_course_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_snow_course_data

classmethod points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], **kwargs)[source]

See docstring for PointData.points_from_geometry

Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription snow_courses: boolean for including only snowcourse data or no

snowcourse data

within_geometry: filter the points to within the shapefile

instead of just the extents. Default True

buffer: buffer added to search box

Returns:

PointDataCollection

property tzinfo

tzinfo that pandas can use for tz_localize

metloom.pointdata.snotel_client module

class metloom.pointdata.snotel_client.BaseSnotelClient(**kwargs)[source]

Bases: object

Base snotel client class. Used for interacting with SNOTEL SOAP client. This is just a base class and not meant for direct use.

Example use with extended class:

MetaDataSnotelClient('TNY:CA:SNOW').get_data()
DEFAULT_PARAMS = {}
PARAMS_MAP = {'begin_date': 'beginDate', 'duration': 'duration', 'element_cd': 'elementCd', 'element_cds': 'elementCds', 'end_date': 'endDate', 'height_depth': 'heightDepth', 'max_latitude': 'maxLatitude', 'max_longitude': 'maxLongitude', 'min_latitude': 'minLatitude', 'min_longitude': 'minLongitude', 'network_cds': 'networkCds', 'ordinal': 'ordinal', 'parameter': 'parameter', 'station_triplet': 'stationTriplet', 'station_triplets': 'stationTriplets'}
SERVICE_NAME = None
URL = 'https://wcc.sc.egov.usda.gov/awdbWebService/services?WSDL'
get_data()[source]

Make the actual request and return data.

class metloom.pointdata.snotel_client.DailySnotelDataClient(begin_date: datetime, end_date: datetime, station_triplet: str, **kwargs)[source]

Bases: SeriesSnotelClient

Class for getting daily data

class metloom.pointdata.snotel_client.ElementSnotelClient(station_triplet: str, **kwargs)[source]

Bases: BaseSnotelClient

Get all station elements for a station triplet. Station triplets are descriptions of each sensor on the station

get_data returns a list of zeep objects. Zeep objects are indexible or attributes can be accessed with getattr or .

SERVICE_NAME = 'getStationElements'
class metloom.pointdata.snotel_client.HourlySnotelDataClient(begin_date: datetime, end_date: datetime, station_triplet: str, **kwargs)[source]

Bases: SeriesSnotelClient

Class for getting hourly data

DEFAULT_PARAMS = {'ordinal': 1}
DURATION = None
SERVICE_NAME = 'getHourlyData'
class metloom.pointdata.snotel_client.MetaDataSnotelClient(station_triplet: str, **kwargs)[source]

Bases: BaseSnotelClient

Read metadata from the metadata service for a particular station triplet

SERVICE_NAME = 'getStationMetadata'
get_data()[source]

Returns a dictionary of metadata values

class metloom.pointdata.snotel_client.PointSearchSnotelClient(max_latitude: float, min_latitude: float, max_longitude: float, min_longitude: float, network_cds: str, element_cds: str, **kwargs)[source]

Bases: BaseSnotelClient

Search for stations based on criteria. This search is default logical AND meaning all criteria need to be true. get_data returns a list of string station triplets

DEFAULT_PARAMS = {'logicalAnd': 'true'}
SERVICE_NAME = 'getStations'
class metloom.pointdata.snotel_client.SemiMonthlySnotelClient(begin_date: datetime, end_date: datetime, station_triplet: str, **kwargs)[source]

Bases: SeriesSnotelClient

Class for getting semi monthly (snow course) data

DURATION = 'SEMIMONTHLY'
class metloom.pointdata.snotel_client.SeriesSnotelClient(begin_date: datetime, end_date: datetime, station_triplet: str, **kwargs)[source]

Bases: BaseSnotelClient

Base extension for services that return timseries data.

DEFAULT_PARAMS = {'alwaysReturnDailyFeb29': 'false', 'getFlags': 'true', 'ordinal': 1}
DURATION = 'DAILY'
SERVICE_NAME = 'getData'
get_data(element_cd: str, **extra_params)[source]

get the timeseires data

Args:
element_cd: the variable code from the allowed variable codes

in the API

extra_params: kwargs for any extra parameters. These override

the default parameters

metloom.pointdata.snowex module

class metloom.pointdata.snowex.SnowExMet(station_id, name=None, metadata=None, cache='./cache')[source]

Bases: CSVPointData

These data are stored in csv data formats

ALLOWED_STATIONS

alias of SnowExMetInfo

ALLOWED_VARIABLES

alias of SnowExVariables

DATASOURCE = 'NSIDC'
DOI = 'https://doi.org/10.5067/497NQVJ0CBEX'
URL = 'https://n5eil01u.ecs.nsidc.org/SNOWEX/SNEX_Met.001/'
UTC_OFFSET_HOURS = 0
class metloom.pointdata.snowex.SnowExMetInfo(value)[source]

Bases: StationInfo

An enumeration.

GM_STUDY_PLOT = ('Grand Mesa Study Plot', 'GMSP', 39.05084, -108.06144, 10626, '2017.06.21/SNEX_Met_GMSP2_final_output.csv')
LS_OBS_SITE = ('Local Scale Observation Site', 'LSOS', 39.05225, -108.09792, 9791, '2016.10.09/SNEX_Met_LSOS_final_output.csv')
MESA_EAST = ('Mesa East', 'ME', 39.10358, -107.88383, 10105, '2016.10.10/SNEX_Met_ME_final_output.csv')
MESA_MIDDLE = ('Mesa Middle', 'MM', 39.03954, -107.94174, 10286, '2016.10.10/SNEX_Met_MM_final_output.csv')
MESA_WEST = ('Mesa West', 'MW', 39.03388, -108.21399, 9950, '2016.10.09/SNEX_Met_MW_final_output.csv')

metloom.pointdata.usgs module

class metloom.pointdata.usgs.USGSPointData(station_id, name, metadata=None, duration=None)[source]

Bases: PointData

Implement PointData methods for USGS data source.

APIs and help

https://waterservices.usgs.gov/rest/DV-Service.html#Service https://waterservices.usgs.gov/rest/IV-Service.html#Service https://waterservices.usgs.gov/rest/Site-Test-Tool.html

ALLOWED_VARIABLES

alias of USGSVariables

DATASOURCE = 'USGS'
META_URL = 'https://waterservices.usgs.gov/nwis/site/'
USGS_URL = 'https://waterservices.usgs.gov/nwis/'
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_daily_data

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get hourly measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_instantaneous_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

USGS ‘instantaneous’ data, which is generally 15 minutes.

classmethod points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], **kwargs)[source]

See docstring for PointData.points_from_geometry

Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription

Returns:

PointDataCollection

property tzinfo

tzinfo that pandas can use for tz_localize

Module contents

class metloom.pointdata.CDECPointData(station_id, name, metadata=None)[source]

Bases: PointData

Implement PointData methods for CDEC data source API documentation here https://cdec.water.ca.gov/dynamicapp/

ALLOWED_VARIABLES

alias of CdecStationVariables

CDEC_URL = 'http://cdec.water.ca.gov/dynamicapp/req/JSONDataServlet'
DATASOURCE = 'CDEC'
META_URL = 'https://cdec.water.ca.gov/dynamicapp/req/CSVMetaDataServlet'
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_daily_data Example query: https://cdec.water.ca.gov/dynamicapp/req/JSONDataServlet? Stations=TNY&SensorNums=3&dur_code=D&Start=2021-05-16&End=2021-05-16

get_event_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]
get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_hourly_data

get_snow_course_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_snow_course_data

is_only_monthly()[source]

determine if all sensors for a station are on a monthly interval

is_only_snow_course(variables: List[SensorDescription])[source]

Determine if a station only has snow course measurements

is_partly_snow_course()[source]

Determine if any of the snow sensors at a station are on a monthly interval Assumption: Monthly snow sensor measurements are snow courses

classmethod points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], **kwargs)[source]

See docstring for PointData.points_from_geometry

Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription snow_courses: Boolean for including only snowcourse data or no snowcourse data within_geometry: filter the points to within the shapefile instead of just the extents. Default True buffer: buffer added to search box

Returns:

PointDataCollection

class metloom.pointdata.CSASMet(station_id, name=None, metadata=None, cache='./cache')[source]

Bases: CSVPointData

ALLOWED_STATIONS

alias of CSASStationInfo

ALLOWED_VARIABLES

alias of CSASVariables

CURRENT_AVAILABLE_YEAR = 2024
DATASOURCE = 'CSAS'
DOI = ''
URL = 'https://snowstudies.org/wp-content/uploads/'
UTC_OFFSET_HOURS = -7
class metloom.pointdata.CSVPointData(station_id, name=None, metadata=None, cache='./cache')[source]

Bases: PointData

Some met station data is stored off in flat csv files. This class enables the management of downloading those files while still allowing a similar interface

ALLOWED_STATIONS

alias of StationInfo

UTC_OFFSET_HOURS = 0
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get daily measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get hourly measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

classmethod points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], within_geometry=True, buffer=0.0, **kwargs)[source]

Find a collection of points with measurements for certain variables contained within a shapefile. Any point in the shapefile with measurements for any of the variables should be included Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription snow_courses: boolean for including only snowcourse data or no

snowcourse data

within_geometry: filter the points to within the shapefile

instead of just the extents. Default True

buffer: buffer added to search box

Returns:

PointDataCollection

class metloom.pointdata.CuesLevel1(station_id, name, metadata=None)[source]

Bases: PointData

Implement PointData methods for CUES level 1 data https://snow.ucsb.edu/index.php/description/ https://snow.ucsb.edu/index.php/query-db/ https://doi.org/10.21424/R4159Q

ALLOWED_VARIABLES

alias of CuesLevel1Variables

DATASOURCE = 'UCSB CUES'
URL = 'https://snow.ucsb.edu/index.php/query-db/'
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get daily measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get hourly measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_snow_course_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get snow course data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

property metadata

Hardcode the metadata

points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], snow_courses=False, within_geometry=True, buffer=0.0)[source]

Find a collection of points with measurements for certain variables contained within a shapefile. Any point in the shapefile with measurements for any of the variables should be included Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription snow_courses: boolean for including only snowcourse data or no

snowcourse data

within_geometry: filter the points to within the shapefile

instead of just the extents. Default True

buffer: buffer added to search box

Returns:

PointDataCollection

class metloom.pointdata.GeoSphereCurrentPointData(station_id, name, metadata=None)[source]

Bases: GeoSpherePointDataBase

Implement PointData methods for GeoSphere Austria data source API documentation here https://dataset.api.hub.geosphere.at/v1/docs/index.html https://dataset.api.hub.geosphere.at/v1/docs/user-guide/resource.html

Datasets available here https://data.hub.geosphere.at/dataset/

We use tawes-v1 data which consists of data from the last 3 months in 10 minute increment

ALLOWED_VARIABLES

alias of GeoSphereCurrentVariables

DATASOURCE = 'GEOSPHERE'
META_EXTENSION = '/v1/station/current/tawes-v1-10min/metadata'
URL = 'https://dataset.api.hub.geosphere.at'
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_daily_data Example query: https://dataset.api.hub.geosphere.at/v1/station/current/ tawes-v1-10min?parameters=TL&station_ids=11035

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_hourly_data

class metloom.pointdata.GeoSphereHistPointData(station_id, name, metadata=None)[source]

Bases: GeoSpherePointDataBase

Implement PointData methods for GeoSphere Austria data source API documentation here https://dataset.api.hub.geosphere.at/v1/docs/index.html https://dataset.api.hub.geosphere.at/v1/docs/user-guide/resource.html

Datasets available here https://data.hub.geosphere.at/dataset/

We use klima-v1-1d data which consists of historical daily data. There is historical hourly data, but the parameter names are different and as such this has not bee implemented

ALLOWED_VARIABLES

alias of GeoSphereHistVariables

DATASOURCE = 'GEOSPHERE'
META_EXTENSION = '/v1/station/historical/klima-v1-1d/metadata'
URL = 'https://dataset.api.hub.geosphere.at'
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_daily_data Example query: https://dataset.api.hub.geosphere.at/v1/station/historical/klima-v1-1d ?station_ids=11401&start=2023-04-12&end=2023-04-14&parameters=schnee

class metloom.pointdata.MesowestPointData(station_id, name, token_json='~/.synoptic_token.json', metadata=None)[source]

Bases: PointData

ALLOWED_VARIABLES

alias of MesowestVariables

DATASOURCE = 'Mesowest'
MESO_URL = 'https://api.synopticdata.com/v2/stations/timeseries'
META_URL = 'https://api.synopticdata.com/v2/stations/metadata'
NO_DATA_MESSAGE = 'No stations found for this request.'
POINTS_FROM_GEOM_DEFAULTS = {'buffer': 0.0, 'token_json': '~/.synoptic_token.json', 'within_geometry': True}
classmethod create_token_json(token)[source]

Creates the neccessary synoptic token json for mesowest requests. To get public token visit: https://synopticdata.com/mesonet-api Args:

token: Syntoptic Lab’s public token.

Returns:

None

get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get daily measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get hourly measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

classmethod get_token(token_json)[source]

Return the token stored in the json

classmethod points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], **kwargs)[source]

See docstring for PointData.points_from_geometry

Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription within_geometry: filter the points to within the shapefile

instead of just the extents. Default True

buffer: buffer added to search box token_json: Path to the public token for the mesowest api

default = “~/.synoptic_token.json”

Returns:

PointDataCollection

property token
class metloom.pointdata.MetNorwayPointData(station_id, name, token_json='~/.frost_token.json', metadata=None)[source]

Bases: PointData

Class for the Norway Frost API https://frost.met.no/index.html

To create a user, go here https://frost.met.no/auth/requestCredentials.html

Data is provided by MET Norway, see license for details https://www.met.no/en/free-meteorological-data

Element (variable) information can be found here https://frost.met.no/elementtable

Observations/AvailableTimeSeries/ can be used to find out what types elements are available for a station or time range - we can use this to filter

The Sources endpoint returns metadata. It can be used to filter based on geometry and variables

For this class we will use default levels and timeoffsets. See more info here https://frost.met.no/concepts2.html#level-offset-filter

It is important to note that this class does NOT implement all of the functionality of the frost API. The frost documentation is extensive and worth looking through.

Read more about data quality codes here https://frost.met.no/dataclarifications.html

Read more about general concepts: https://frost.met.no/concepts2.html

ALLOWED_VARIABLES

alias of MetNorwayVariables

DATASOURCE = 'MET Norway'
POINTS_FROM_GEOM_DEFAULTS = {'buffer': 0.0, 'token_json': '~/.frost_token.json', 'within_geometry': True}
URL = 'https://frost.met.no/'
property auth_header

Get the auth header and set the expiration time

get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_daily_data

get_event_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get data in original frequency from API

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_hourly_data

classmethod points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], **kwargs)[source]

See docstring for PointData.points_from_geometry

Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription within_geometry: filter the points to within the shapefile

instead of just the extents. Default True

buffer: buffer added to search box token_json: Path to the public token for the mesowest api

default = “~/.frost_token.json”

Returns:

PointDataCollection

class metloom.pointdata.NWSForecastPointData(station_id, name, initial_metadata=None, metadata=None)[source]

Bases: GenericPoint

Implementation for NWS forecast API https://www.weather.gov/documentation/services-web-api

We can call the points api to get the URL for the forecast, i.e. https://api.weather.gov/points/42,-119

In this example, 3 forecast URLs are available

“forecast”: “https://api.weather.gov/gridpoints/BOI/28,28/forecast”, “forecastHourly”: “https://api.weather.gov/gridpoints/BOI/28,28/forecast/hourly”, “forecastGridData”: “https://api.weather.gov/gridpoints/BOI/28,28”,

forecast can be used to return the 12 hour increments

forecastHourly returns hourly data (hourly in local tz)

forecastGridData returns the ‘raw’ grid data (hourly UTC).

We will use the forecastGridData endpoint for this implementation

The API returns data from a grid, meaning the geometry column in the returned geodataframes will represent the CENTER of the forecast grid cell.

ALLOWED_VARIABLES

alias of NWSForecastVariables

DATASOURCE = 'NWS Forecast'
POINTS_FROM_GEOM_DEFAULTS = {'buffer': 0.0, 'token_json': '~/.frost_token.json', 'within_geometry': True}
URL = 'https://api.weather.gov'
get_daily_forecast(variables: List[SensorDescription])[source]

Get a geopandas dataframe with daily results for a 7 day forecast. The geometry column will be the center of the forecast gridcell

Args:

variables: list of variables to return

get_forecast(variables: List[SensorDescription])[source]

Get a geopandas dataframe with hourly results for a 7 day forecast. The geometry column will be the center of the forecast gridcell

Args:

variables: list of variables to return

get_hourly_forecast(variables: List[SensorDescription])[source]

Get a geopandas dataframe with hourly results for a 7 day forecast. The geometry column will be the center of the forecast gridcell

Args:

variables: list of variables to return

class metloom.pointdata.PointData(station_id, name, metadata=None)[source]

Bases: GenericPoint

Extend GenericPoint and add functions for finding data from geometry and for gettings daily, hourly, or snow course data

POINTS_FROM_GEOM_DEFAULTS = {'buffer': 0.0, 'filter_to_active': False, 'snow_courses': False, 'within_geometry': True}
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get daily measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get hourly measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_snow_course_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get snow course data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

classmethod points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], snow_courses=False, within_geometry=True, buffer=0.0)[source]

Find a collection of points with measurements for certain variables contained within a shapefile. Any point in the shapefile with measurements for any of the variables should be included Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription snow_courses: boolean for including only snowcourse data or no

snowcourse data

within_geometry: filter the points to within the shapefile

instead of just the extents. Default True

buffer: buffer added to search box

Returns:

PointDataCollection

classmethod validate_sensor_df(gdf: GeoDataFrame)[source]

Validate that the GeoDataFrame returned is formatted correctly. The goal of this method is to ensure base classes are returning a consistent format of dataframe

class metloom.pointdata.PointDataCollection(points: List[object] | None = None)[source]

Bases: object

Iterator class for a collection of PointData objects. This allows conversion to a GeoDataFrame

add_point(point)[source]

Append point to collection of PointData objects

Args:

point: PointData object

to_dataframe()[source]
Returns:

GeoDataFrame of points. Columns are [‘name’, ‘id’, ‘geometry’]

class metloom.pointdata.SAILPointData(station_id: str, metadata: dict | None = None, cache: str | Path = PosixPath('.cache'), token_json: str | Path = PosixPath('~/.arm_token.json'))[source]

Bases: PointData

https://adc.arm.gov/discovery/#/results/site_code::guc

ALLOWED_VARIABLES

alias of SAILStationVariables

DATASOURCE = 'SAIL'
get_daily_data(start_date: datetime, end_date: datetime, variables: list[SensorDescription])[source]

Get daily measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_hourly_data(start_date: datetime, end_date: datetime, variables: list[SensorDescription])[source]

Get hourly measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

static get_location(station_id: str, variable: SensorDescription | None = None) → tuple[float, float, float][source]

Get the location of the site and facility code.

The Gunnison SAIL site has 3 supplemental sites (S1, S2, S3, S4) and one main site (M1). The S4 site is atmospheric measurements made with a teathered balloon, thus the location is not constant and it is excluded from the hard-coded locations.

Returns a tuple of (latitude, longitude, elevation [m]) https://www.arm.gov/capabilities/observatories/guc/locations

get_snow_course_data(start_date: date, end_date: date, variables: list[SensorDescription])[source]

Get snow course data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

classmethod points_from_geometry(geometry: GeoDataFrame, variables: list[SensorDescription], snow_courses=None, within_geometry=True, buffer=0.0)[source]

Find a collection of points with measurements for certain variables contained within a shapefile. Any point in the shapefile with measurements for any of the variables should be included Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription snow_courses: boolean for including only snowcourse data or no

snowcourse data

within_geometry: filter the points to within the shapefile

instead of just the extents. Default True

buffer: buffer added to search box

Returns:

PointDataCollection

class metloom.pointdata.SnotelPointData(station_id, name, metadata=None)[source]

Bases: PointData

Implement PointData methods for SNOTEL data source API documentation here: https://www.nrcs.usda.gov/wps/portal/wcc/home/dataAccessHelp/webService/webServiceReference/ Website has variable and network codes Possible testing resource: https://wcc.sc.egov.usda.gov/awdbWebService/webservice/testwebservice.jsf?webserviceName=/awdbWebService

ALLOWED_VARIABLES

alias of SnotelVariables

DATASOURCE = 'NRCS'
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_daily_data

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_hourly_data

get_snow_course_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_snow_course_data

classmethod points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], **kwargs)[source]

See docstring for PointData.points_from_geometry

Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription snow_courses: boolean for including only snowcourse data or no

snowcourse data

within_geometry: filter the points to within the shapefile

instead of just the extents. Default True

buffer: buffer added to search box

Returns:

PointDataCollection

property tzinfo

tzinfo that pandas can use for tz_localize

class metloom.pointdata.SnowExMet(station_id, name=None, metadata=None, cache='./cache')[source]

Bases: CSVPointData

These data are stored in csv data formats

ALLOWED_STATIONS

alias of SnowExMetInfo

ALLOWED_VARIABLES

alias of SnowExVariables

DATASOURCE = 'NSIDC'
DOI = 'https://doi.org/10.5067/497NQVJ0CBEX'
URL = 'https://n5eil01u.ecs.nsidc.org/SNOWEX/SNEX_Met.001/'
UTC_OFFSET_HOURS = 0
class metloom.pointdata.StationInfo(value)[source]

Bases: Enum

Since there is not enough info via an API, csv readers rely on a list of stations and info associated with them to build the calls. This should allow for a common dataset with multiple stations to be verified and isolated

classmethod all_points()[source]
classmethod all_station_ids()[source]
property elevation
classmethod from_station_id(station_id)[source]
property latitude
property longitude
property path
property point
property station_id
property station_name
class metloom.pointdata.USGSPointData(station_id, name, metadata=None, duration=None)[source]

Bases: PointData

Implement PointData methods for USGS data source.

APIs and help

https://waterservices.usgs.gov/rest/DV-Service.html#Service https://waterservices.usgs.gov/rest/IV-Service.html#Service https://waterservices.usgs.gov/rest/Site-Test-Tool.html

ALLOWED_VARIABLES

alias of USGSVariables

DATASOURCE = 'USGS'
META_URL = 'https://waterservices.usgs.gov/nwis/site/'
USGS_URL = 'https://waterservices.usgs.gov/nwis/'
get_daily_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

See docstring for PointData.get_daily_data

get_hourly_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

Get hourly measurement data Args:

start_date: datetime object for start of data collection period end_date: datetime object for end of data collection period variables: List of metloom.variables.SensorDescription object

from self.ALLOWED_VARIABLES

Returns:

GeoDataFrame of data. The dataframe should be indexed on [‘datetime’, ‘site’] and have columns [‘geometry’, ‘site’, ‘measurementDate’]. Additionally, for each variables, it should have column f’{variable.name}’ and f’{variable.name}_UNITS’ See CDECPointData._get_data for example implementation and TestCDECStation.tny_daily_expected for example dataframe. Datetimes should be in UTC

get_instantaneous_data(start_date: datetime, end_date: datetime, variables: List[SensorDescription])[source]

USGS ‘instantaneous’ data, which is generally 15 minutes.

classmethod points_from_geometry(geometry: GeoDataFrame, variables: List[SensorDescription], **kwargs)[source]

See docstring for PointData.points_from_geometry

Args:

geometry: GeoDataFrame for shapefile from gpd.read_file variables: List of SensorDescription

Returns:

PointDataCollection

property tzinfo

tzinfo that pandas can use for tz_localize