def _add_model(self,model_df,name,freq='d'):
self.selection._add(name)
m = ObjectDict(freq=SAMPLE_RATE[freq])
self.models[name] = m
m.name = name
m.data = ObjectDict()
m.obs = ObjectDict()
m.data.raw = model_df.loc[self.period]
if self.obs.freq != 'y':
if freq == 'd' and self.obs.freq == 'd':
m.data.daily,m.obs.daily = self._intersect(m.data.raw, self.obs.data)
m.data.monthly = resample_to_months_df(m.data.daily, self.aggr_how)
m.obs.monthly = resample_to_months_df(m.obs.daily, self.aggr_how)
elif freq == 'm' or self.obs.freq == 'm':
if freq == 'm':
_mod = m.data.raw.resample(rule='m', how=self.aggr_how)
else: # assume must be daily
_mod = resample_to_months_df(m.data.raw, self.aggr_how)
if self.obs.freq == 'm':
_obs = self.obs.data.resample(rule='m', how=self.aggr_how)
else:
_obs = resample_to_months_df(self.obs.data, self.aggr_how)
m.data.monthly,m.obs.monthly = self._intersect(_mod,_obs)
else:
raise Exception('model freq is %s' % repr(freq))
m.data.annual = resample_to_years_df(m.data.monthly, self.aggr_how, min_months=6)
m.obs.annual = resample_to_years_df(m.obs.monthly, self.aggr_how, min_months=6)
m.data.annual,m.obs.annual = self._intersect(m.data.annual,m.obs.annual)
else: #obs are annual (recharge)
m.data.annual = m.data.raw.resample(rule='a', how=self.aggr_how)
m.obs.annual = self.obs.data
m.stats = ObjectDict(freq=freq)
m.stats.daily = None
m.stats.monthly = None
if freq == 'd' and self.obs.freq == 'd':
m.stats.daily = build_stats_df(m.obs.daily, m.data.daily, m.obs.daily.keys())
if self.obs.freq != 'y':
m.stats.monthly = build_stats_df(m.obs.monthly, m.data.monthly, m.obs.monthly.keys())
if m.obs.annual is not None and m.data.annual is not None:
m.stats.annual = build_stats_df(m.obs.annual, m.data.annual, m.obs.annual.keys())
self.build_objfunc_stats(m.stats)
self._assign_colours()
def _index_results(results_folder):
filenames = _identify_results_files(results_folder)
expected_variables = _identify_variables(filenames)
result = ObjectDict()
result.metadata_from = filenames[0]
result.name = None
result.extent = _infer_extent(result.metadata_from, results_folder)
result.period = _infer_time_period(result.metadata_from, results_folder)
result.variables = expected_variables
result.model_version = _infer_model_version(result.metadata_from,
results_folder)
return result