def setUp(self):
self.c = UsgsRest()
def get_usgs_data(station_id,
start_date,
end_date,
parameter="00060",
cache_dir=None):
"""Get river discharge data from the USGS REST web service.
See `U.S. Geological Survey Water Services
<https://waterservices.usgs.gov/>`_ (USGS)
Parameters
----------
station_id : str
The station id to get
start_date : str
String for start date in the format: 'YYYY-MM-dd', e.g. '1980-01-01'
end_date : str
String for start date in the format: 'YYYY-MM-dd', e.g. '2018-12-31'
parameter : str
The parameter code to get, e.g. ('00060') discharge, cubic feet per second
cache_dir : str
Directory where files retrieved from the web service are cached.
If set to None then USGS_DATA_HOME env var will be used as cache directory.
Examples
--------
>>> from ewatercycle.observation.usgs import get_usgs_data
>>> data = get_usgs_data('03109500', '2000-01-01', '2000-12-31', cache_dir='.')
>>> data
<xarray.Dataset>
Dimensions: (time: 8032)
Coordinates:
* time (time) datetime64[ns] 2000-01-04T05:00:00 ... 2000-12-23T04:00:00
Data variables:
Streamflow (time) float32 8.296758 10.420501 ... 10.647034 11.694747
Attributes:
title: USGS Data from streamflow data
station: Little Beaver Creek near East Liverpool OH
stationid: 03109500
location: (40.6758974, -80.5406244)
""" # noqa: E501
if cache_dir is None:
cache_dir = os.environ["USGS_DATA_HOME"]
# Check if we have the netcdf data
netcdf = os.path.join(
cache_dir,
"USGS_" + station_id + "_" + parameter + "_" + start_date + "_" +
end_date + ".nc",
)
if os.path.exists(netcdf):
return xr.open_dataset(netcdf)
# Download the data if needed
out = os.path.join(
cache_dir,
"USGS_" + station_id + "_" + parameter + "_" + start_date + "_" +
end_date + ".wml",
)
if not os.path.exists(out):
collector = UsgsRest()
collector.filter(
start=datetime.strptime(start_date, "%Y-%m-%d"),
end=datetime.strptime(end_date, "%Y-%m-%d"),
variables=[parameter],
features=[station_id],
)
data = collector.raw()
with open(out, "w") as file:
file.write(data)
collector.clear()
else:
with open(out, "r") as file:
data = file.read()
# Convert the raw data to an xarray
data = WaterML11ToPaegan(data).feature
# We expect only 1 station
if len(data.elements) == 0:
raise ValueError("Data does not contain any station data")
else:
station = data.elements[0]
# Unit conversion from cubic feet to cubic meter per second
values = np.array(
[
float(point.members[0]["value"]) / 35.315
for point in station.elements
],
dtype=np.float32,
)
times = [point.time for point in station.elements]
attrs = {
"units": "cubic meters per second",
}
# Create the xarray dataset
ds = xr.Dataset({"streamflow": (["time"], values, attrs)},
coords={"time": times})
# Set some nice attributes
ds.attrs["title"] = "USGS Data from streamflow data"
ds.attrs["station"] = station.name
ds.attrs["stationid"] = station.get_uid()
ds.attrs["location"] = (station.location.y, station.location.x)
ds.to_netcdf(netcdf)
return ds
