def age_specific_rate_function_sparkplot(request, id_str, format='png'):
asrfs = view_utils.id_str_to_objects(id_str, AgeSpecificRateFunction)
width = 1
height = .5
fig = view_utils.clear_plot(width,height)
pl.subplot(1, 1, 1, frameon=False)
ax = None
for ii, rf in enumerate(asrfs):
plot_truth(rf)
plot_mcmc_fit(rf)
#plot_fit(rf, 'mcmc_mean', color='#000000')
#plot_map_fit(rf)
#plot_mcmc_fit(rf)
#plot_prior(rf)
pl.xticks([])
pl.yticks([])
#pl.delaxes()
pl.subplots_adjust(left=-.1, bottom=0, right=1, top=1.1,
wspace=0, hspace=0)
return HttpResponse(view_utils.figure_data(format),
view_utils.MIMETYPE[format])
def age_specific_rate_function_redirect(request, id_str, action):
asrfs = view_utils.id_str_to_objects(id_str, AgeSpecificRateFunction)
if action == 'edit':
url = '/admin/dismod3/agespecificratefunction/%d' % asrfs[0].id
elif action in view_utils.command_list['move']:
url = reverse('dismod3.views.age_specific_rate_function_show', args=(asrfs[0].id+view_utils.id_delta[action],))
elif action in view_utils.command_list['sex']:
url = AgeSpecificRateFunction.objects.filter(disease=asrfs[0].disease, region=asrfs[0].region, rate_type=asrfs[0].rate_type, sex=action)[0].get_absolute_url()
elif action in view_utils.command_list['format']:
url = '%s.%s' % (asrfs[0].get_absolute_url(), action)
else:
raise Http404
return HttpResponseRedirect(url)
def age_specific_rate_function_compare(request, id_str, format='html'):
"""
display information for comparing multiple age specific rate functions
id_str is a comma separate list of asrf ids, and format can be html
or a graphics format that is recognized by matplotlib
"""
asrfs = view_utils.id_str_to_objects(id_str, AgeSpecificRateFunction)
if format == 'html':
return render_to_response('age_specific_rate_function/compare.html', {'id_str': id_str, 'asrfs': asrfs})
size = request.GET.get('size', default='normal')
style = request.GET.get('style', default='overlay')
if size == 'small':
width = 3
height = 2
elif size == 'full_page':
width = 11
height = 8.5
else:
width = 6
height = 4
max_rate = .0001
view_utils.clear_plot(width=width,height=height)
try:
if style == 'overlay':
for ii, rf in enumerate(asrfs):
plot_fit(rf, 'map', alpha=.75, linewidth=5, label='asrf %d'%rf.id)
max_rate = np.max([max_rate] + rf.fit['map'])
pl.axis([0, 100, 0, 1.25*max_rate])
elif style == 'scatter':
x, y = [ [ asrfs[ii].fit[fit_type] for fit_type in ['mcmc_mean', 'mcmc_lower_cl', 'mcmc_upper_cl'] ] for ii in [0,1] ]
max_x = np.max(x[2])
max_y = np.max(y[2])
max_t = max(max_x, max_y, .00001)
xerr = np.abs(np.array(x[1:]) - np.array(x[0]))
yerr = np.abs(np.array(y[1:]) - np.array(y[0]))
pl.plot([probabilistic_utils.NEARLY_ZERO,1.], [probabilistic_utils.NEARLY_ZERO,1.], linestyle='dashed', linewidth=2, color='black', alpha=.75)
pl.errorbar(x=x[0], xerr=xerr, y=y[0], yerr=yerr, fmt='bo')
pl.axis([0,max_t,0,max_t])
elif style == 'stack':
n = asrfs.count()
max_t = probabilistic_utils.NEARLY_ZERO
for ii in range(n):
x = asrfs[ii].fit['mcmc_mean']
max_t = max(np.max(x), max_t)
pl.subplot(n, 1, ii+1, frameon=False)
pl.plot(x, linewidth=3)
if size != 'small':
pl.title(asrfs[ii])
pl.axis([0, 100, 0, max_t*1.1])
pl.xticks([])
pl.yticks([])
pl.subplots_adjust(left=0, right=1)
elif style == 'parallel':
for xx in zip(*[ rf.fit['mcmc_mean'] for rf in asrfs ]):
pl.plot(xx, linewidth=2, color='blue', alpha=.5)
xmin, xmax, ymin, ymax = pl.axis()
pl.vlines(range(len(asrfs)), ymin, ymax, color='black', linestyles='dashed',
alpha=.5, linewidth=2)
pl.xticks(range(len(asrfs)), [ 'asrf %d' % rf.id for rf in asrfs ])
except KeyError:
pass
#pl.figtext(0.4,0.2, 'No MCMC Fit Found')
if size == 'small':
pl.xticks([])
pl.yticks([])
else:
if style != 'stack' and style != 'parallel':
pl.legend()
view_utils.label_plot('Comparison of Age-Specific Rate Functions')
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])
