def test_glg(self):
idx = pd.date_range('20000101', '20550101', freq='d')
s = pd.Series([x.month + x.day for x in idx], index=idx)
ml = Model(s)
ml.freq = 'D'
v = ml.stats.glg(key='observations')
assert v == 16.
def test_ghg_linear(self):
idx = pd.to_datetime(['20160113', '20160115'])
s = pd.Series([0., 10.], index=idx)
ml = Model(s)
ml.freq = 'D'
v = ml.stats.ghg(key='observations', fill_method='linear')
assert v == 5.
def test_ghg_len_yearly(self):
idx = pd.date_range('20000101', '20550101', freq='d')
s = pd.Series(np.ones(len(idx)), index=idx)
ml = Model(s)
ml.freq = 'D'
v = ml.stats.ghg(key='observations', output='yearly')
assert len(v) == 55
def test_q_gvg_nan(self):
idx = pd.to_datetime(['20160820', '20160901', '20161120'])
s = pd.Series([0, 5, 10], index=idx)
ml = Model(s)
ml.freq = 'D'
v = ml.stats.q_gvg(key='observations')
assert np.isnan(v)
def test_ghg(self):
idx = pd.to_datetime(['20160114', '20160115', '20160128', '20160214'])
s = pd.Series([10., 3., 30., 20], index=idx)
ml = Model(s)
ml.freq = 'D'
v = ml.stats.ghg(key='observations')
assert v == 20.
def test_q_gvg(self):
idx = pd.to_datetime(['20160320', '20160401', '20160420'])
s = pd.Series([0, 5, 10], index=idx)
ml = Model(s)
ml.freq = 'D'
v = ml.stats.q_gvg(key='observations')
assert v == 2.5
def test_q_gxg_nan(self):
idx = pd.date_range('20160101', freq='d', periods=3)
s = pd.Series([1, 3, np.nan], index=idx)
ml = Model(s)
ml.freq = 'D'
v = ml.stats.q_ghg(key='observations', q=.5)
assert v == 2.
def add_recharge(self,
ml: ps.Model,
rfunc=ps.Gamma,
recharge=ps.rch.Linear(),
recharge_name: str = "recharge") -> None:
"""Add recharge to a pastas model.
Uses closest precipitation and evaporation timeseries in database.
These are assumed to be labeled with kind = 'prec' or 'evap'.
Parameters
----------
ml : pastas.Model
pastas.Model object
rfunc : pastas.rfunc, optional
response function to use for recharge in model,
by default ps.Gamma (for different response functions, see
pastas documentation)
recharge : ps.RechargeModel
recharge model to use, default is ps.rch.Linear()
recharge_name : str
name of the RechargeModel
"""
# get nearest prec and evap stns
names = []
for var in ("prec", "evap"):
try:
name = self.get_nearest_stresses(ml.oseries.name,
kind=var).iloc[0, 0]
except AttributeError:
msg = "No precipitation or evaporation timeseries found!"
raise Exception(msg)
if isinstance(name, float):
if np.isnan(name):
raise ValueError(f"Unable to find nearest '{var}' stress! "
"Check X and Y coordinates.")
else:
names.append(name)
if len(names) == 0:
msg = "No precipitation or evaporation timeseries found!"
raise Exception(msg)
# get data
tsdict = self.conn.get_stresses(names)
stresses = []
for (k, s), setting in zip(tsdict.items(), ("prec", "evap")):
metadata = self.conn.get_metadata("stresses", k, as_frame=False)
stresses.append(
ps.TimeSeries(s, name=k, settings=setting, metadata=metadata))
# add recharge to model
rch = ps.RechargeModel(stresses[0],
stresses[1],
rfunc,
name=recharge_name,
recharge=recharge,
settings=("prec", "evap"),
metadata=[i.metadata for i in stresses])
ml.add_stressmodel(rch)
def test_q_glg(self):
n = 101
idx = pd.date_range('20160101', freq='d', periods=n)
s = pd.Series(np.arange(n), index=idx)
ml = Model(s)
ml.freq = 'D'
v = ml.stats.q_glg(key='observations', q=.06)
assert v == 6.
def test_gvg(self):
idx = pd.to_datetime(['20170314', '20170328', '20170414', '20170428'])
s = pd.Series([1., 2., 3., 4], index=idx)
ml = Model(s)
ml.freq = 'D'
v = ml.stats.gvg(key='observations',
fill_method='linear',
output='mean')
assert v == 2.
def test_glg_fill_limit_null(self):
idx = pd.to_datetime(['20170101', '20170131', '20200101'])
s = pd.Series(np.ones(len(idx)), index=idx)
ml = Model(s)
ml.freq = 'D'
v = ml.stats.glg(key='observations',
fill_method='linear',
limit=10,
output='yearly')
assert v.count() == 0
def test_gvg_nan(self):
idx = pd.to_datetime(['20170228', '20170428', '20170429'])
s = pd.Series([1., 2., 3.], index=idx)
ml = Model(s)
ml.freq = 'D'
v = ml.stats.gvg(
key='observations',
fill_method=None,
output='mean',
)
assert np.isnan(v)
def add_recharge(self, ml: ps.Model, rfunc=ps.Gamma) -> None:
"""Add recharge to a pastas model.
Uses closest precipitation and evaporation timeseries in database.
These are assumed to be labeled with kind = 'prec' or 'evap'.
Parameters
----------
ml : pastas.Model
pastas.Model object
rfunc : pastas.rfunc, optional
response function to use for recharge in model,
by default ps.Gamma (for different response functions, see
pastas documentation)
"""
# get nearest prec and evap stns
names = []
for var in ("prec", "evap"):
try:
name = self.get_nearest_stresses(
ml.oseries.name, kind=var).iloc[0, 0]
except AttributeError:
msg = "No precipitation or evaporation timeseries found!"
raise Exception(msg)
names.append(name)
if len(names) == 0:
msg = "No precipitation or evaporation timeseries found!"
raise Exception(msg)
# get data
tsdict = self.conn.get_stresses(names)
stresses = []
for k, s in tsdict.items():
metadata = self.conn.get_metadata("stresses", k, as_frame=False)
s = self.conn._get_dataframe_values("stresses", k, s,
metadata=metadata)
stresses.append(ps.TimeSeries(s, name=k, metadata=metadata))
# add recharge to model
rch = ps.StressModel2(stresses, rfunc, name="recharge",
metadata=[i.metadata for i in stresses],
settings=("prec", "evap"))
ml.add_stressmodel(rch)
def test_q_gxg_series(self):
s = pd.read_csv('tests/data/hseries_gxg.csv', index_col=0, header=0,
parse_dates=True, dayfirst=True,
squeeze=True,)
ml = Model(s)
ml.freq = 'D'
ghg = ml.stats.q_ghg(key='observations')
glg = ml.stats.q_glg(key='observations')
gvg = ml.stats.q_gvg(key='observations')
print('\n')
print('calculated GXG\'s percentile method: \n')
print(('GHG: {ghg:.2f} m+NAP\n'
'GLG: {glg:.2f} m+NAP\n'
'GVG: {gvg:.2f} m+NAP\n').format(
ghg=ghg, glg=glg, gvg=gvg))
print('Menyanthes GXG\'s: \n')
print(('GHG: {ghg:.2f} m+NAP\n'
'GLG: {glg:.2f} m+NAP\n'
'GVG: {gvg:.2f} m+NAP\n').format(
ghg=-3.23, glg=-3.82, gvg=-3.43))