def get_usgs_data(SITE, strtDT=''):
# Grab USGS data for comparison
if strtDT == '':
strtDT = '2003-01-01'
nw = wa.nwis('dv', SITE, 'sites', startDT=strtDT)
data = nw.data
label = nw.sites.station_nm[0].title()
data['cfd'] = data.value * 86400 # cfs to cfd
if isinstance(data.index, pd.core.index.MultiIndex):
data.index = data.index.droplevel(0)
# convert to acft by converting from monthly sum of cfd to acft-mo
acft = data['cfd'].groupby(pd.TimeGrouper('MS',
label='left')).sum() * 2.29569E-5
acft = acft.to_frame()
acft['month'] = acft.index.month
return acft, label
def test_fdc():
d16 = wa.nwis('dv', '01659500', 'sites')
ci = wa.fdc(d16.data, 'value', 1900, 2016)
assert type(ci[0]) == list
def test_nwis_gw():
nw = wa.nwis('gwlevels', '16010204', 'huc', siteStatus='all')
df = nw.avg_wl()
assert len(df) > 5
def test_nwis():
nw = wa.nwis('dv', '01585200', 'sites')
assert len(nw.sites) == 1
def test_get_info():
nw = wa.nwis('gwlevels', '16010204', 'huc', siteStatus='all')
df = nw.get_info(siteStatus='all')
assert 'site_no' in list(df.columns)
def test_gantt():
ashley = wa.nwis('dv', '09265500', 'sites')
gn = wa.gantt(ashley.data, stations=['value'])
assert type(gn.gantt()[2]) == matplotlib.figure.Figure