def disease_model_show(request, id, format='html'):
dm = get_object_or_404(DiseaseModel, id=id)
if format == 'html':
return render_to_response('disease_model/show.html',
view_utils.template_params(dm, id_str=dm.get_asrf_id_str(), asrfs=dm.rates.all()))
# TODO: handle json & csv formats
if format in ['json', 'csv']:
if format == 'json':
data_str = json.dumps([[rf.id, rf.fit] for rf in dm.rates.all()])
elif format == 'csv':
headings = {}
rows = {}
data_str = ''
for rf in dm.rates.all():
headings[rf] = ['Age (years)', 'MAP Rate (per 1.0)']
rows[rf] = [[a, p] for a,p in zip(rf.fit['out_age_mesh'], rf.fit['map'])]
data_str += view_utils.csv_str(headings[rf], rows[rf])
return HttpResponse(data_str, view_utils.MIMETYPE[format])
else:
raise Http404
def population_plot(request, id, format):
pop = get_object_or_404(Population, pk=id)
M,C = pop.gaussian_process()
x = np.arange(0.,100.,1.)
# handle json & csv formats, which are not technically a plot
if format in ['json', 'csv']:
if format == 'json':
data_str = pop.data_json
elif format == 'csv':
headings = ['Age (years)', 'Population (thousands)']
rows = [[a, p] for a,p in zip(pop.data['mesh'], pop.data['vals'])]
data_str = view_utils.csv_str(headings, rows)
return HttpResponse(data_str, view_utils.MIMETYPE[format])
view_utils.clear_plot()
pl.plot(x,np.maximum(0.0,M(x)))
view_utils.label_plot("%s, %d, %s" % (pop.country, pop.year, pop.sex))
pl.ylabel('Population (thousands)')
return HttpResponse(view_utils.figure_data(format),
view_utils.MIMETYPE[format])
def age_specific_rate_function_show(request, id_str, format='html'):
asrfs = view_utils.id_str_to_objects(id_str, AgeSpecificRateFunction)
if format == 'html':
return render_to_response('age_specific_rate_function/show.html',
view_utils.template_params(asrfs[0], asrfs=asrfs, id_str=id_str, query_str=request.META['QUERY_STRING']))
# handle json & csv formats
if format in ['json', 'csv']:
if format == 'json':
data_str = json.dumps([[rf.id, rf.fit] for rf in asrfs])
elif format == 'csv':
headings = {}
rows = {}
data_str = ''
for rf in asrfs:
headings[rf] = ['Age (years)', 'MAP Rate (per 1.0)']
rows[rf] = [[a, p] for a,p in zip(rf.fit['out_age_mesh'], rf.fit['map'])]
data_str += view_utils.csv_str(headings[rf], rows[rf])
return HttpResponse(data_str, view_utils.MIMETYPE[format])
# handle graphics formats
cnt = asrfs.count()
cols = 2
rows = int(np.ceil(float(cnt) / float(cols)))
subplot_width = 6
subplot_height = 4
view_utils.clear_plot(width=subplot_width*cols,height=subplot_height*rows)
for ii, rf in enumerate(asrfs):
pl.subplot(rows,cols,ii+1)
if request.GET.get('bars'):
bars_mcmc_fit(rf)
#plot_map_fit(rf, alpha=.3)
plot_truth(rf)
else:
plot_intervals(rf, rf.rates.all(), fontsize=12, alpha=.5)
plot_intervals(rf, rf.rates.filter(params_json__contains='Rural'), fontsize=12, alpha=.5, color='brown')
#plot_normal_approx(rf)
plot_map_fit(rf)
plot_mcmc_fit(rf)
plot_truth(rf)
plot_prior(rf)
pl.text(0,0,rf.fit.get('priors',''), color='black', family='monospace', alpha=.75)
view_utils.label_plot('%s (id=%d)' % (rf, rf.id), fontsize=10)
max_rate = np.max([.0001] + [r.rate for r in rf.rates.all()] + rf.fit.get('mcmc_upper_cl', []))
xmin = float(request.GET.get('xmin', default=0.))
xmax = float(request.GET.get('xmax', default=100.))
ymin = float(request.GET.get('ymin', default=0.))
ymax = float(request.GET.get('ymax', default=1.25*max_rate))
pl.axis([xmin, xmax, ymin, ymax])
if ii % cols != 0:
pl.ylabel('')
#pl.yticks([])
if (ii + cols) < cnt:
pl.xlabel('')
#pl.xticks([])
return HttpResponse(view_utils.figure_data(format),
view_utils.MIMETYPE[format])