def get(self, request,resampling_serie_id, *args, **kwargs):
resampling_serie = ResamplingSerie.objects.get(id=resampling_serie_id)
if not resampling_serie.probabilities:
pass
self.context['resampling_serie'] = resampling_serie
self.context["graph"] = plot_web([[resampling_serie.data,resampling_serie.probabilities],], 'Permanence Curve',"percent","%",['quantile',],'m3/s',mode='markers')
return render(request,'resampling_information.html',self.context)
def ReferenceFlow(self):
data = {}
xys = []
names = []
for type_data in self.daily:
daily = self.daily[type_data]
daily['month'] = [o.month for o in daily.index]
mean_by_month = daily.groupby('month').quantile(q=0.05,
numeric_only=True)
data[type_data] = [d[0] for d in mean_by_month.values]
date = pd.DatetimeIndex(
[datetime(1900, m, 1) for m in set(daily['month'].values)])
xys.append([date, data[type_data]])
names.append(type_data)
graph = plot_web(xys=xys,
title=_("Reference Flow"),
variable=_("Flow"),
unit="m³/s",
names=names)
return [
Table(month_names[i], data['pre_data'][i], data['pos_data'][i])
for i in range(12)
], graph
def get_or_create_reduced_series(self):
self.update_originals()
self.update_variables_and_sources()
self.reduceds = []
graphs = []
for original in self.originals:
temporals = TemporalSerie.objects.filter(
id=original.temporal_serie_id)
daily = self.create_daily_data_pandas(temporals)
anos_hidrologicos = self.hydrologic_years_dict(daily)
discretizations = self.discretizations[:]
for discretization in discretizations:
graph, names = self.get_graphs_data(daily, original,
discretization)
if graph.xys:
graph.graph = self.create_graph(graph.xys,
original.variable,
original.unit,
discretization, names)
graphs.append(graph)
if self.plot_permancence_curve:
xys = self.get_permancence_curve_data(daily)
graph = generic_obj()
graph.graph = plot_web(xys, _('Permanence Curve'),
original.variable, original.unit, [
_('data'),
], '%')
graph.title = _('Permanence Curve')
graphs.append(graph)
self.reduceds = graphs
return self.reduceds
def create_graph(self, xys, variable, unit, discretization, names):
return plot_web(xys=xys,
title=_("%(discretization)s %(variable)s") % {
'variable': _('frequency of change'),
'discretization': str(discretization)
},
variable=_('frequency'),
unit='un',
names=names)
def Group2cv(self):
datas = []
discretizations = list(
Discretization.objects.filter(stats_type__type='rolling mean'))
discretizations.sort(key=lambda x: int(x.pandas_code))
graphs = []
for discretization in discretizations:
for reduction in Reduction.objects.filter(
stats_type__type='rolling mean'):
data_mean = {}
xys = []
names = []
for type_data in self.daily:
daily_data = self.daily[type_data]
basic_stats = RollingMean(self.station.id,
self.variable.id)
reduced, date, data = basic_stats.get_or_create_reduced_data(
self.daily[type_data], self.original[type_data],
discretization, reduction, None, None, self.start_year,
self.end_year)
mean_ = sum(data) / len(data)
std_ = std(data)
cv = std_ / mean_
data_mean[type_data] = round(cv, 2)
xys.append([date, data])
names.append(type_data)
xys.append([[date[0], date[-1]], [mean_, mean_]])
names.append(type_data + " " + _("mean"))
graph = plot_web(
xys=xys,
title=_("%(discretization)s %(variable)s %(reduction)s") %
{
'variable': str(self.variable),
'discretization': str(discretization),
'reduction': str(reduction.type)
},
variable=self.variable,
unit="m³/s",
names=names)
graphs.append(graph)
line = Table(
'Annual %(reduction)s %(discretization)s means' % {
'reduction': reduction.type,
'discretization': discretization.type
},
data_mean['pre_data'],
data_mean['pos_data'],
)
datas.append(line)
return datas, graphs
def create_graph(self, xys, variable, unit, discretization, names):
'''
This method is responsible to return the graph. To change the graph.
'''
return plot_web(xys=xys,
title=_("%(discretization)s %(variable)s") % {
'variable': str(variable),
'discretization': str(discretization)
},
variable=variable,
unit=unit,
names=names)