def get(self, request, *args, **kwargs):
self.set_session_value('startpage', 'measurement_point',
'No Time Series Selected')
self.set_session_value('startpage', 'uuid', 'EMPTY')
self.request.session.modified = True
page = self.request.GET.get('active', 'startpage')
self.request.session[page]['selected_coords'] = self.coordinates
self.request.session['map_']['bounds'] = self.bounds
if len(self.timeseries) == 1:
x = self.timeseries[0]
self.set_session_value(
'startpage', 'measurement_point', "Groundwater well: " +
x['location']['name'] + ' - ' + x['name'])
self.set_session_value(
'startpage', 'datepicker', {
'start':
jsdt.js_to_datetime(int(
x['first_value_timestamp'])).strftime('%d-%m-%Y'),
'end':
jsdt.js_to_datetime(int(
x['last_value_timestamp'])).strftime('%d-%m-%Y')
})
self.set_session_value('startpage', 'start_js',
x['first_value_timestamp'])
self.set_session_value('startpage', 'end_js',
x['last_value_timestamp'])
self.set_session_value('startpage', 'uuid', x['uuid'])
self.request.session['disabled']['trend_detection'] = 'enabled'
self.set_session_value('trend_detection', 'active', True)
self.request.session.modified = True
return super().get(request, *args, **kwargs)
def step_trend(self):
try:
split = int(self.request.session['trend_detection']['graph']['x'])
if split == 0:
return
breakpoint = jsdt.js_to_datetime(split)
return [calculator.step(
data=self.pandas_timeseries,
bp=int(self.pandas_timeseries.index.searchsorted(breakpoint)),
alpha=float(self.request.session[
'trend_detection']['spinner_0']['value']),
detrend_anyway=True
)]
except KeyError:
return
def get(self, request, *args, **kwargs):
page = self.request.GET.get('active', 'startpage')
self.request.session[page]['selected_coords'] = self.coordinates
self.request.session['map_']['bounds'] = self.bounds
if len(self.timeseries) == 1:
x = self.timeseries[0]
self.set_session_value('startpage', 'measurement_point',
"Groundwater well: " + x['location']['name']
+ ' - ' + x['name'])
self.set_session_value('startpage', 'datepicker', {
'start': jsdt.js_to_datetime(
int(x['first_value_timestamp'])).strftime('%d-%m-%Y'),
'end': jsdt.js_to_datetime(
int(x['last_value_timestamp'])).strftime('%d-%m-%Y')
})
self.set_session_value('startpage', 'start_js', x[
'first_value_timestamp'])
self.set_session_value('startpage', 'end_js', x[
'last_value_timestamp'])
self.set_session_value('startpage', 'uuid', x['uuid'])
self.request.session['disabled']['trend_detection'] = 'enabled'
self.set_session_value('trend_detection', 'active', True)
self.request.session.modified = True
return super().get(request, *args, **kwargs)
def step_trend(self):
try:
split = int(self.request.session['trend_detection']['graph']['x'])
if split == 0:
return
breakpoint = jsdt.js_to_datetime(split)
return [
calculator.step(
data=self.pandas_timeseries,
bp=int(
self.pandas_timeseries.index.searchsorted(breakpoint)),
alpha=float(self.request.session['trend_detection']
['spinner_0']['value']),
detrend_anyway=True)
]
except KeyError:
return
def to_date(self, date_extreme):
return jsdt.js_to_datetime(int(self.request.GET[date_extreme])
).strftime('%d-%m-%Y')
def bbox(self,
south_west,
north_east,
statistic=None,
start='0001-01-01T00:00:00Z',
end=None,
organisation=None):
"""
Find all timeseries within a certain bounding box.
Returns records within bounding box using Bounding Box format (min Lon,
min Lat, max Lon, max Lat). Also returns features with overlapping
geometry.
:param south_west: lattitude and longtitude of the south-western point
:param north_east: lattitude and longtitude of the north-eastern point
:param start_: start timestamp in ISO 8601 format
:param end: end timestamp in ISO 8601 format
:return: a dictionary of the api-response.
"""
if not end:
end = jsdatetime.now_iso()
if isinstance(start, int):
start -= 10000
if isinstance(end, int):
end += 10000
min_lat, min_lon = south_west
max_lat, max_lon = north_east
polygon_coordinates = [
[min_lon, min_lat],
[min_lon, max_lat],
[max_lon, max_lat],
[max_lon, min_lat],
[min_lon, min_lat],
]
points = [' '.join([str(x), str(y)]) for x, y in polygon_coordinates]
geom_within = {'a': 'POLYGON ((' + ', '.join(points) + '))'}
geom_within = urllib.parse.urlencode(geom_within).split('=')[1]
org_query = self.organisation_query(organisation)
self.statistic = statistic
if statistic == 'mean':
statistic = ['count', 'sum']
elif not statistic:
statistic = ['min', 'max', 'count', 'sum']
self.statistic = None
elif statistic == 'range (max - min)':
statistic = ['min', 'max']
elif statistic == 'difference (last - first)':
statistic = 'count'
elif statistic == 'difference (mean last - first year)':
year = dt.timedelta(days=366)
first_end = jsdatetime.datetime_to_js(
jsdatetime.js_to_datetime(start) + year)
last_start = jsdatetime.datetime_to_js(
jsdatetime.js_to_datetime(end) - year)
self.get(start=start,
end=first_end,
min_points=1,
fields=['count', 'sum'],
location__geom_within=geom_within,
**org_query)
first_year = {}
for r in self.results:
try:
first_year[r['location']['uuid']] = {
'first_value_timestamp': r['first_value_timestamp'],
'mean': r['events'][0]['sum'] / r['events'][0]['count']
}
except IndexError:
first_year[r['location']['uuid']] = {
'first_value_timestamp': np.nan,
'mean': np.nan
}
self.results = []
self.get(start=last_start,
end=end,
min_points=1,
fields=['count', 'sum'],
location__geom_within=geom_within,
**org_query)
for r in self.results:
try:
r['events'][0]['difference (mean last - first year)'] = \
r['events'][0]['sum'] / r['events'][0]['count'] - \
first_year[r['location']['uuid']]['mean']
r['first_value_timestamp'] = \
first_year[
r['location']['uuid']]['first_value_timestamp']
except IndexError:
r['events'] = [{
'difference (mean last - first year)':
np.nan
}]
r['first_value_timestamp'] = np.nan
r['last_value_timestamp'] = np.nan
return
self.get(start=start,
end=end,
min_points=1,
fields=statistic,
location__geom_within=geom_within,
**org_query)
def to_date(self, date_extreme):
return jsdt.js_to_datetime(int(
self.request.GET[date_extreme])).strftime('%d-%m-%Y')
