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])