def species(group_code):
from art17.species import get_dal
species_dataset = get_dal()
if not species_dataset:
flask.flash('No active consultation', 'danger')
return flask.redirect('/')
user_id = flask.g.identity.id
data_by_region = (
species_dataset
.get_subject_region_overview_consultation(user_id)
)
relevant_regions = set(reg for n, reg in data_by_region)
bioreg_list = [
r for r in dal.get_biogeo_region_list()
if r.code in relevant_regions
]
return flask.render_template(
'consultation/species.html', **{
'bioreg_list': bioreg_list,
'tabmenu_data': list(get_tabmenu_data()),
'species_group': dal.get_species_group(group_code),
'species_list': dal.get_species_list(group_code=group_code),
'data_by_region': data_by_region,
})
def get_context_data(self):
dal_object = self.ds_model(self.dataset_id)
dataset = models.Dataset.query.get_or_404(self.dataset_id)
object_regions = dal_object.get_subject_region_overview_aggregation()
relevant_regions = set(reg for n, reg in object_regions)
bioreg_list = dal.get_biogeo_region_list(relevant_regions)
if dataset.preview:
tabmenu = get_tabmenu_preview(dataset)
else:
tabmenu = list(get_tabmenu_data(dataset))
return {
'current_tab': self.current_tab,
'bioreg_list': bioreg_list,
'tabmenu_data': tabmenu,
'dataset_url': flask.url_for('.dashboard',
dataset_id=self.dataset_id),
'dataset_id': self.dataset_id,
'object_list': self.get_object_list(dataset),
'object_regions': object_regions,
'dataset': dataset,
'habitat_count': dataset.agg_habitat.count(),
'species_count': dataset.agg_species.count(),
}
def get_context(self, **kwargs):
checklist_id = get_reporting_id()
current_checklist = get_checklist(checklist_id)
checklist_id = current_checklist.id
species_refvals = load_species_refval()
species_checklist = get_species_checklist(dataset_id=checklist_id)
species_data = parse_checklist_ref(species_checklist)
species_list = get_species_checklist(groupped=True,
dataset_id=checklist_id)
habitat_refvals = load_habitat_refval()
habitat_checklist = get_habitat_checklist(dataset_id=checklist_id)
habitat_data = parse_checklist_ref(habitat_checklist)
habitat_list = get_habitat_checklist(distinct=True,
dataset_id=checklist_id,
groupped=True)
relevant_regions = (
{s.bio_region for s in species_checklist}.union(
{h.bio_region for h in habitat_checklist}
))
bioreg_list = dal.get_biogeo_region_list(relevant_regions)
groups = dict(
LuGrupSpecie.query
.with_entities(LuGrupSpecie.code, LuGrupSpecie.description)
)
return dict(
species_refvals=species_refvals,
species_data=species_data,
species_list=species_list,
habitat_refvals=habitat_refvals,
habitat_data=habitat_data,
habitat_list=habitat_list,
bioreg_list=bioreg_list,
GROUPS=groups,
current_checklist=current_checklist,
page='refvalues',
)
def aggregation_missing_data_report(dataset_id):
dataset = models.Dataset.query.get_or_404(dataset_id)
species = dataset.species_objs.filter_by(cons_role=ROLE_MISSING)
habitats = dataset.habitat_objs.filter_by(cons_role=ROLE_MISSING)
species_list = set([s.species for s in species])
habitat_list = set([h.habitat for h in habitats])
species_regions = {(s.subject_id, s.region): 0 for s in species}
habitat_regions = {(h.subject_id, h.region): 0 for h in habitats}
relevant_regions = [
a[1] for a in species_regions.keys() + habitat_regions.keys()
]
bioreg_list = dal.get_biogeo_region_list(relevant_regions)
return {
'missing_species': species,
'species_regions': species_regions,
'species_list': species_list,
'habitat_list': habitat_list,
'missing_habitats': habitats,
'habitat_regions': habitat_regions,
'bioreg_list': bioreg_list,
}
def habitats():
from art17.habitat import get_dal
habitat_dataset = get_dal()
if not habitat_dataset:
flask.flash('No active consultation', 'danger')
return flask.redirect('/')
user_id = flask.g.identity.id
data_by_region = (
habitat_dataset
.get_subject_region_overview_consultation(user_id)
)
relevant_regions = set(reg for n, reg in data_by_region)
bioreg_list = [
r for r in dal.get_biogeo_region_list()
if r.code in relevant_regions
]
return flask.render_template(
'consultation/habitat.html', **{
'bioreg_list': bioreg_list,
'tabmenu_data': list(get_tabmenu_data()),
'habitat_list': dal.get_habitat_list(),
'data_by_region': data_by_region,
})
def aggregation_missing_data_report(dataset_id):
dataset = models.Dataset.query.get_or_404(dataset_id)
species = dataset.species_objs.filter_by(cons_role=ROLE_MISSING)
habitats = dataset.habitat_objs.filter_by(cons_role=ROLE_MISSING)
species_list = set([s.species for s in species])
habitat_list = set([h.habitat for h in habitats])
species_regions = {(s.subject_id, s.region): 0 for s in species}
habitat_regions = {(h.subject_id, h.region): 0 for h in habitats}
relevant_regions = [
a[1] for a in species_regions.keys() + habitat_regions.keys()
]
bioreg_list = dal.get_biogeo_region_list(relevant_regions)
return {
'missing_species': species,
'species_regions': species_regions,
'species_list': species_list,
'habitat_list': habitat_list,
'missing_habitats': habitats,
'habitat_regions': habitat_regions,
'bioreg_list': bioreg_list,
}
def compare_datasets(dataset1, dataset2):
d1 = models.Dataset.query.get_or_404(dataset1)
d2 = models.Dataset.query.get_or_404(dataset2)
conclusions_s_d1 = d1.species_objs.filter_by(cons_role=ROLE_FINAL)
conclusions_s_d2 = d2.species_objs.filter_by(cons_role=ROLE_FINAL)
relevant_regions = set([r[0] for r in (
list(conclusions_s_d1.with_entities(models.DataSpeciesRegion.region)
.distinct()) +
list(conclusions_s_d2.with_entities(models.DataSpeciesRegion.region)
.distinct())
) if r[0]])
s_data = {}
for r in conclusions_s_d1:
if r.species not in s_data:
s_data[r.species] = {'d1': {}, 'd2': {}}
s_data[r.species]['d1'][r.region] = r
for r in conclusions_s_d2:
if r.species not in s_data:
s_data[r.species] = {'d1': {}, 'd2': {}}
s_data[r.species]['d2'][r.region] = r
if None in s_data:
del s_data[None]
s_stat = {'objs': 0, 'diff': 0}
for k, v in s_data.iteritems():
for reg, ass in v['d1'].iteritems():
ass2 = v['d2'].get(reg, None)
if (not ass2 or
ass2.conclusion_assessment != ass.conclusion_assessment):
s_stat['diff'] += 1
s_stat['objs'] += 1
conclusions_h_d1 = d1.habitat_objs.filter_by(cons_role=ROLE_FINAL)
conclusions_h_d2 = d2.habitat_objs.filter_by(cons_role=ROLE_FINAL)
h_data = {}
for r in conclusions_h_d1:
if r.habitat not in h_data:
h_data[r.habitat] = {'d1': {}, 'd2': {}}
h_data[r.habitat]['d1'][r.region] = r
for r in conclusions_h_d2:
if r.habitat not in h_data:
h_data[r.habitat] = {'d1': {}, 'd2': {}}
h_data[r.habitat]['d2'][r.region] = r
if None in h_data:
del h_data[None]
h_stat = {'objs': 0, 'diff': 0}
for k, v in h_data.iteritems():
for reg, ass in v['d1'].iteritems():
ass2 = v['d2'].get(reg, None)
if (not ass2 or
ass2.conclusion_assessment != ass.conclusion_assessment):
h_stat['diff'] += 1
h_stat['objs'] += 1
bioreg_list = dal.get_biogeo_region_list(relevant_regions)
return render_template(
'aggregation/compare_datasets.html',
species_data=s_data, dataset1=d1, dataset2=d2, bioreg_list=bioreg_list,
habitat_data=h_data, s_stat=s_stat, h_stat=h_stat,
page='compare',
)