def preset(request, group, scale='linear'):
if int(group) == 0:
lis = [str(r) for r in get_good_mid()]
what = "municipality with persistent growth"
name = "persistent growth"
elif int(group) == 9:
lis = [str(r) for r in get_bad_mid()]
what = "municipality with persistent depopulation"
name = "persistent depopulation"
else:
lis = [str(m.mid) for m in Municipality.objects.filter(region__id=int(group))]
what = "municipality within the region {}".format(Regions.objects.get(id=int(group)).name)
name = Regions.objects.get(id=int(group)).name
args = ",".join(lis)
print(name)
return plot(request, args, scale=scale, what=what, name=name)
def sveito(request, mid):
try:
mun = Municipality.objects.get(mid=int(mid))
except Municipality.DoesNotExist:
raise Http404('Municipality does not exist')
#gets change table for a specific municipality
#recursion ho!
def getChanges(mun, fromYear=2015):
changes = []
for i in Changes.objects.filter(new=mun):
if i.year > fromYear: continue
#If it is not 100% then there was a split
if i.percent != 100:
total = False
for split in Changes.objects.filter(old=i.old,year=i.year).exclude(new=i.new):
changes.append((split.old.name,split.new.name,split.year))
if not total: changes.append((i.old.name,i.old.name,i.year))
changes.append((i.old.name,i.new.name,i.year))
if i.old.name != i.new.name: changes += getChanges(i.old,i.year)
#Handle the cases where something splits from our muni
changes = sorted(changes, key=lambda x:x[2])[::-1]
#This might be a bit dirty but the set conversion fixes it
splits = []
for i in changes:
for split in Changes.objects.filter(old=Municipality.objects.get(name=i[0])):
splits.append((split.old.name,split.new.name,split.year))
return changes+splits
#Double loops are in P
def niceChanges(mun):
changes = getChanges(mun)
changes.sort(key=lambda x:x[1])
dic = {}
for change in changes:
f, t, y = change
if y not in dic: dic[y] = [[],[]]
dic[y][0].append(f)
dic[y][1].append(t)
changes = []
for y in dic:
changes.append((y, ", ".join(list(set(dic[y][0]))), ", ".join(list(set(dic[y][1])))))
changes.sort(key=lambda x:x[0])
return changes[::-1]
# changes = getChanges(mun)
# years = set(map(lambda x:x[2], changes))
# return [[y for y in changes if y[2]==x] for x in years]
changes = niceChanges(mun)
#Year is permanently hardcoded
year = 2014
munipop = Population.objects.get(municipality=mun,year=year).val
mun = mun.name
#Create the data for population pyramids
gpop = []
gpop_obj = GenderPop.objects.filter(municipality__mid=int(mid),year=year)
for i in gpop_obj:
gpop.append([i.ageclass,i.valm,i.valf,i.year])
#Add the data from total iceland
gpop_all = []
gpop_obj = GenderPop.objects.filter(municipality__name="Alls",year=year)
for i in gpop_obj:
gpop_all.append([i.ageclass,i.valm,i.valf,i.year])
#Add the data for spending
reg_ind = -1
for i in Regions.objects.all():
if i.low <= int(mid) <= i.high:
reg = i.name
reg_ind = i
break
names = ['Alls','good','bad',mun,reg]
indnames = [(i,i) for i in names]
spending = []
toPlot = [SpendingPerCapita.objects.get(name=i) for i in names]
field_names = toPlot[0]._meta.get_all_field_names()
verbose_name = {
'culture' : 'culture',
'social' : 'social services',
'sports' : 'sports and youth activities'
}
def namefix(name):
if name == 'Alls': return 'Iceland'
if name == 'good': return 'Persistent growth'
if name == 'bad' : return 'Persistent depopulation'
return name
for field in field_names:
if field == 'name' or field == 'id' or field == 'income' or field == 'health': continue
s = [(verbose_name[field],'Sveito')]
for model in toPlot:
s.append((getattr(model,field)/1000,namefix(model.name)))
spending.append(s)
#print(GenderPop.objects.filter(municipality__mid=int(mid)).values('year').annotate(Sum('valm')))
def ratio(obj):
lis = []
res = obj.values('year').annotate(ratio=Sum(F('valm')))
for i,d in zip(range(len(res)), obj.values('year').annotate(total=Sum(F('valm')+F('valf')))):
if res[i]['year'] != d['year']:
print('Error: year are not the same')
if d['total']:
lis.append((d['year'], 100 * res[i]['ratio'] / d['total']))
return lis
good = get_good_mid()
bad = get_bad_mid()
lineplot=[]
tmp = GenderPop.objects.filter(year__gte=2000)
lineplot.append((namefix('Alls'),ratio(tmp.filter(municipality__name='Alls'))))
lineplot.append((namefix('good'),ratio(tmp.filter(municipality__mid__in=good))))
lineplot.append((namefix('bad'),ratio(tmp.filter(municipality__mid__in=bad))))
lineplot.append((mun,ratio(tmp.filter(municipality__mid=int(mid)))))
lineplot.append((reg,ratio(tmp.filter(municipality__region_id=reg_ind))))
#sorting for consistency
spending.sort(key= lambda x:x[1])
spending = spending[::-1]
template = loader.get_template("sveito/sveito.html")
context = RequestContext(request, {
'title' : mun,
'sveitoactive' : True,
'js' : ['lib/d3/d3.min.js', 'lib/nvd3/build/nv.d3.min.js'],
'css' : ['lib/nvd3/build/nv.d3.min.css'],
'region': reg,
'gpop' : gpop,
'allgpop' : gpop_all,
'spending' : spending,
'lineplot' : {"values": lineplot, "height":500, "opacity":0.6, "linewidth":"4px",
"valformat": "0.2f",
"y" : {
"name":"Percent of males",
"format": "d",
"force": "49,51,50,",
}
},
'munipop' : munipop,
'changes' : changes
},processors=[])
return HttpResponse(template.render(context))
