def test_fetch_data_many_stations(self):
""" Test download nultiple agrimet station data download.
This runs through a list of stations, reformats data, checks unit conversion,
and Pandas.DataFrame
:return:
"""
for site in self.outside_PnGp_sites:
agrimet = Agrimet(station=site,
start_date='2015-05-15',
end_date='2015-05-15',
interval='daily')
raw = agrimet.fetch_met_data(return_raw=True)
formed = agrimet.fetch_met_data()
params = ['et', 'mm', 'pc', 'sr', 'wr']
for param in params:
key = '{}_{}'.format(site, param)
converted = formed[param.upper()].values.flatten()[0]
unconverted = raw[key].values.flatten()[0]
if param in ['et', 'pc']:
unconverted *= 25.4
if param == 'mm':
unconverted = (unconverted - 32) * 5 / 9
if param == 'sr':
unconverted *= 41868.
if param == 'wr':
unconverted *= 1609.34
if isnan(converted):
pass
elif agrimet.empty_df:
pass
else:
self.assertAlmostEqual(converted, unconverted, delta=0.01)
def test_fetch_data(self):
""" Test download agrimet data within time slice.
Test refomatting of data, test unit converstion to std units.
Checked data is in a Pandas.DataFrame object.
:return:
"""
agrimet = Agrimet(station=self.fetch_site,
start_date='2015-01-01',
end_date='2015-12-31',
interval='daily')
raw = agrimet.fetch_met_data(return_raw=True)
formed = agrimet.fetch_met_data()
a = raw.iloc[1, :].tolist()
b = formed.iloc[1, :].tolist()
# dates equality
self.assertEqual(a[0], b[0])
self.assertEqual(a[0], '2015-01-02')
# in to mm
self.assertEqual(a[2], b[2] / 25.4)
# deg F to deg C
self.assertAlmostEqual(a[4], b[4] * 1.8 + 32, delta=0.01)
# in to mm
self.assertEqual(a[7], b[7] / 25.4)
# Langleys to J m-2
self.assertEqual(a[9], b[9] / 41868.)
# mph to m sec-1
self.assertEqual(a[12], b[12] / 0.44704)
def test_web_retrieval_all_stations_met(self):
for s in self.all_stations:
a = Agrimet(station=s,
start_date=self.start_season,
end_date=self.end_season,
interval='daily')
try:
a.fetch_met_data()
print('{} appears valid'.format(a.station))
except:
print('{} appears invalid'.format(a.station))
def test_agrimet_mesonet_gridmet_etr(self):
agrimet = Agrimet(station=self.fetch_site,
start_date=self.start,
end_date=self.end,
interval='daily')
formed = agrimet.fetch_met_data()
agri_etr = formed['ETRS'].values
s, e = datetime.strptime(self.start, '%Y-%m-%d'), \
datetime.strptime(self.end, '%Y-%m-%d')
gridmet = GridMet('etr', start=s, end=e, lat=self.lat, lon=self.lon)
gridmet_etr = gridmet.get_point_timeseries()
gridmet_etr = gridmet_etr.values
mco = Mesonet(self.covm_mco, start=self.start, end=self.end)
mesonet_daily = mco.mesonet_etr(lat=46.3, elevation=1000.0)
mesonet_etr = mesonet_daily['ETR'].values
plt.plot(gridmet_etr[121:273], label='gridmet')
plt.plot(mesonet_etr[121:273], label='mesonet')
plt.plot(agri_etr[121:273], label='agrimet')
plt.xlabel('GROWING SEASON DAY (MAY 01 - SEP 30)')
plt.ylabel('Tall Crop Reference ET (mm) daily')
plt.legend()
# plt.show()
saved = os.path.join(os.path.dirname(__file__),
'grid_agri_meso_fig.png')
print('saved to {}'.format(saved))
plt.savefig(saved)
ga_ratio = gridmet_etr[121:273].sum() / agri_etr[121:273].sum()
print('gridmet - agrimet ratio: {}'.format(ga_ratio))
ma_ratio = mesonet_etr[121:273].sum() / agri_etr[121:273].sum()
print('mesonet - agrimet ratio: {}'.format(ma_ratio))
def test_pacific_nw_met(self):
a = Agrimet(station=self.pn_site,
start_date=self.start,
end_date=self.end,
interval='daily')
df = a.fetch_met_data()
self.assertIsInstance(a, Agrimet)
def test_great_plains_met(self):
a = Agrimet(station=self.gp_site,
start_date=self.start,
end_date=self.end,
interval='daily')
df = a.fetch_met_data()
self.assertIsInstance(a, Agrimet)
def test_agrimet_gridmet_precip(self):
agrimet = Agrimet(station=self.fetch_site,
start_date=self.start,
end_date=self.end,
interval='daily')
formed = agrimet.fetch_met_data()
agri_ppt = formed['PP'].values
s, e = datetime.strptime(self.start, '%Y-%m-%d'), \
datetime.strptime(self.end, '%Y-%m-%d')
gridmet = GridMet('pr', start=s, end=e, lat=self.lat, lon=self.lon)
gridmet_ppt = gridmet.get_point_timeseries()
gridmet_ppt = gridmet_ppt.values
difference = abs(gridmet_ppt - agri_ppt)
self.assertLess(nanmean(difference), -100000)
def get_agrimet_etr(self, yr, first=False):
agrimet = Agrimet(station=self.station, start_date=START.format(yr),
end_date=END.format(yr), interval='daily')
formed = agrimet.fetch_met_data()
if isnull(formed['ETRS']).values.sum() == formed['ETRS'].shape[0]:
agri_etr = formed['ETRS'].groupby(lambda x: x.month).sum().values
else:
agri_etr = formed['ETRS'].groupby(lambda x: x.month).sum().values
if first:
self.elev = get_elevation(agrimet.station_coords[0], agrimet.station_coords[1])
print('Station {}'.format(self.station))
if formed['YM'].values.mean() != nan:
t_dew = formed['YM'].values
ea = calcs._sat_vapor_pressure(t_dew)[0]
tmin, tmax = formed['MN'].values, formed['MX'].values
try:
doy = formed.index.strftime('%j').astype(int).values
except AttributeError:
doy = formed.index.strftime('%j').astype(int)
doy = doy.reshape((len(doy), 1))[0]
lat = agrimet.station_coords[0]
rs = formed['SR'].values
uz = formed['UA'].values
zw = 2.0
formed['calc_etr'] = Daily(tmin=tmin, tmax=tmax, ea=ea, rs=rs, uz=uz, zw=zw, elev=self.elev,
lat=lat, doy=doy).etr()
calc_etr = formed['calc_etr'].groupby(lambda x: x.month).sum().values
else:
calc_etr = empty(agri_etr.shape)
calc_etr[:] = 0.0
return agri_etr, calc_etr
def test_agrimet_gridmet_etr(self):
agrimet = Agrimet(station=self.fetch_site,
start_date=self.start,
end_date=self.end,
interval='daily')
formed = agrimet.fetch_met_data()
agri_etr = formed['ETRS'].values
s, e = datetime.strptime(self.start, '%Y-%m-%d'), \
datetime.strptime(self.end, '%Y-%m-%d')
gridmet = GridMet('etr', start=s, end=e, lat=self.lat, lon=self.lon)
gridmet_etr = gridmet.get_point_timeseries()
gridmet_etr = gridmet_etr.values
# plt.plot(gridmet_etr, label='gridmet')
# plt.plot(agri_etr, label='agrimet')
# plt.legend()
# plt.show()
# plt.close()
ratio = agri_etr.sum() / gridmet_etr.sum()
print('ratio: {}'.format(ratio))