def get_exclusive_breastfeeding_data_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter(**filters).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level).annotate( children=Sum('ebf_in_month'), all=Sum('ebf_eligible'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset data_for_map, valid_total, in_month_total, average, total = generate_data_for_map( get_data_for(config), loc_level, 'children', 'all', 20, 60) fills = OrderedDict() fills.update({'0%-20%': MapColors.RED}) fills.update({'20%-60%': MapColors.ORANGE}) fills.update({'60%-100%': MapColors.PINK}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels( config) return { "slug": "severe", "label": "Percent Exclusive Breastfeeding{}".format(chosen_filters), "fills": fills, "rightLegend": { "average": average, "info": exclusive_breastfeeding_help_text(html=True), "extended_info": [{ 'indicator': 'Total number of children between ages 0 - 6 months{}:'.format( chosen_filters), 'value': indian_formatted_number(valid_total) }, { 'indicator': ('Total number of children (0-6 months) exclusively breastfed in the given month{}:' .format(chosen_filters)), 'value': indian_formatted_number(in_month_total) }, { 'indicator': '% children (0-6 months) exclusively breastfed in the ' 'given month{}:'.format(chosen_filters), 'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1)) }] }, "data": dict(data_for_map), }
def get_enrolled_children_data_map(domain, config, loc_level, show_test=False, icds_features_flag=False): config['month'] = datetime(*config['month']) def get_data_for(filters): queryset = AggChildHealthMonthly.objects.filter(**filters).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level).annotate( valid=Sum('valid_in_month'), all=Sum('valid_all_registered_in_month')).order_by( '%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset data_for_map = defaultdict(lambda: { 'valid': 0, 'all': 0, 'original_name': [], 'fillKey': 'Children' }) average = [] total_valid = 0 total = 0 location_launched_status = get_location_launched_status(config, loc_level) for row in get_data_for(config): if location_launched_status: launched_status = location_launched_status.get(row['%s_name' % loc_level]) if launched_status is None or launched_status <= 0: continue valid = row['valid'] or 0 name = row['%s_name' % loc_level] all_children = row['all'] or 0 on_map_name = row['%s_map_location_name' % loc_level] or name average.append(valid) total_valid += valid total += all_children data_for_map[on_map_name]['valid'] += valid data_for_map[on_map_name]['all'] += all_children data_for_map[on_map_name]['original_name'].append(name) fills = OrderedDict() fills.update({'Children': MapColors.BLUE}) fills.update({'Not Launched': MapColors.GREY}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval='0 - 6 years') return { "slug": "enrolled_children", "label": "", "fills": fills, "rightLegend": { "average": '%.2f' % (total_valid * 100 / float(total or 1)), "info": percent_children_enrolled_help_text(age_label=age_label), "extended_info": [{ 'indicator': 'Number of children{} who are enrolled for Anganwadi Services:' .format(chosen_filters), 'value': indian_formatted_number(total_valid) }, { 'indicator': ('Total number of children{} who are registered: '.format( chosen_filters)), 'value': indian_formatted_number(total) }, { 'indicator': ('Percentage of registered children{} who are enrolled for Anganwadi Services:' .format(chosen_filters)), 'value': '%.2f%%' % (total_valid * 100 / float(total or 1)) }] }, "data": dict(data_for_map), }
def get_prevalence_of_undernutrition_data_map(domain, config, loc_level, show_test=False, icds_features_flag=False): config['month'] = datetime(*config['month']) def get_data_for(filters): queryset = AggChildHealthMonthly.objects.filter(**filters).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level).annotate( moderately_underweight=Sum( 'nutrition_status_moderately_underweight'), severely_underweight=Sum( 'nutrition_status_severely_underweight'), normal=Sum('nutrition_status_normal'), weighed=Sum('nutrition_status_weighed'), total=Sum('wer_eligible'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) if 'age_tranche' not in config: queryset = queryset.filter(age_tranche__lt=72) return queryset data_for_map = defaultdict( lambda: { 'moderately_underweight': 0, 'severely_underweight': 0, 'normal': 0, 'weighed': 0, 'total': 0, 'original_name': [] }) moderately_underweight_total = 0 severely_underweight_total = 0 normal_total = 0 all_total = 0 weighed_total = 0 values_to_calculate_average = {'numerator': 0, 'denominator': 0} location_launched_status = get_location_launched_status(config, loc_level) for row in get_data_for(config): if location_launched_status: launched_status = location_launched_status.get(row['%s_name' % loc_level]) if launched_status is None or launched_status <= 0: continue weighed = row['weighed'] or 0 total = row['total'] or 0 name = row['%s_name' % loc_level] on_map_name = row['%s_map_location_name' % loc_level] or name severely_underweight = row['severely_underweight'] or 0 moderately_underweight = row['moderately_underweight'] or 0 normal = row['normal'] or 0 values_to_calculate_average[ 'numerator'] += moderately_underweight if moderately_underweight else 0 values_to_calculate_average[ 'numerator'] += severely_underweight if severely_underweight else 0 values_to_calculate_average['denominator'] += weighed if weighed else 0 moderately_underweight_total += moderately_underweight severely_underweight_total += severely_underweight normal_total += normal all_total += total weighed_total += weighed data_for_map[on_map_name][ 'severely_underweight'] += severely_underweight data_for_map[on_map_name][ 'moderately_underweight'] += moderately_underweight data_for_map[on_map_name]['normal'] += normal data_for_map[on_map_name]['total'] += total data_for_map[on_map_name]['weighed'] += weighed data_for_map[on_map_name]['original_name'].append(name) for data_for_location in data_for_map.values(): numerator = data_for_location[ 'moderately_underweight'] + data_for_location[ 'severely_underweight'] value = numerator * 100 / (data_for_location['weighed'] or 1) if value < 20: data_for_location.update({'fillKey': '0%-20%'}) elif 20 <= value < 35: data_for_location.update({'fillKey': '20%-35%'}) elif value >= 35: data_for_location.update({'fillKey': '35%-100%'}) fills = OrderedDict() fills.update({'0%-20%': MapColors.PINK}) fills.update({'20%-35%': MapColors.ORANGE}) fills.update({'35%-100%': MapColors.RED}) fills.update({'Not Launched': MapColors.GREY}) fills.update({'defaultFill': MapColors.GREY}) average = ((values_to_calculate_average['numerator'] * 100) / float(values_to_calculate_average['denominator'] or 1)) gender_label, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval='0 - 5 years') return { "slug": "moderately_underweight", "label": "Percent of Children{gender} Underweight ({age})".format( gender=gender_label, age=age_label), "fills": fills, "rightLegend": { "average": format_decimal(average), "info": underweight_children_help_text(age_label=age_label, html=True), "extended_info": [{ 'indicator': 'Total Children{} weighed in given month:'.format( chosen_filters), 'value': indian_formatted_number(weighed_total) }, { 'indicator': 'Number of children unweighed{}:'.format(chosen_filters), 'value': indian_formatted_number(all_total - weighed_total) }, { 'indicator': '% Severely Underweight{}:'.format(chosen_filters), 'value': '%.2f%%' % (severely_underweight_total * 100 / float(weighed_total or 1)) }, { 'indicator': '% Moderately Underweight{}:'.format(chosen_filters), 'value': '%.2f%%' % (moderately_underweight_total * 100 / float(weighed_total or 1)) }, { 'indicator': '% Normal{}:'.format(chosen_filters), 'value': '%.2f%%' % (normal_total * 100 / float(weighed_total or 1)) }] }, "data": dict(data_for_map) }
def get_newborn_with_low_birth_weight_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter(**filters).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level).annotate( low_birth=Sum('low_birth_weight_in_month'), in_month=Sum('born_in_month'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset data_for_map, in_month_total, low_birth_total, average = generate_data_for_map( get_data_for(config), loc_level, 'low_birth', 'in_month', 20, 60) fills = OrderedDict() fills.update({'0%-20%': MapColors.PINK}) fills.update({'20%-60%': MapColors.ORANGE}) fills.update({'60%-100%': MapColors.RED}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels( config) return { "slug": "low_birth", "label": "Percent Newborns with Low Birth Weight{}".format(chosen_filters), "fills": fills, "rightLegend": { "average": average, "info": _(("Percentage of newborns with born with birth weight less than 2500 grams." "<br/><br/>" "Newborns with Low Birth Weight are closely associated with foetal and neonatal " "mortality and morbidity, inhibited growth and cognitive development, and chronic " "diseases later in life")), "extended_info": [{ 'indicator': 'Total Number of Newborns born in given month{}:'.format( chosen_filters), 'value': indian_formatted_number(in_month_total) }, { 'indicator': 'Number of Newborns with LBW in given month{}:'.format( chosen_filters), 'value': indian_formatted_number(low_birth_total) }, { 'indicator': '% newborns with LBW in given month{}:'.format(chosen_filters), 'value': '%.2f%%' % (low_birth_total * 100 / float(in_month_total or 1)) }, { 'indicator': '% Unweighed{}:'.format(chosen_filters), 'value': '%.2f%%' % ((in_month_total - low_birth_total) * 100 / float(in_month_total or 1)) }] }, "data": dict(data_for_map), }
def get_early_initiation_breastfeeding_map(domain, config, loc_level, show_test=False, icds_features_flag=False): config['month'] = datetime(*config['month']) def get_data_for(filters): queryset = AggChildHealthMonthly.objects.filter( **filters ).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level ).annotate( birth=Sum('bf_at_birth'), in_month=Sum('born_in_month'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset if icds_features_flag: location_launched_status = get_location_launched_status(config, loc_level) else: location_launched_status = None data_for_map, in_month_total, birth_total, average, total = generate_data_for_map( get_data_for(config), loc_level, 'birth', 'in_month', 20, 60, location_launched_status=location_launched_status ) fills = OrderedDict() fills.update({'0%-20%': MapColors.RED}) fills.update({'20%-60%': MapColors.ORANGE}) fills.update({'60%-100%': MapColors.PINK}) if icds_features_flag: fills.update({'Not Launched': MapColors.GREY}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(config) return { "slug": "early_initiation", "label": "Percent Early Initiation of Breastfeeding{}".format(chosen_filters), "fills": fills, "rightLegend": { "average": average, "info": early_initiation_breastfeeding_help_text(html=True), "extended_info": [ { 'indicator': 'Total Number of Children born in the current month{}:'.format(chosen_filters), 'value': indian_formatted_number(in_month_total) }, { 'indicator': ( 'Total Number of Children who were put to the breast within one hour of birth{}:' .format(chosen_filters) ), 'value': indian_formatted_number(birth_total) }, { 'indicator': '% children who were put to the breast within one hour of ' 'birth{}:'.format(chosen_filters), 'value': '%.2f%%' % (birth_total * 100 / float(in_month_total or 1)) } ] }, "data": dict(data_for_map), }
def get_prevalence_of_stunting_data_map(domain, config, loc_level, show_test=False, icds_feature_flag=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter( **filters ).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level ).annotate( moderate=Sum(stunting_moderate_column(icds_feature_flag)), severe=Sum(stunting_severe_column(icds_feature_flag)), normal=Sum(stunting_normal_column(icds_feature_flag)), total=Sum('height_eligible'), total_measured=Sum(hfa_recorded_in_month_column(icds_feature_flag)), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) if 'age_tranche' not in config: queryset = queryset.exclude(age_tranche=72) return queryset data_for_map = defaultdict(lambda: { 'moderate': 0, 'severe': 0, 'normal': 0, 'total': 0, 'total_measured': 0, 'original_name': [] }) moderate_total = 0 severe_total = 0 normal_total = 0 all_total = 0 measured_total = 0 values_to_calculate_average = {'numerator': 0, 'denominator': 0} for row in get_data_for(config): total = row['total'] or 0 name = row['%s_name' % loc_level] on_map_name = row['%s_map_location_name' % loc_level] or name severe = row['severe'] or 0 moderate = row['moderate'] or 0 normal = row['normal'] or 0 total_measured = row['total_measured'] or 0 values_to_calculate_average['numerator'] += moderate if moderate else 0 values_to_calculate_average['numerator'] += severe if severe else 0 values_to_calculate_average['denominator'] += total_measured if total_measured else 0 severe_total += severe moderate_total += moderate normal_total += normal all_total += total measured_total += total_measured data_for_map[on_map_name]['severe'] += severe data_for_map[on_map_name]['moderate'] += moderate data_for_map[on_map_name]['normal'] += normal data_for_map[on_map_name]['total'] += total data_for_map[on_map_name]['total_measured'] += total_measured data_for_map[on_map_name]['original_name'].append(name) for data_for_location in six.itervalues(data_for_map): numerator = data_for_location['moderate'] + data_for_location['severe'] value = numerator * 100 / (data_for_location['total_measured'] or 1) if value < 25: data_for_location.update({'fillKey': '0%-25%'}) elif 25 <= value < 38: data_for_location.update({'fillKey': '25%-38%'}) elif value >= 38: data_for_location.update({'fillKey': '38%-100%'}) fills = OrderedDict() fills.update({'0%-25%': MapColors.PINK}) fills.update({'25%-38%': MapColors.ORANGE}) fills.update({'38%-100%': MapColors.RED}) fills.update({'defaultFill': MapColors.GREY}) gender_label, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval=default_age_interval(icds_feature_flag) ) average = ( (values_to_calculate_average['numerator'] * 100) / float(values_to_calculate_average['denominator'] or 1) ) return { "slug": "severe", "label": "Percent of Children{gender} Stunted ({age})".format( gender=gender_label, age=age_label ), "fills": fills, "rightLegend": { "average": "%.2f" % average, "info": _(( "Of the children enrolled for Anganwadi services, whose height was measured, the percentage of " "children between {} who were moderately/severely stunted in the current month. " "<br/><br/>" "Stunting is a sign of chronic undernutrition and has long lasting harmful consequences on " "the growth of a child".format(age_label) )), "extended_info": [ { 'indicator': 'Total Children{} eligible to have height measured:'.format(chosen_filters), 'value': indian_formatted_number(all_total) }, { 'indicator': 'Total Children{} with height measured in given month:' .format(chosen_filters), 'value': indian_formatted_number(measured_total) }, { 'indicator': 'Number of Children{} unmeasured:'.format(chosen_filters), 'value': indian_formatted_number(all_total - measured_total) }, { 'indicator': '% children{} with severely stunted growth:'.format(chosen_filters), 'value': '%.2f%%' % (severe_total * 100 / float(measured_total or 1)) }, { 'indicator': '% children{} with moderate stunted growth:'.format(chosen_filters), 'value': '%.2f%%' % (moderate_total * 100 / float(measured_total or 1)) }, { 'indicator': '% children{} with normal stunted growth:'.format(chosen_filters), 'value': '%.2f%%' % (normal_total * 100 / float(measured_total or 1)) } ] }, "data": dict(data_for_map), }
def get_prevalence_of_stunting_data_map(domain, config, loc_level, show_test=False, icds_feature_flag=False): config['month'] = datetime(*config['month']) def get_data_for(filters): queryset = AggChildHealthMonthly.objects.filter(**filters).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level).annotate( moderate=Sum(stunting_moderate_column(icds_feature_flag)), severe=Sum(stunting_severe_column(icds_feature_flag)), normal=Sum(stunting_normal_column(icds_feature_flag)), total=Sum('height_eligible'), total_measured=Sum( hfa_recorded_in_month_column(icds_feature_flag)), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) if 'age_tranche' not in config: queryset = queryset.filter(age_tranche__lt=72) return queryset data_for_map = defaultdict( lambda: { 'moderate': 0, 'severe': 0, 'normal': 0, 'total': 0, 'total_measured': 0, 'original_name': [] }) moderate_total = 0 severe_total = 0 normal_total = 0 all_total = 0 measured_total = 0 values_to_calculate_average = {'numerator': 0, 'denominator': 0} if icds_feature_flag: location_launched_status = get_location_launched_status( config, loc_level) else: location_launched_status = None for row in get_data_for(config): if location_launched_status: launched_status = location_launched_status.get(row['%s_name' % loc_level]) if launched_status is None or launched_status <= 0: continue total = row['total'] or 0 name = row['%s_name' % loc_level] on_map_name = row['%s_map_location_name' % loc_level] or name severe = row['severe'] or 0 moderate = row['moderate'] or 0 normal = row['normal'] or 0 total_measured = row['total_measured'] or 0 values_to_calculate_average['numerator'] += moderate if moderate else 0 values_to_calculate_average['numerator'] += severe if severe else 0 values_to_calculate_average[ 'denominator'] += total_measured if total_measured else 0 severe_total += severe moderate_total += moderate normal_total += normal all_total += total measured_total += total_measured data_for_map[on_map_name]['severe'] += severe data_for_map[on_map_name]['moderate'] += moderate data_for_map[on_map_name]['normal'] += normal data_for_map[on_map_name]['total'] += total data_for_map[on_map_name]['total_measured'] += total_measured data_for_map[on_map_name]['original_name'].append(name) for data_for_location in data_for_map.values(): numerator = data_for_location['moderate'] + data_for_location['severe'] value = numerator * 100 / (data_for_location['total_measured'] or 1) if value < 25: data_for_location.update({'fillKey': '0%-25%'}) elif 25 <= value < 38: data_for_location.update({'fillKey': '25%-38%'}) elif value >= 38: data_for_location.update({'fillKey': '38%-100%'}) fills = OrderedDict() fills.update({'0%-25%': MapColors.PINK}) fills.update({'25%-38%': MapColors.ORANGE}) fills.update({'38%-100%': MapColors.RED}) fills.update({'defaultFill': MapColors.GREY}) gender_label, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval=default_age_interval(icds_feature_flag)) average = ((values_to_calculate_average['numerator'] * 100) / float(values_to_calculate_average['denominator'] or 1)) return { "slug": "severe", "label": "Percent of Children{gender} Stunted ({age})".format( gender=gender_label, age=age_label), "fills": fills, "rightLegend": { "average": "%.2f" % average, "info": _(("Of the children enrolled for Anganwadi services, whose height was measured, the percentage of " "children between {} who were moderately/severely stunted in the current month. " "<br/><br/>" "Stunting is a sign of chronic undernutrition and has long lasting harmful consequences on " "the growth of a child".format(age_label))), "extended_info": [{ 'indicator': 'Total Children{} eligible to have height measured:'.format( chosen_filters), 'value': indian_formatted_number(all_total) }, { 'indicator': 'Total Children{} with height measured in given month:'.format( chosen_filters), 'value': indian_formatted_number(measured_total) }, { 'indicator': 'Number of Children{} unmeasured:'.format(chosen_filters), 'value': indian_formatted_number(all_total - measured_total) }, { 'indicator': '% children{} with severely stunted growth:'.format( chosen_filters), 'value': '%.2f%%' % (severe_total * 100 / float(measured_total or 1)) }, { 'indicator': '% children{} with moderate stunted growth:'.format( chosen_filters), 'value': '%.2f%%' % (moderate_total * 100 / float(measured_total or 1)) }, { 'indicator': '% children{} with normal stunted growth:'.format( chosen_filters), 'value': '%.2f%%' % (normal_total * 100 / float(measured_total or 1)) }] }, "data": dict(data_for_map), }
def get_prevalence_of_undernutrition_data_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter( **filters ).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level ).annotate( moderately_underweight=Sum('nutrition_status_moderately_underweight'), severely_underweight=Sum('nutrition_status_severely_underweight'), normal=Sum('nutrition_status_normal'), weighed=Sum('nutrition_status_weighed'), total=Sum('wer_eligible'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) if 'age_tranche' not in config: queryset = queryset.exclude(age_tranche=72) return queryset data_for_map = defaultdict(lambda: { 'moderately_underweight': 0, 'severely_underweight': 0, 'normal': 0, 'weighed': 0, 'total': 0, 'original_name': [] }) moderately_underweight_total = 0 severely_underweight_total = 0 normal_total = 0 all_total = 0 weighed_total = 0 values_to_calculate_average = {'numerator': 0, 'denominator': 0} for row in get_data_for(config): weighed = row['weighed'] or 0 total = row['total'] or 0 name = row['%s_name' % loc_level] on_map_name = row['%s_map_location_name' % loc_level] or name severely_underweight = row['severely_underweight'] or 0 moderately_underweight = row['moderately_underweight'] or 0 normal = row['normal'] or 0 values_to_calculate_average['numerator'] += moderately_underweight if moderately_underweight else 0 values_to_calculate_average['numerator'] += severely_underweight if severely_underweight else 0 values_to_calculate_average['denominator'] += weighed if weighed else 0 moderately_underweight_total += moderately_underweight severely_underweight_total += severely_underweight normal_total += normal all_total += total weighed_total += weighed data_for_map[on_map_name]['severely_underweight'] += severely_underweight data_for_map[on_map_name]['moderately_underweight'] += moderately_underweight data_for_map[on_map_name]['normal'] += normal data_for_map[on_map_name]['total'] += total data_for_map[on_map_name]['weighed'] += weighed data_for_map[on_map_name]['original_name'].append(name) for data_for_location in six.itervalues(data_for_map): numerator = data_for_location['moderately_underweight'] + data_for_location['severely_underweight'] value = numerator * 100 / (data_for_location['weighed'] or 1) if value < 20: data_for_location.update({'fillKey': '0%-20%'}) elif 20 <= value < 35: data_for_location.update({'fillKey': '20%-35%'}) elif value >= 35: data_for_location.update({'fillKey': '35%-100%'}) fills = OrderedDict() fills.update({'0%-20%': MapColors.PINK}) fills.update({'20%-35%': MapColors.ORANGE}) fills.update({'35%-100%': MapColors.RED}) fills.update({'defaultFill': MapColors.GREY}) average = ( (values_to_calculate_average['numerator'] * 100) / float(values_to_calculate_average['denominator'] or 1) ) gender_label, age_label, chosen_filters = chosen_filters_to_labels(config, default_interval='0 - 5 years') return { "slug": "moderately_underweight", "label": "Percent of Children{gender} Underweight ({age})".format( gender=gender_label, age=age_label ), "fills": fills, "rightLegend": { "average": format_decimal(average), "info": underweight_children_help_text(age_label=age_label, html=True), "extended_info": [ { 'indicator': 'Total Children{} weighed in given month:'.format(chosen_filters), 'value': indian_formatted_number(weighed_total) }, { 'indicator': 'Number of children unweighed{}:'.format(chosen_filters), 'value': indian_formatted_number(all_total - weighed_total) }, { 'indicator': '% Severely Underweight{}:'.format(chosen_filters), 'value': '%.2f%%' % (severely_underweight_total * 100 / float(weighed_total or 1)) }, { 'indicator': '% Moderately Underweight{}:'.format(chosen_filters), 'value': '%.2f%%' % (moderately_underweight_total * 100 / float(weighed_total or 1)) }, { 'indicator': '% Normal{}:'.format(chosen_filters), 'value': '%.2f%%' % (normal_total * 100 / float(weighed_total or 1)) } ] }, "data": dict(data_for_map) }
def get_awc_reports_maternal_child(domain, config, month, prev_month, show_test=False, icds_feature_flag=False): def get_data_for(date): age_filters = { 'age_tranche': 72 } if icds_feature_flag else { 'age_tranche__in': [0, 6, 72] } moderately_underweight = exclude_records_by_age_for_column( {'age_tranche': 72}, 'nutrition_status_moderately_underweight') severely_underweight = exclude_records_by_age_for_column( {'age_tranche': 72}, 'nutrition_status_severely_underweight') wasting_moderate = exclude_records_by_age_for_column( age_filters, wasting_moderate_column(icds_feature_flag)) wasting_severe = exclude_records_by_age_for_column( age_filters, wasting_severe_column(icds_feature_flag)) stunting_moderate = exclude_records_by_age_for_column( age_filters, stunting_moderate_column(icds_feature_flag)) stunting_severe = exclude_records_by_age_for_column( age_filters, stunting_severe_column(icds_feature_flag)) nutrition_status_weighed = exclude_records_by_age_for_column( {'age_tranche': 72}, 'nutrition_status_weighed') height_measured_in_month = exclude_records_by_age_for_column( age_filters, hfa_recorded_in_month_column(icds_feature_flag)) weighed_and_height_measured_in_month = exclude_records_by_age_for_column( age_filters, wfh_recorded_in_month_column(icds_feature_flag)) queryset = AggChildHealthMonthly.objects.filter( month=date, **config).values('month', 'aggregation_level').annotate( underweight=(Sum(moderately_underweight) + Sum(severely_underweight)), valid_weighed=Sum(nutrition_status_weighed), immunized=(Sum('fully_immunized_on_time') + Sum('fully_immunized_late')), eligible=Sum('fully_immunized_eligible'), wasting=Sum(wasting_moderate) + Sum(wasting_severe), height_measured_in_month=Sum(height_measured_in_month), weighed_and_height_measured_in_month=Sum( weighed_and_height_measured_in_month), stunting=Sum(stunting_moderate) + Sum(stunting_severe), low_birth=Sum('low_birth_weight_in_month'), birth=Sum('bf_at_birth'), born=Sum('born_in_month'), weighed_and_born_in_month=Sum('weighed_and_born_in_month'), month_ebf=Sum('ebf_in_month'), ebf=Sum('ebf_eligible'), month_cf=Sum('cf_initiation_in_month'), cf=Sum('cf_initiation_eligible')) if not show_test: queryset = apply_exclude(domain, queryset) return queryset def get_weight_efficiency(date): queryset = AggAwcMonthly.objects.filter(month=date, **config).values( 'month', 'aggregation_level', 'awc_name').annotate(wer_weight=Sum('wer_weighed'), wer_eli=Sum('wer_eligible')) if not show_test: queryset = apply_exclude(domain, queryset) return queryset def get_institutional_delivery_data(date): queryset = AggCcsRecordMonthly.objects.filter( month=date, **config).values( 'month', 'aggregation_level', 'awc_name').annotate( institutional_delivery_in_month_sum=Sum( 'institutional_delivery_in_month'), delivered_in_month_sum=Sum('delivered_in_month')) if not show_test: queryset = apply_exclude(domain, queryset) return queryset this_month_data = get_data_for(datetime(*month)) prev_month_data = get_data_for(datetime(*prev_month)) this_month_data_we = get_weight_efficiency(datetime(*month)) prev_month_data_we = get_weight_efficiency(datetime(*prev_month)) this_month_institutional_delivery_data = get_institutional_delivery_data( datetime(*month)) prev_month_institutional_delivery_data = get_institutional_delivery_data( datetime(*prev_month)) gender_label, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval=default_age_interval(icds_feature_flag)) return { 'kpi': [[ { 'label': _('Underweight (Weight-for-Age)'), 'help_text': _(("Of the total children weighed, the percentage of children between 0-5 years who were " "moderately/severely underweight in the current month. Children who are moderately or " "severely underweight have a higher risk of mortality. ")), 'percent': percent_diff('underweight', this_month_data, prev_month_data, 'valid_weighed'), 'color': 'red' if percent_diff('underweight', this_month_data, prev_month_data, 'valid_weighed') > 0 else 'green', 'value': get_value(this_month_data, 'underweight'), 'all': get_value(this_month_data, 'valid_weighed'), 'format': 'percent_and_div', 'frequency': 'month' }, { 'label': _('Wasting (Weight-for-Height)'), 'help_text': wasting_help_text(age_label), 'percent': percent_diff('wasting', this_month_data, prev_month_data, 'weighed_and_height_measured_in_month'), 'color': 'red' if percent_diff('wasting', this_month_data, prev_month_data, 'weighed_and_height_measured_in_month') > 0 else 'green', 'value': get_value(this_month_data, 'wasting'), 'all': get_value(this_month_data, 'weighed_and_height_measured_in_month'), 'format': 'percent_and_div', 'frequency': 'month' }, ], [ { 'label': _('Stunting (Height-for-Age)'), 'help_text': stunting_help_text(age_label), 'percent': percent_diff('stunting', this_month_data, prev_month_data, 'height_measured_in_month'), 'color': 'red' if percent_diff('stunting', this_month_data, prev_month_data, 'height_measured_in_month') > 0 else 'green', 'value': get_value(this_month_data, 'stunting'), 'all': get_value(this_month_data, 'height_measured_in_month'), 'format': 'percent_and_div', 'frequency': 'month' }, { 'label': _('Weighing Efficiency'), 'help_text': _("Of the children between the ages of 0-5 years who are enrolled for Anganwadi Services, " "the percentage who were weighed in the given month. "), 'percent': percent_diff('wer_weight', this_month_data_we, prev_month_data_we, 'wer_eli'), 'color': 'green' if percent_diff('wer_weight', this_month_data_we, prev_month_data_we, 'wer_eli') > 0 else 'red', 'value': get_value(this_month_data_we, 'wer_weight'), 'all': get_value(this_month_data_we, 'wer_eli'), 'format': 'percent_and_div', 'frequency': 'month' }, ], [ { 'label': _('Newborns with Low Birth Weight'), 'help_text': _("Of all the children born in the current month, the percentage that had a birth weight " "less than 2500 grams. Newborns with Low Birth Weight are closely associated wtih foetal " "and neonatal mortality and morbidity, inhibited growth and cognitive development, " "and chronic diseases later in life."), 'percent': percent_diff('low_birth', this_month_data, prev_month_data, 'weighed_and_born_in_month'), 'color': 'red' if percent_diff('low_birth', this_month_data, prev_month_data, 'weighed_and_born_in_month') > 0 else 'green', 'value': get_value(this_month_data, 'low_birth'), 'all': get_value(this_month_data, 'weighed_and_born_in_month'), 'format': 'percent_and_div', 'frequency': 'month' }, { 'label': _('Early Initiation of Breastfeeding'), 'help_text': _("Of the children born in the last month, the percentage whose " "breastfeeding was initiated within 1 hour of delivery. Early initiation " "of breastfeeding ensure the newborn recieves the \"first milk\" rich " "in nutrients and encourages exclusive breastfeeding practice" ), 'percent': percent_diff('birth', this_month_data, prev_month_data, 'born'), 'color': 'green' if percent_diff('birth', this_month_data, prev_month_data, 'born') > 0 else 'red', 'value': get_value(this_month_data, 'birth'), 'all': get_value(this_month_data, 'born'), 'format': 'percent_and_div', 'frequency': 'month' }, ], [ { 'label': _('Exclusive breastfeeding'), 'help_text': _("Of the total children between the ages of 0 to 6 months, the percentage that was " "exclusively fed with breast milk. An infant is exclusively breastfed if they receive " "only breastmilk with no additional food or liquids (even water), ensuring optimal " "nutrition and growth between 0 - 6 months"), 'percent': percent_diff('month_ebf', this_month_data, prev_month_data, 'ebf'), 'color': 'green' if percent_diff('month_ebf', this_month_data, prev_month_data, 'ebf') > 0 else 'red', 'value': get_value(this_month_data, 'month_ebf'), 'all': get_value(this_month_data, 'ebf'), 'format': 'percent_and_div', 'frequency': 'month' }, { 'label': _('Children initiated appropriate Complementary Feeding'), 'help_text': _("Of the total children between the ages of 6 to 8 months, the percentage that was " "given a timely introduction to solid, semi-solid or soft food. Timely intiation of " "complementary feeding in addition to breastmilk at 6 months of age is a key feeding " "practice to reduce malnutrition"), 'percent': percent_diff('month_cf', this_month_data, prev_month_data, 'cf'), 'color': 'green' if percent_diff('month_cf', this_month_data, prev_month_data, 'cf') > 0 else 'red', 'value': get_value(this_month_data, 'month_cf'), 'all': get_value(this_month_data, 'cf'), 'format': 'percent_and_div', 'frequency': 'month' }, ], [ { 'label': _('Immunization Coverage (at age 1 year)'), 'help_text': _(("Of the total number of children enrolled for Anganwadi Services who are over a year old, " "the percentage of children who have received the complete immunization as per the " "National Immunization Schedule of India that is required by age 1." "<br/><br/> " "This includes the following immunizations:<br/> " "If Pentavalent path: Penta1/2/3, OPV1/2/3, BCG, Measles, VitA1<br/> " "If DPT/HepB path: DPT1/2/3, HepB1/2/3, OPV1/2/3, BCG, Measles, VitA1" )), 'percent': percent_diff('immunized', this_month_data, prev_month_data, 'eligible'), 'color': 'green' if percent_diff('immunized', this_month_data, prev_month_data, 'eligible') > 0 else 'red', 'value': get_value(this_month_data, 'immunized'), 'all': get_value(this_month_data, 'eligible'), 'format': 'percent_and_div', 'frequency': 'month' }, { 'label': _('Institutional Deliveries'), 'help_text': _(("Of the total number of women who gave birth in the last month, the percentage who " "delivered in a public or private medical facility. Delivery in medical instituitions " "is associated with a decrease in maternal mortality rate" )), 'percent': percent_diff('institutional_delivery_in_month_sum', this_month_institutional_delivery_data, prev_month_institutional_delivery_data, 'delivered_in_month_sum'), 'color': 'green' if percent_diff('institutional_delivery_in_month_sum', this_month_institutional_delivery_data, prev_month_institutional_delivery_data, 'delivered_in_month_sum') > 0 else 'red', 'value': get_value(this_month_institutional_delivery_data, 'institutional_delivery_in_month_sum'), 'all': get_value(this_month_institutional_delivery_data, 'delivered_in_month_sum'), 'format': 'percent_and_div', 'frequency': 'month' }, ]] }
def get_maternal_child_data(domain, config, show_test=False, icds_feature_flag=False): def get_data_for_child_health_monthly(date, filters): age_filters = {'age_tranche': 72} if icds_feature_flag else {'age_tranche__in': [0, 6, 72]} moderately_underweight = exclude_records_by_age_for_column( {'age_tranche': 72}, 'nutrition_status_moderately_underweight' ) severely_underweight = exclude_records_by_age_for_column( {'age_tranche': 72}, 'nutrition_status_severely_underweight' ) wasting_moderate = exclude_records_by_age_for_column( age_filters, wasting_moderate_column(icds_feature_flag) ) wasting_severe = exclude_records_by_age_for_column( age_filters, wasting_severe_column(icds_feature_flag) ) stunting_moderate = exclude_records_by_age_for_column( age_filters, stunting_moderate_column(icds_feature_flag) ) stunting_severe = exclude_records_by_age_for_column( age_filters, stunting_severe_column(icds_feature_flag) ) nutrition_status_weighed = exclude_records_by_age_for_column( {'age_tranche': 72}, 'nutrition_status_weighed' ) height_measured_in_month = exclude_records_by_age_for_column( age_filters, hfa_recorded_in_month_column(icds_feature_flag) ) weighed_and_height_measured_in_month = exclude_records_by_age_for_column( age_filters, wfh_recorded_in_month_column(icds_feature_flag) ) queryset = AggChildHealthMonthly.objects.filter( month=date, **filters ).values( 'aggregation_level' ).annotate( underweight=( Sum(moderately_underweight) + Sum(severely_underweight) ), valid=Sum(nutrition_status_weighed), wasting=Sum(wasting_moderate) + Sum(wasting_severe), stunting=Sum(stunting_moderate) + Sum(stunting_severe), height_measured_in_month=Sum(height_measured_in_month), weighed_and_height_measured_in_month=Sum(weighed_and_height_measured_in_month), low_birth_weight=Sum('low_birth_weight_in_month'), bf_birth=Sum('bf_at_birth'), born=Sum('born_in_month'), weighed_and_born_in_month=Sum('weighed_and_born_in_month'), ebf=Sum('ebf_in_month'), ebf_eli=Sum('ebf_eligible'), cf_initiation=Sum('cf_initiation_in_month'), cf_initiation_eli=Sum('cf_initiation_eligible') ) if not show_test: queryset = apply_exclude(domain, queryset) return queryset def get_data_for_deliveries(date, filters): queryset = AggCcsRecordMonthly.objects.filter( month=date, **filters ).values( 'aggregation_level' ).annotate( institutional_delivery=Sum('institutional_delivery_in_month'), delivered=Sum('delivered_in_month') ) if not show_test: queryset = apply_exclude(domain, queryset) return queryset current_month = datetime(*config['month']) previous_month = datetime(*config['prev_month']) del config['month'] del config['prev_month'] this_month_data = get_data_for_child_health_monthly(current_month, config) prev_month_data = get_data_for_child_health_monthly(previous_month, config) deliveries_this_month = get_data_for_deliveries(current_month, config) deliveries_prev_month = get_data_for_deliveries(previous_month, config) gender_label, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval=default_age_interval(icds_feature_flag) ) return { 'records': [ [ { 'label': _('Underweight (Weight-for-Age)'), 'help_text': _(( "Of the total children enrolled for Anganwadi services and weighed, the percentage " "of children between 0-5 years who were moderately/severely underweight in the current " "month. Children who are moderately or severely underweight have a higher risk " "of mortality. " )), 'percent': percent_diff( 'underweight', this_month_data, prev_month_data, 'valid' ), 'color': 'red' if percent_diff( 'underweight', this_month_data, prev_month_data, 'valid' ) > 0 else 'green', 'value': get_value(this_month_data, 'underweight'), 'all': get_value(this_month_data, 'valid'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'underweight_children' }, { 'label': _('Wasting (Weight-for-Height)'), 'help_text': _(wasting_help_text(age_label)), 'percent': percent_diff( 'wasting', this_month_data, prev_month_data, 'weighed_and_height_measured_in_month' ), 'color': 'red' if percent_diff( 'wasting', this_month_data, prev_month_data, 'weighed_and_height_measured_in_month' ) > 0 else 'green', 'value': get_value(this_month_data, 'wasting'), 'all': get_value(this_month_data, 'weighed_and_height_measured_in_month'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'wasting' } ], [ { 'label': _('Stunting (Height-for-Age)'), 'help_text': _(stunting_help_text(age_label)), 'percent': percent_diff( 'stunting', this_month_data, prev_month_data, 'height_measured_in_month' ), 'color': 'red' if percent_diff( 'stunting', this_month_data, prev_month_data, 'height_measured_in_month' ) > 0 else 'green', 'value': get_value(this_month_data, 'stunting'), 'all': get_value(this_month_data, 'height_measured_in_month'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'stunting' }, { 'label': _('Newborns with Low Birth Weight'), 'help_text': _(( "Of all the children born in the current month and enrolled for Anganwadi services, " "the percentage that had a birth weight less than 2500 grams. Newborns with Low Birth " "Weight are closely associated wtih foetal and neonatal mortality and morbidity, " "inhibited growth and cognitive development, and chronic diseases later in life. ")), 'percent': percent_diff( 'low_birth_weight', this_month_data, prev_month_data, 'weighed_and_born_in_month' ), 'color': 'red' if percent_diff( 'low_birth_weight', this_month_data, prev_month_data, 'weighed_and_born_in_month' ) > 0 else 'green', 'value': get_value(this_month_data, 'low_birth_weight'), 'all': get_value(this_month_data, 'weighed_and_born_in_month'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'low_birth' } ], [ { 'label': _('Early Initiation of Breastfeeding'), 'help_text': _(( "Of the children born in the last month and enrolled for Anganwadi services, " "the percentage whose breastfeeding was initiated within 1 hour of delivery. " "Early initiation of breastfeeding ensure the newborn recieves the \"first milk\" " "rich in nutrients and encourages exclusive breastfeeding practice") ), 'percent': percent_diff( 'bf_birth', this_month_data, prev_month_data, 'born' ), 'color': 'green' if percent_diff( 'bf_birth', this_month_data, prev_month_data, 'born' ) > 0 else 'red', 'value': get_value(this_month_data, 'bf_birth'), 'all': get_value(this_month_data, 'born'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'early_initiation' }, { 'label': _('Exclusive Breastfeeding'), 'help_text': _(( "Of the total children enrolled for Anganwadi services between the ages of 0 to 6 months, " "the percentage that was exclusively fed with breast milk. An infant is exclusively " "breastfed if they receive only breastmilk with no additional food or liquids " "(even water), ensuring optimal nutrition and growth between 0 - 6 months") ), 'percent': percent_diff( 'ebf', this_month_data, prev_month_data, 'ebf_eli' ), 'color': 'green' if percent_diff( 'ebf', this_month_data, prev_month_data, 'ebf_eli' ) > 0 else 'red', 'value': get_value(this_month_data, 'ebf'), 'all': get_value(this_month_data, 'ebf_eli'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'exclusive_breastfeeding' } ], [ { 'label': _('Children initiated appropriate Complementary Feeding'), 'help_text': _(( "Of the total children enrolled for Anganwadi services between the ages of 6 to 8 months, " "the percentage that was given a timely introduction to solid, semi-solid or soft food. " "Timely intiation of complementary feeding in addition to breastmilk at 6 months of age " "is a key feeding practice to reduce malnutrition") ), 'percent': percent_diff( 'cf_initiation', this_month_data, prev_month_data, 'cf_initiation_eli' ), 'color': 'green' if percent_diff( 'cf_initiation', this_month_data, prev_month_data, 'cf_initiation_eli' ) > 0 else 'red', 'value': get_value(this_month_data, 'cf_initiation'), 'all': get_value(this_month_data, 'cf_initiation_eli'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'children_initiated' }, { 'label': _('Institutional Deliveries'), 'help_text': _(( "Of the total number of women enrolled for Anganwadi services who gave birth in the last " "month, the percentage who delivered in a public or private medical facility. Delivery " "in medical instituitions is associated with a decrease in maternal mortality rate") ), 'percent': percent_diff( 'institutional_delivery', deliveries_this_month, deliveries_prev_month, 'delivered' ), 'color': 'green' if percent_diff( 'institutional_delivery', deliveries_this_month, deliveries_prev_month, 'delivered' ) > 0 else 'red', 'value': get_value(deliveries_this_month, 'institutional_delivery'), 'all': get_value(deliveries_this_month, 'delivered'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'institutional_deliveries' } ] ] }
def get_prevalence_of_severe_data_map(domain, config, loc_level, show_test=False, icds_feature_flag=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter(**filters).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level).annotate( moderate=Sum(wasting_moderate_column(icds_feature_flag)), severe=Sum(wasting_severe_column(icds_feature_flag)), normal=Sum(wasting_normal_column(icds_feature_flag)), total_height_eligible=Sum('height_eligible'), total_weighed=Sum('nutrition_status_weighed'), total_measured=Sum( wfh_recorded_in_month_column(icds_feature_flag)), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) if 'age_tranche' not in config: if icds_feature_flag: queryset = queryset.exclude(age_tranche=72) else: queryset = queryset.exclude(age_tranche__in=[0, 6, 72]) return queryset data_for_map = defaultdict( lambda: { 'moderate': 0, 'severe': 0, 'normal': 0, 'total_weighed': 0, 'total_measured': 0, 'total_height_eligible': 0, 'original_name': [] }) severe_for_all_locations = 0 moderate_for_all_locations = 0 normal_for_all_locations = 0 weighed_for_all_locations = 0 measured_for_all_locations = 0 height_eligible_for_all_locations = 0 values_to_calculate_average = [] for row in get_data_for(config): total_weighed = row['total_weighed'] or 0 total_height_eligible = row['total_height_eligible'] or 0 name = row['%s_name' % loc_level] on_map_name = row['%s_map_location_name' % loc_level] or name severe = row['severe'] or 0 moderate = row['moderate'] or 0 normal = row['normal'] or 0 total_measured = row['total_measured'] or 0 numerator = moderate + severe values_to_calculate_average.append(numerator * 100 / (total_weighed or 1)) severe_for_all_locations += severe moderate_for_all_locations += moderate normal_for_all_locations += normal weighed_for_all_locations += total_weighed measured_for_all_locations += total_measured height_eligible_for_all_locations += total_height_eligible data_for_map[on_map_name]['severe'] += severe data_for_map[on_map_name]['moderate'] += moderate data_for_map[on_map_name]['normal'] += normal data_for_map[on_map_name]['total_weighed'] += total_weighed data_for_map[on_map_name]['total_measured'] += total_measured data_for_map[on_map_name][ 'total_height_eligible'] += total_height_eligible data_for_map[on_map_name]['original_name'].append(name) for data_for_location in six.itervalues(data_for_map): numerator = data_for_location['moderate'] + data_for_location['severe'] value = numerator * 100 / (data_for_location['total_measured'] or 1) if value < 5: data_for_location.update({'fillKey': '0%-5%'}) elif 5 <= value <= 7: data_for_location.update({'fillKey': '5%-7%'}) elif value > 7: data_for_location.update({'fillKey': '7%-100%'}) fills = OrderedDict() fills.update({'0%-5%': MapColors.PINK}) fills.update({'5%-7%': MapColors.ORANGE}) fills.update({'7%-100%': MapColors.RED}) fills.update({'defaultFill': MapColors.GREY}) gender_label, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval=default_age_interval(icds_feature_flag)) return { "slug": "severe", "label": "Percent of Children{gender} Wasted ({age})".format( gender=gender_label, age=age_label), "fills": fills, "rightLegend": { "average": "%.2f" % ((sum(values_to_calculate_average)) / float(len(values_to_calculate_average) or 1)), "info": _(("Of the children enrolled for Anganwadi services, whose weight and height was measured, " "the percentage of children between {} who were moderately/severely wasted in the current month. " "<br/><br/>" "Severe Acute Malnutrition (SAM) or wasting in children is a symptom of acute undernutrition " "usually as a consequence of insufficient food intake or a high incidence of infectious diseases." .format(age_label))), "extended_info": [{ 'indicator': 'Total Children{} weighed in given month:'.format( chosen_filters), 'value': indian_formatted_number(weighed_for_all_locations) }, { 'indicator': 'Total Children{} with height measured in given month:'.format( chosen_filters), 'value': indian_formatted_number(measured_for_all_locations) }, { 'indicator': 'Number of children{} unmeasured:'.format(chosen_filters), 'value': indian_formatted_number(height_eligible_for_all_locations - weighed_for_all_locations) }, { 'indicator': '% Severely Acute Malnutrition{}:'.format(chosen_filters), 'value': '%.2f%%' % (severe_for_all_locations * 100 / float(measured_for_all_locations or 1)) }, { 'indicator': '% Moderately Acute Malnutrition{}:'.format(chosen_filters), 'value': '%.2f%%' % (moderate_for_all_locations * 100 / float(measured_for_all_locations or 1)) }, { 'indicator': '% Normal{}:'.format(chosen_filters), 'value': '%.2f%%' % (normal_for_all_locations * 100 / float(measured_for_all_locations or 1)) }] }, "data": dict(data_for_map), }
def get_maternal_child_data(domain, config, show_test=False, icds_feature_flag=False): def get_data_for_child_health_monthly(date, filters): age_filters = {'age_tranche': 72} if icds_feature_flag else {'age_tranche__in': [0, 6, 72]} moderately_underweight = exclude_records_by_age_for_column( {'age_tranche': 72}, 'nutrition_status_moderately_underweight' ) severely_underweight = exclude_records_by_age_for_column( {'age_tranche': 72}, 'nutrition_status_severely_underweight' ) wasting_moderate = exclude_records_by_age_for_column( age_filters, wasting_moderate_column(icds_feature_flag) ) wasting_severe = exclude_records_by_age_for_column( age_filters, wasting_severe_column(icds_feature_flag) ) stunting_moderate = exclude_records_by_age_for_column( age_filters, stunting_moderate_column(icds_feature_flag) ) stunting_severe = exclude_records_by_age_for_column( age_filters, stunting_severe_column(icds_feature_flag) ) nutrition_status_weighed = exclude_records_by_age_for_column( {'age_tranche': 72}, 'nutrition_status_weighed' ) height_measured_in_month = exclude_records_by_age_for_column( age_filters, hfa_recorded_in_month_column(icds_feature_flag) ) weighed_and_height_measured_in_month = exclude_records_by_age_for_column( age_filters, wfh_recorded_in_month_column(icds_feature_flag) ) queryset = AggChildHealthMonthly.objects.filter( month=date, **filters ).values( 'aggregation_level' ).annotate( underweight=( Sum(moderately_underweight) + Sum(severely_underweight) ), valid=Sum(nutrition_status_weighed), wasting=Sum(wasting_moderate) + Sum(wasting_severe), stunting=Sum(stunting_moderate) + Sum(stunting_severe), height_measured_in_month=Sum(height_measured_in_month), weighed_and_height_measured_in_month=Sum(weighed_and_height_measured_in_month), low_birth_weight=Sum('low_birth_weight_in_month'), bf_birth=Sum('bf_at_birth'), born=Sum('born_in_month'), weighed_and_born_in_month=Sum('weighed_and_born_in_month'), ebf=Sum('ebf_in_month'), ebf_eli=Sum('ebf_eligible'), cf_initiation=Sum('cf_initiation_in_month'), cf_initiation_eli=Sum('cf_initiation_eligible') ) if not show_test: queryset = apply_exclude(domain, queryset) return queryset def get_data_for_deliveries(date, filters): queryset = AggCcsRecordMonthly.objects.filter( month=date, **filters ).values( 'aggregation_level' ).annotate( institutional_delivery=Sum('institutional_delivery_in_month'), delivered=Sum('delivered_in_month') ) if not show_test: queryset = apply_exclude(domain, queryset) return queryset current_month = datetime(*config['month']) previous_month = datetime(*config['prev_month']) del config['month'] del config['prev_month'] this_month_data = get_data_for_child_health_monthly(current_month, config) prev_month_data = get_data_for_child_health_monthly(previous_month, config) deliveries_this_month = get_data_for_deliveries(current_month, config) deliveries_prev_month = get_data_for_deliveries(previous_month, config) gender_label, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval=default_age_interval(icds_feature_flag) ) return { 'records': [ [ { 'label': _('Underweight (Weight-for-Age)'), 'help_text': underweight_children_help_text(), 'percent': percent_diff( 'underweight', this_month_data, prev_month_data, 'valid' ), 'color': 'red' if percent_diff( 'underweight', this_month_data, prev_month_data, 'valid' ) > 0 else 'green', 'value': get_value(this_month_data, 'underweight'), 'all': get_value(this_month_data, 'valid'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/underweight_children' }, { 'label': _('Wasting (Weight-for-Height)'), 'help_text': _(wasting_help_text(age_label)), 'percent': percent_diff( 'wasting', this_month_data, prev_month_data, 'weighed_and_height_measured_in_month' ), 'color': 'red' if percent_diff( 'wasting', this_month_data, prev_month_data, 'weighed_and_height_measured_in_month' ) > 0 else 'green', 'value': get_value(this_month_data, 'wasting'), 'all': get_value(this_month_data, 'weighed_and_height_measured_in_month'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/wasting' } ], [ { 'label': _('Stunting (Height-for-Age)'), 'help_text': _(stunting_help_text(age_label)), 'percent': percent_diff( 'stunting', this_month_data, prev_month_data, 'height_measured_in_month' ), 'color': 'red' if percent_diff( 'stunting', this_month_data, prev_month_data, 'height_measured_in_month' ) > 0 else 'green', 'value': get_value(this_month_data, 'stunting'), 'all': get_value(this_month_data, 'height_measured_in_month'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/stunting' }, { 'label': _('Newborns with Low Birth Weight'), 'help_text': _(( new_born_with_low_weight_help_text(html=False) )), 'percent': percent_diff( 'low_birth_weight', this_month_data, prev_month_data, 'weighed_and_born_in_month' ), 'color': 'red' if percent_diff( 'low_birth_weight', this_month_data, prev_month_data, 'weighed_and_born_in_month' ) > 0 else 'green', 'value': get_value(this_month_data, 'low_birth_weight'), 'all': get_value(this_month_data, 'weighed_and_born_in_month'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/low_birth' } ], [ { 'label': _('Early Initiation of Breastfeeding'), 'help_text': early_initiation_breastfeeding_help_text(), 'percent': percent_diff( 'bf_birth', this_month_data, prev_month_data, 'born' ), 'color': 'green' if percent_diff( 'bf_birth', this_month_data, prev_month_data, 'born' ) > 0 else 'red', 'value': get_value(this_month_data, 'bf_birth'), 'all': get_value(this_month_data, 'born'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/early_initiation' }, { 'label': _('Exclusive Breastfeeding'), 'help_text': exclusive_breastfeeding_help_text(), 'percent': percent_diff( 'ebf', this_month_data, prev_month_data, 'ebf_eli' ), 'color': 'green' if percent_diff( 'ebf', this_month_data, prev_month_data, 'ebf_eli' ) > 0 else 'red', 'value': get_value(this_month_data, 'ebf'), 'all': get_value(this_month_data, 'ebf_eli'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/exclusive_breastfeeding' } ], [ { 'label': _('Children initiated appropriate Complementary Feeding'), 'help_text': children_initiated_appropriate_complementary_feeding_help_text(), 'percent': percent_diff( 'cf_initiation', this_month_data, prev_month_data, 'cf_initiation_eli' ), 'color': 'green' if percent_diff( 'cf_initiation', this_month_data, prev_month_data, 'cf_initiation_eli' ) > 0 else 'red', 'value': get_value(this_month_data, 'cf_initiation'), 'all': get_value(this_month_data, 'cf_initiation_eli'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/children_initiated' }, { 'label': _('Institutional Deliveries'), 'help_text': institutional_deliveries_help_text(), 'percent': percent_diff( 'institutional_delivery', deliveries_this_month, deliveries_prev_month, 'delivered' ), 'color': 'green' if percent_diff( 'institutional_delivery', deliveries_this_month, deliveries_prev_month, 'delivered' ) > 0 else 'red', 'value': get_value(deliveries_this_month, 'institutional_delivery'), 'all': get_value(deliveries_this_month, 'delivered'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/institutional_deliveries' } ] ] }
def get_newborn_with_low_birth_weight_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter( **filters ).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level ).annotate( low_birth=Sum('low_birth_weight_in_month'), in_month=Sum('weighed_and_born_in_month'), all=Sum('born_in_month') ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset data_for_map, in_month_total, low_birth_total, average, total = generate_data_for_map( get_data_for(config), loc_level, 'low_birth', 'in_month', 20, 60, 'all' ) fills = OrderedDict() fills.update({'0%-20%': MapColors.PINK}) fills.update({'20%-60%': MapColors.ORANGE}) fills.update({'60%-100%': MapColors.RED}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(config) return { "slug": "low_birth", "label": "Percent Newborns with Low Birth Weight{}".format(chosen_filters), "fills": fills, "rightLegend": { "average": average, "info": _(( new_born_with_low_weight_help_text(html=True) )), "extended_info": [ { 'indicator': 'Total Number of Newborns born in given month{}:'.format(chosen_filters), 'value': indian_formatted_number(total) }, { 'indicator': 'Number of Newborns with LBW in given month{}:'.format(chosen_filters), 'value': indian_formatted_number(low_birth_total) }, { 'indicator': 'Total Number of children born and weight in given month{}:'.format( chosen_filters ), 'value': indian_formatted_number(in_month_total) }, { 'indicator': '% newborns with LBW in given month{}:'.format(chosen_filters), 'value': '%.2f%%' % (low_birth_total * 100 / float(in_month_total or 1)) }, { 'indicator': '% of children with weight in normal{}:'.format(chosen_filters), 'value': '%.2f%%' % ((in_month_total - low_birth_total) * 100 / float(in_month_total or 1)) }, { 'indicator': '% Unweighted{}:'.format(chosen_filters), 'value': '%.2f%%' % ((total - in_month_total) * 100 / float(total or 1)) } ] }, "data": dict(data_for_map), }
def get_prevalence_of_undernutrition_data_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter(**filters).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level).annotate( moderately_underweight=Sum( 'nutrition_status_moderately_underweight'), severely_underweight=Sum( 'nutrition_status_severely_underweight'), normal=Sum('nutrition_status_normal'), valid=Sum('wer_eligible'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) if 'age_tranche' not in config: queryset = queryset.exclude(age_tranche=72) return queryset data_for_map = defaultdict( lambda: { 'moderately_underweight': 0, 'severely_underweight': 0, 'normal': 0, 'total': 0, 'original_name': [] }) moderately_underweight_total = 0 severely_underweight_total = 0 normal_total = 0 valid_total = 0 for row in get_data_for(config): valid = row['valid'] or 0 name = row['%s_name' % loc_level] on_map_name = row['%s_map_location_name' % loc_level] or name severely_underweight = row['severely_underweight'] or 0 moderately_underweight = row['moderately_underweight'] or 0 normal = row['normal'] or 0 moderately_underweight_total += moderately_underweight severely_underweight_total += severely_underweight_total normal_total += normal valid_total += valid data_for_map[on_map_name][ 'severely_underweight'] += severely_underweight data_for_map[on_map_name][ 'moderately_underweight'] += moderately_underweight data_for_map[on_map_name]['normal'] += normal data_for_map[on_map_name]['total'] += valid data_for_map[on_map_name]['original_name'].append(name) for data_for_location in six.itervalues(data_for_map): numerator = data_for_location[ 'moderately_underweight'] + data_for_location[ 'severely_underweight'] value = numerator * 100 / (data_for_location['total'] or 1) if value < 20: data_for_location.update({'fillKey': '0%-20%'}) elif 20 <= value < 35: data_for_location.update({'fillKey': '20%-35%'}) elif value >= 35: data_for_location.update({'fillKey': '35%-100%'}) fills = OrderedDict() fills.update({'0%-20%': MapColors.PINK}) fills.update({'20%-35%': MapColors.ORANGE}) fills.update({'35%-100%': MapColors.RED}) fills.update({'defaultFill': MapColors.GREY}) average = ( (moderately_underweight_total or 0) + (severely_underweight_total or 0)) * 100 / float(valid_total or 1) sum_of_indicators = moderately_underweight_total + severely_underweight_total + normal_total percent_unweighed = (valid_total - sum_of_indicators) * 100 / float( valid_total or 1) gender_label, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval='0 - 5 years') return { "slug": "moderately_underweight", "label": "Percent of Children{gender} Underweight ({age})".format( gender=gender_label, age=age_label), "fills": fills, "rightLegend": { "average": average, "info": _(("Percentage of children between {} enrolled for ICDS services with weight-for-age " "less than -2 standard deviations of the WHO Child Growth Standards median. " "<br/><br/>" "Children who are moderately or severely underweight have a higher risk of mortality" .format(age_label))), "extended_info": [{ 'indicator': 'Total Children{} weighed in given month:'.format( chosen_filters), 'value': indian_formatted_number(valid_total) }, { 'indicator': '% Unweighed{}:'.format(chosen_filters), 'value': '%.2f%%' % percent_unweighed }, { 'indicator': '% Severely Underweight{}:'.format(chosen_filters), 'value': '%.2f%%' % (severely_underweight_total * 100 / float(valid_total or 1)) }, { 'indicator': '% Moderately Underweight{}:'.format(chosen_filters), 'value': '%.2f%%' % (moderately_underweight_total * 100 / float(valid_total or 1)) }, { 'indicator': '% Normal{}:'.format(chosen_filters), 'value': '%.2f%%' % (normal_total * 100 / float(valid_total or 1)) }] }, "data": dict(data_for_map) }
def get_children_initiated_data_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter(**filters).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level).annotate( children=Sum('cf_initiation_in_month'), all=Sum('cf_initiation_eligible'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset data_for_map, valid_total, in_month_total, average, total = generate_data_for_map( get_data_for(config), loc_level, 'children', 'all', 20, 60) fills = OrderedDict() fills.update({'0%-20%': MapColors.RED}) fills.update({'20%-60%': MapColors.ORANGE}) fills.update({'60%-100%': MapColors.PINK}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels( config) return { "slug": "severe", "label": "Percent Children (6-8 months) initiated Complementary Feeding{}". format(chosen_filters), "fills": fills, "rightLegend": { "average": average, "info": _(("Of the total children enrolled for Anganwadi services between the ages of 6 to 8 months, " "the percentage that was given a timely introduction to solid, semi-solid or soft food." "<br/><br/>" "Timely intiation of complementary feeding in addition to breastmilk at 6 months of age " "is a key feeding practice to reduce malnutrition")), "extended_info": [{ 'indicator': 'Total number of children between age 6 - 8 months{}:'.format( chosen_filters), 'value': indian_formatted_number(valid_total) }, { 'indicator': ('Total number of children (6-8 months) given timely introduction to sold or ' 'semi-solid food in the given month{}:'.format(chosen_filters) ), 'value': indian_formatted_number(in_month_total) }, { 'indicator': ('% children (6-8 months) given timely introduction to solid or ' 'semi-solid food in the given month{}:'.format(chosen_filters) ), 'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1)) }] }, "data": dict(data_for_map), }
def get_prevalence_of_stunting_sector_data(domain, config, loc_level, location_id, show_test=False, icds_feature_flag=False): group_by = ['%s_name' % loc_level] config['month'] = datetime(*config['month']) data = AggChildHealthMonthly.objects.filter(**config).values( *group_by).annotate( moderate=Sum(stunting_moderate_column(icds_feature_flag)), severe=Sum(stunting_severe_column(icds_feature_flag)), normal=Sum(stunting_normal_column(icds_feature_flag)), total=Sum('height_eligible'), total_measured=Sum('height_measured_in_month'), ).order_by('%s_name' % loc_level) if not show_test: data = apply_exclude(domain, data) if 'age_tranche' not in config: if icds_feature_flag: data = data.exclude(age_tranche=72) else: data = data.exclude(age_tranche__in=[0, 6, 72]) chart_data = { 'blue': [], } tooltips_data = defaultdict(lambda: { 'severe': 0, 'moderate': 0, 'total': 0, 'normal': 0, 'total_measured': 0 }) loc_children = get_child_locations(domain, location_id, show_test) result_set = set() for row in data: total = row['total'] or 0 name = row['%s_name' % loc_level] result_set.add(name) severe = row['severe'] or 0 moderate = row['moderate'] or 0 normal = row['normal'] or 0 total_measured = row['total_measured'] or 0 row_values = { 'severe': severe, 'moderate': moderate, 'total': total, 'normal': normal, 'total_measured': total_measured, } for prop, value in six.iteritems(row_values): tooltips_data[name][prop] += value value = (moderate + severe) / float(total_measured or 1) chart_data['blue'].append([name, value]) for sql_location in loc_children: if sql_location.name not in result_set: chart_data['blue'].append([sql_location.name, 0]) chart_data['blue'] = sorted(chart_data['blue']) __, __, chosen_filters = chosen_filters_to_labels( config, default_interval=default_age_interval(icds_feature_flag)) return { "tooltips_data": dict(tooltips_data), "info": _(("Percentage of children{} enrolled for Anganwadi Services with height-for-age below " "-2Z standard deviations of the WHO Child Growth Standards median." "<br/><br/>" "Stunting is a sign of chronic undernutrition and has long lasting harmful " "consequences on the growth of a child".format(chosen_filters))), "chart_data": [ { "values": chart_data['blue'], "key": "", "strokeWidth": 2, "classed": "dashed", "color": MapColors.BLUE }, ] }
def get_children_initiated_data_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter( **filters ).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level ).annotate( children=Sum('cf_initiation_in_month'), all=Sum('cf_initiation_eligible'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset data_for_map, valid_total, in_month_total, average, total = generate_data_for_map( get_data_for(config), loc_level, 'children', 'all', 20, 60 ) fills = OrderedDict() fills.update({'0%-20%': MapColors.RED}) fills.update({'20%-60%': MapColors.ORANGE}) fills.update({'60%-100%': MapColors.PINK}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(config) return { "slug": "severe", "label": "Percent Children (6-8 months) initiated Complementary Feeding{}".format(chosen_filters), "fills": fills, "rightLegend": { "average": average, "info": children_initiated_appropriate_complementary_feeding_help_text(html=True), "extended_info": [ { 'indicator': 'Total number of children between age 6 - 8 months{}:'.format(chosen_filters), 'value': indian_formatted_number(valid_total) }, { 'indicator': ( 'Total number of children (6-8 months) given timely introduction to sold or ' 'semi-solid food in the given month{}:'.format(chosen_filters) ), 'value': indian_formatted_number(in_month_total) }, { 'indicator': ( '% children (6-8 months) given timely introduction to solid or ' 'semi-solid food in the given month{}:'.format(chosen_filters) ), 'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1)) } ] }, "data": dict(data_for_map), }
def get_early_initiation_breastfeeding_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter( **filters ).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level ).annotate( birth=Sum('bf_at_birth'), in_month=Sum('born_in_month'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset data_for_map, in_month_total, birth_total, average, total = generate_data_for_map( get_data_for(config), loc_level, 'birth', 'in_month', 20, 60 ) fills = OrderedDict() fills.update({'0%-20%': MapColors.RED}) fills.update({'20%-60%': MapColors.ORANGE}) fills.update({'60%-100%': MapColors.PINK}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(config) return { "slug": "early_initiation", "label": "Percent Early Initiation of Breastfeeding{}".format(chosen_filters), "fills": fills, "rightLegend": { "average": average, "info": early_initiation_breastfeeding_help_text(html=True), "extended_info": [ { 'indicator': 'Total Number of Children born in the given month{}:'.format(chosen_filters), 'value': indian_formatted_number(in_month_total) }, { 'indicator': ( 'Total Number of Children who were put to the breast within one hour of birth{}:' .format(chosen_filters) ), 'value': indian_formatted_number(birth_total) }, { 'indicator': '% children who were put to the breast within one hour of ' 'birth{}:'.format(chosen_filters), 'value': '%.2f%%' % (birth_total * 100 / float(in_month_total or 1)) } ] }, "data": dict(data_for_map), }
def get_prevalence_of_severe_sector_data(domain, config, loc_level, location_id, show_test=False, icds_feature_flag=False): group_by = ['%s_name' % loc_level] config['month'] = datetime(*config['month']) data = AggChildHealthMonthly.objects.filter( **config ).values( *group_by ).annotate( moderate=Sum(wasting_moderate_column(icds_feature_flag)), severe=Sum(wasting_severe_column(icds_feature_flag)), normal=Sum(wasting_normal_column(icds_feature_flag)), total_height_eligible=Sum('height_eligible'), total_weighed=Sum('nutrition_status_weighed'), total_measured=Sum(wfh_recorded_in_month_column(icds_feature_flag)), ).order_by('%s_name' % loc_level) if not show_test: data = apply_exclude(domain, data) if 'age_tranche' not in config: data = data.exclude(age_tranche=72) chart_data = { 'blue': [], } tooltips_data = defaultdict(lambda: { 'severe': 0, 'moderate': 0, 'total_height_eligible': 0, 'normal': 0, 'total_weighed': 0, 'total_measured': 0 }) loc_children = get_child_locations(domain, location_id, show_test) result_set = set() for row in data: total_weighed = row['total_weighed'] or 0 name = row['%s_name' % loc_level] result_set.add(name) severe = row['severe'] or 0 moderate = row['moderate'] or 0 normal = row['normal'] or 0 total_measured = row['total_measured'] or 0 total_height_eligible = row['total_height_eligible'] or 0 tooltips_data[name]['severe'] += severe tooltips_data[name]['moderate'] += moderate tooltips_data[name]['total_weighed'] += total_weighed tooltips_data[name]['normal'] += normal tooltips_data[name]['total_measured'] += total_measured tooltips_data[name]['total_height_eligible'] += total_height_eligible value = (moderate + severe) / float(total_weighed or 1) chart_data['blue'].append([ name, value ]) for sql_location in loc_children: if sql_location.name not in result_set: chart_data['blue'].append([sql_location.name, 0]) chart_data['blue'] = sorted(chart_data['blue']) gender_label, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval=default_age_interval(icds_feature_flag) ) return { "tooltips_data": dict(tooltips_data), "info": _(wasting_help_text(age_label)), "chart_data": [ { "values": chart_data['blue'], "key": "", "strokeWidth": 2, "classed": "dashed", "color": MapColors.BLUE }, ] }
def get_prevalence_of_severe_data_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter( **filters ).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level ).annotate( moderate=Sum('wasting_moderate'), severe=Sum('wasting_severe'), normal=Sum('wasting_normal'), valid=Sum('height_eligible'), total_measured=Sum('height_measured_in_month'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) if 'age_tranche' not in config: queryset = queryset.exclude(age_tranche__in=[0, 6, 72]) return queryset data_for_map = defaultdict(lambda: { 'moderate': 0, 'severe': 0, 'normal': 0, 'total': 0, 'total_measured': 0, 'original_name': [] }) severe_total = 0 moderate_total = 0 normal_total = 0 valid_total = 0 measured_total = 0 for row in get_data_for(config): valid = row['valid'] or 0 name = row['%s_name' % loc_level] on_map_name = row['%s_map_location_name' % loc_level] or name severe = row['severe'] or 0 moderate = row['moderate'] or 0 normal = row['normal'] or 0 total_measured = row['total_measured'] or 0 severe_total += severe moderate_total += moderate normal_total += normal valid_total += valid measured_total += total_measured data_for_map[on_map_name]['severe'] += severe data_for_map[on_map_name]['moderate'] += moderate data_for_map[on_map_name]['normal'] += normal data_for_map[on_map_name]['total'] += valid data_for_map[on_map_name]['total_measured'] += total_measured data_for_map[on_map_name]['original_name'].append(name) for data_for_location in six.itervalues(data_for_map): numerator = data_for_location['moderate'] + data_for_location['severe'] value = numerator * 100 / (data_for_location['total'] or 1) if value < 5: data_for_location.update({'fillKey': '0%-5%'}) elif 5 <= value <= 7: data_for_location.update({'fillKey': '5%-7%'}) elif value > 7: data_for_location.update({'fillKey': '7%-100%'}) fills = OrderedDict() fills.update({'0%-5%': MapColors.PINK}) fills.update({'5%-7%': MapColors.ORANGE}) fills.update({'7%-100%': MapColors.RED}) fills.update({'defaultFill': MapColors.GREY}) sum_of_indicators = moderate_total + severe_total + normal_total percent_unmeasured = (valid_total - sum_of_indicators) * 100 / float(valid_total or 1) gender_label, age_label, chosen_filters = chosen_filters_to_labels(config, default_interval='6 - 60 months') return { "slug": "severe", "label": "Percent of Children{gender} Wasted ({age})".format( gender=gender_label, age=age_label ), "fills": fills, "rightLegend": { "average": "%.2f" % (((severe_total + moderate_total) * 100) / float(valid_total or 1)), "info": _(( "Percentage of children between {} enrolled for ICDS services with " "weight-for-height below -2 standard deviations of the WHO Child Growth Standards median. " "<br/><br/>" "Wasting in children is a symptom of acute undernutrition usually as a consequence " "of insufficient food intake or a high incidence of infectious diseases. Severe Acute " "Malnutrition (SAM) is nutritional status for a child who has severe wasting " "(weight-for-height) below -3 Z and Moderate Acute Malnutrition (MAM) is nutritional " "status for a child that has moderate wasting (weight-for-height) below -2Z." .format(age_label) )), "extended_info": [ { 'indicator': 'Total Children{} weighed in given month:'.format(chosen_filters), 'value': indian_formatted_number(valid_total) }, { 'indicator': 'Total Children{} with height measured in given month:' .format(chosen_filters), 'value': indian_formatted_number(measured_total) }, { 'indicator': '% Unmeasured{}:'.format(chosen_filters), 'value': '%.2f%%' % percent_unmeasured }, { 'indicator': '% Severely Acute Malnutrition{}:'.format(chosen_filters), 'value': '%.2f%%' % (severe_total * 100 / float(valid_total or 1)) }, { 'indicator': '% Moderately Acute Malnutrition{}:'.format(chosen_filters), 'value': '%.2f%%' % (moderate_total * 100 / float(valid_total or 1)) }, { 'indicator': '% Normal{}:'.format(chosen_filters), 'value': '%.2f%%' % (normal_total * 100 / float(valid_total or 1)) } ] }, "data": dict(data_for_map), }
def get_prevalence_of_stunting_sector_data(domain, config, loc_level, location_id, show_test=False, icds_feature_flag=False): group_by = ['%s_name' % loc_level] config['month'] = datetime(*config['month']) data = AggChildHealthMonthly.objects.filter(**config).values( *group_by).annotate( moderate=Sum(stunting_moderate_column(icds_feature_flag)), severe=Sum(stunting_severe_column(icds_feature_flag)), normal=Sum(stunting_normal_column(icds_feature_flag)), total=Sum('height_eligible'), total_measured=Sum( hfa_recorded_in_month_column(icds_feature_flag)), ).order_by('%s_name' % loc_level) if not show_test: data = apply_exclude(domain, data) if 'age_tranche' not in config: data = data.filter(age_tranche__lt=72) chart_data = { 'blue': [], } tooltips_data = defaultdict(lambda: { 'severe': 0, 'moderate': 0, 'total': 0, 'normal': 0, 'total_measured': 0 }) if icds_feature_flag: location_launched_status = get_location_launched_status( config, loc_level) else: location_launched_status = None for row in data: if location_launched_status: launched_status = location_launched_status.get(row['%s_name' % loc_level]) if launched_status is None or launched_status <= 0: continue total = row['total'] or 0 name = row['%s_name' % loc_level] severe = row['severe'] or 0 moderate = row['moderate'] or 0 normal = row['normal'] or 0 total_measured = row['total_measured'] or 0 row_values = { 'severe': severe, 'moderate': moderate, 'total': total, 'normal': normal, 'total_measured': total_measured, } for prop, value in row_values.items(): tooltips_data[name][prop] += value value = (moderate + severe) / float(total_measured or 1) chart_data['blue'].append([name, value]) chart_data['blue'] = sorted(chart_data['blue']) __, __, chosen_filters = chosen_filters_to_labels( config, default_interval=default_age_interval(icds_feature_flag)) return { "tooltips_data": dict(tooltips_data), "info": _(("Of the children enrolled for Anganwadi services, whose height was measured, the percentage " "of children between {} who were moderately/severely stunted in the current month. " "<br/><br/>" "Stunting is a sign of chronic undernutrition and has long lasting harmful consequences on " "the growth of a child".format(chosen_filters))), "chart_data": [ { "values": chart_data['blue'], "key": "", "strokeWidth": 2, "classed": "dashed", "color": MapColors.BLUE }, ] }
def get_exclusive_breastfeeding_data_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter( **filters ).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level ).annotate( children=Sum('ebf_in_month'), all=Sum('ebf_eligible'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset data_for_map, valid_total, in_month_total = generate_data_for_map( get_data_for(config), loc_level, 'children', 'all', 20, 60 ) fills = OrderedDict() fills.update({'0%-20%': MapColors.RED}) fills.update({'20%-60%': MapColors.ORANGE}) fills.update({'60%-100%': MapColors.PINK}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(config) return { "slug": "severe", "label": "Percent Exclusive Breastfeeding{}".format(chosen_filters), "fills": fills, "rightLegend": { "average": (in_month_total * 100) / (float(valid_total) or 1), "info": _(( "Percentage of infants 0-6 months of age who are fed exclusively with breast milk. " "<br/><br/>" "An infant is exclusively breastfed if they recieve only breastmilk with no additional food, " "liquids (even water) ensuring optimal nutrition and growth between 0 - 6 months" )), "extended_info": [ { 'indicator': 'Total number of children between ages 0 - 6 months{}:' .format(chosen_filters), 'value': indian_formatted_number(valid_total) }, { 'indicator': ( 'Total number of children (0-6 months) exclusively breastfed in the given month{}:' .format(chosen_filters) ), 'value': indian_formatted_number(in_month_total) }, { 'indicator': '% children (0-6 months) exclusively breastfed in the ' 'given month{}:'.format(chosen_filters), 'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1)) } ] }, "data": dict(data_for_map), }
def get_prevalence_of_stunting_sector_data(domain, config, loc_level, location_id, show_test=False, icds_feature_flag=False): group_by = ['%s_name' % loc_level] config['month'] = datetime(*config['month']) data = AggChildHealthMonthly.objects.filter( **config ).values( *group_by ).annotate( moderate=Sum(stunting_moderate_column(icds_feature_flag)), severe=Sum(stunting_severe_column(icds_feature_flag)), normal=Sum(stunting_normal_column(icds_feature_flag)), total=Sum('height_eligible'), total_measured=Sum(hfa_recorded_in_month_column(icds_feature_flag)), ).order_by('%s_name' % loc_level) if not show_test: data = apply_exclude(domain, data) if 'age_tranche' not in config: data = data.exclude(age_tranche=72) chart_data = { 'blue': [], } tooltips_data = defaultdict(lambda: { 'severe': 0, 'moderate': 0, 'total': 0, 'normal': 0, 'total_measured': 0 }) loc_children = get_child_locations(domain, location_id, show_test) result_set = set() for row in data: total = row['total'] or 0 name = row['%s_name' % loc_level] result_set.add(name) severe = row['severe'] or 0 moderate = row['moderate'] or 0 normal = row['normal'] or 0 total_measured = row['total_measured'] or 0 row_values = { 'severe': severe, 'moderate': moderate, 'total': total, 'normal': normal, 'total_measured': total_measured, } for prop, value in six.iteritems(row_values): tooltips_data[name][prop] += value value = (moderate + severe) / float(total_measured or 1) chart_data['blue'].append([ name, value ]) for sql_location in loc_children: if sql_location.name not in result_set: chart_data['blue'].append([sql_location.name, 0]) chart_data['blue'] = sorted(chart_data['blue']) __, __, chosen_filters = chosen_filters_to_labels( config, default_interval=default_age_interval(icds_feature_flag) ) return { "tooltips_data": dict(tooltips_data), "info": _(( "Of the children enrolled for Anganwadi services, whose height was measured, the percentage " "of children between {} who were moderately/severely stunted in the current month. " "<br/><br/>" "Stunting is a sign of chronic undernutrition and has long lasting harmful consequences on " "the growth of a child".format(chosen_filters) )), "chart_data": [ { "values": chart_data['blue'], "key": "", "strokeWidth": 2, "classed": "dashed", "color": MapColors.BLUE }, ] }
def get_maternal_child_data(domain, config, show_test=False, icds_feature_flag=False): def get_data_for_child_health_monthly(date, filters): age_filters = { 'age_tranche': 72 } if icds_feature_flag else { 'age_tranche__in': [0, 6, 72] } moderately_underweight = exclude_records_by_age_for_column( {'age_tranche': 72}, 'nutrition_status_moderately_underweight') severely_underweight = exclude_records_by_age_for_column( {'age_tranche': 72}, 'nutrition_status_severely_underweight') wasting_moderate = exclude_records_by_age_for_column( age_filters, wasting_moderate_column(icds_feature_flag)) wasting_severe = exclude_records_by_age_for_column( age_filters, wasting_severe_column(icds_feature_flag)) stunting_moderate = exclude_records_by_age_for_column( age_filters, stunting_moderate_column(icds_feature_flag)) stunting_severe = exclude_records_by_age_for_column( age_filters, stunting_severe_column(icds_feature_flag)) nutrition_status_weighed = exclude_records_by_age_for_column( {'age_tranche': 72}, 'nutrition_status_weighed') height_measured_in_month = exclude_records_by_age_for_column( age_filters, hfa_recorded_in_month_column(icds_feature_flag)) weighed_and_height_measured_in_month = exclude_records_by_age_for_column( age_filters, wfh_recorded_in_month_column(icds_feature_flag)) queryset = AggChildHealthMonthly.objects.filter( month=date, **filters).values('aggregation_level').annotate( underweight=(Sum(moderately_underweight) + Sum(severely_underweight)), valid=Sum(nutrition_status_weighed), wasting=Sum(wasting_moderate) + Sum(wasting_severe), stunting=Sum(stunting_moderate) + Sum(stunting_severe), height_measured_in_month=Sum(height_measured_in_month), weighed_and_height_measured_in_month=Sum( weighed_and_height_measured_in_month), low_birth_weight=Sum('low_birth_weight_in_month'), bf_birth=Sum('bf_at_birth'), born=Sum('born_in_month'), weighed_and_born_in_month=Sum('weighed_and_born_in_month'), ebf=Sum('ebf_in_month'), ebf_eli=Sum('ebf_eligible'), cf_initiation=Sum('cf_initiation_in_month'), cf_initiation_eli=Sum('cf_initiation_eligible')) if not show_test: queryset = apply_exclude(domain, queryset) return queryset def get_data_for_deliveries(date, filters): queryset = AggCcsRecordMonthly.objects.filter( month=date, **filters).values('aggregation_level').annotate( institutional_delivery=Sum('institutional_delivery_in_month'), delivered=Sum('delivered_in_month')) if not show_test: queryset = apply_exclude(domain, queryset) return queryset current_month = datetime(*config['month']) previous_month = datetime(*config['prev_month']) del config['month'] del config['prev_month'] this_month_data = get_data_for_child_health_monthly(current_month, config) prev_month_data = get_data_for_child_health_monthly(previous_month, config) deliveries_this_month = get_data_for_deliveries(current_month, config) deliveries_prev_month = get_data_for_deliveries(previous_month, config) gender_label, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval=default_age_interval(icds_feature_flag)) return { 'records': [[{ 'label': _('Underweight (Weight-for-Age)'), 'help_text': underweight_children_help_text(), 'percent': percent_diff('underweight', this_month_data, prev_month_data, 'valid'), 'color': 'red' if percent_diff('underweight', this_month_data, prev_month_data, 'valid') > 0 else 'green', 'value': get_value(this_month_data, 'underweight'), 'all': get_value(this_month_data, 'valid'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/underweight_children' }, { 'label': _('Wasting (Weight-for-Height)'), 'help_text': _(wasting_help_text(age_label)), 'percent': percent_diff('wasting', this_month_data, prev_month_data, 'weighed_and_height_measured_in_month'), 'color': 'red' if percent_diff('wasting', this_month_data, prev_month_data, 'weighed_and_height_measured_in_month') > 0 else 'green', 'value': get_value(this_month_data, 'wasting'), 'all': get_value(this_month_data, 'weighed_and_height_measured_in_month'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/wasting' }], [{ 'label': _('Stunting (Height-for-Age)'), 'help_text': _(stunting_help_text(age_label)), 'percent': percent_diff('stunting', this_month_data, prev_month_data, 'height_measured_in_month'), 'color': 'red' if percent_diff('stunting', this_month_data, prev_month_data, 'height_measured_in_month') > 0 else 'green', 'value': get_value(this_month_data, 'stunting'), 'all': get_value(this_month_data, 'height_measured_in_month'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/stunting' }, { 'label': _('Newborns with Low Birth Weight'), 'help_text': _((new_born_with_low_weight_help_text(html=False))), 'percent': percent_diff('low_birth_weight', this_month_data, prev_month_data, 'weighed_and_born_in_month'), 'color': get_color_with_red_positive( percent_diff('low_birth_weight', this_month_data, prev_month_data, 'weighed_and_born_in_month')), 'value': get_value(this_month_data, 'low_birth_weight'), 'all': get_value(this_month_data, 'weighed_and_born_in_month'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/low_birth' }], [{ 'label': _('Early Initiation of Breastfeeding'), 'help_text': early_initiation_breastfeeding_help_text(), 'percent': percent_diff('bf_birth', this_month_data, prev_month_data, 'born'), 'color': get_color_with_green_positive( percent_diff('bf_birth', this_month_data, prev_month_data, 'born')), 'value': get_value(this_month_data, 'bf_birth'), 'all': get_value(this_month_data, 'born'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/early_initiation' }, { 'label': _('Exclusive Breastfeeding'), 'help_text': exclusive_breastfeeding_help_text(), 'percent': percent_diff('ebf', this_month_data, prev_month_data, 'ebf_eli'), 'color': get_color_with_green_positive( percent_diff('ebf', this_month_data, prev_month_data, 'ebf_eli')), 'value': get_value(this_month_data, 'ebf'), 'all': get_value(this_month_data, 'ebf_eli'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/exclusive_breastfeeding' }], [{ 'label': _('Children initiated appropriate Complementary Feeding'), 'help_text': children_initiated_appropriate_complementary_feeding_help_text(), 'percent': percent_diff('cf_initiation', this_month_data, prev_month_data, 'cf_initiation_eli'), 'color': get_color_with_green_positive( percent_diff('cf_initiation', this_month_data, prev_month_data, 'cf_initiation_eli')), 'value': get_value(this_month_data, 'cf_initiation'), 'all': get_value(this_month_data, 'cf_initiation_eli'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/children_initiated' }, { 'label': _('Institutional Deliveries'), 'help_text': institutional_deliveries_help_text(), 'percent': percent_diff('institutional_delivery', deliveries_this_month, deliveries_prev_month, 'delivered'), 'color': get_color_with_green_positive( percent_diff('institutional_delivery', deliveries_this_month, deliveries_prev_month, 'delivered')), 'value': get_value(deliveries_this_month, 'institutional_delivery'), 'all': get_value(deliveries_this_month, 'delivered'), 'format': 'percent_and_div', 'frequency': 'month', 'redirect': 'maternal_and_child/institutional_deliveries' }]] }
def get_immunization_coverage_data_map(domain, config, loc_level, show_test=False, icds_features_flag=False): config['month'] = datetime(*config['month']) def get_data_for(filters): queryset = AggChildHealthMonthly.objects.filter(**filters).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level).annotate( children=Sum('fully_immunized_on_time') + Sum('fully_immunized_late'), all=Sum('fully_immunized_eligible'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset if icds_features_flag: location_launched_status = get_location_launched_status( config, loc_level) else: location_launched_status = None data_for_map, valid_total, in_month_total, average, total = generate_data_for_map( get_data_for(config), loc_level, 'children', 'all', 20, 60, location_launched_status=location_launched_status) fills = OrderedDict() fills.update({'0%-20%': MapColors.RED}) fills.update({'20%-60%': MapColors.ORANGE}) fills.update({'60%-100%': MapColors.PINK}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels( config) return { "slug": "institutional_deliveries", "label": "Percent Immunization Coverage at 1 year{}".format(chosen_filters), "fills": fills, "rightLegend": { "average": average, "info": _(("Of the total number of children enrolled for Anganwadi Services who are over a year old, " "the percentage of children who have received the complete immunization as per the National " "Immunization Schedule of India that is required by age 1." "<br/><br/>" "This includes the following immunizations:<br/>" "If Pentavalent path: Penta1/2/3, OPV1/2/3, BCG, Measles, VitA1<br/>" "If DPT/HepB path: DPT1/2/3, HepB1/2/3, OPV1/2/3, BCG, Measles, VitA1" )), "extended_info": [{ 'indicator': 'Total number of ICDS Child beneficiaries older than ' '1 year{}:'.format(chosen_filters), 'value': indian_formatted_number(valid_total) }, { 'indicator': ('Total number of children who have recieved complete immunizations required ' 'by age 1{}:'.format(chosen_filters)), 'value': indian_formatted_number(in_month_total) }, { 'indicator': ('% of children who have recieved complete immunizations required by age 1{}:' .format(chosen_filters)), 'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1)) }] }, "data": dict(data_for_map), }
def get_enrolled_children_data_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter( **filters ).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level ).annotate( valid=Sum('valid_in_month'), all=Sum('valid_all_registered_in_month') ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset data_for_map = defaultdict(lambda: { 'valid': 0, 'all': 0, 'original_name': [], 'fillKey': 'Children' }) average = [] total_valid = 0 total = 0 for row in get_data_for(config): valid = row['valid'] or 0 name = row['%s_name' % loc_level] all_children = row['all'] or 0 on_map_name = row['%s_map_location_name' % loc_level] or name average.append(valid) total_valid += valid total += all_children data_for_map[on_map_name]['valid'] += valid data_for_map[on_map_name]['all'] += all_children data_for_map[on_map_name]['original_name'].append(name) fills = OrderedDict() fills.update({'Children': MapColors.BLUE}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_label, chosen_filters = chosen_filters_to_labels(config, default_interval='0 - 6 years') return { "slug": "enrolled_children", "label": "", "fills": fills, "rightLegend": { "average": '%.2f' % (total_valid * 100 / float(total or 1)), "info": percent_children_enrolled_help_text(age_label=age_label), "extended_info": [ { 'indicator': 'Number of children{} who are enrolled for Anganwadi Services:' .format(chosen_filters), 'value': indian_formatted_number(total_valid) }, { 'indicator': ( 'Total number of children{} who are registered: ' .format(chosen_filters) ), 'value': indian_formatted_number(total) }, { 'indicator': ( 'Percentage of registered children{} who are enrolled for Anganwadi Services:' .format(chosen_filters) ), 'value': '%.2f%%' % (total_valid * 100 / float(total or 1)) } ] }, "data": dict(data_for_map), }
def get_immunization_coverage_data_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter( **filters ).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level ).annotate( children=Sum('fully_immunized_on_time') + Sum('fully_immunized_late'), all=Sum('fully_immunized_eligible'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset data_for_map, valid_total, in_month_total, average, total = generate_data_for_map( get_data_for(config), loc_level, 'children', 'all', 20, 60 ) fills = OrderedDict() fills.update({'0%-20%': MapColors.RED}) fills.update({'20%-60%': MapColors.ORANGE}) fills.update({'60%-100%': MapColors.PINK}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(config) return { "slug": "institutional_deliveries", "label": "Percent Immunization Coverage at 1 year{}".format(chosen_filters), "fills": fills, "rightLegend": { "average": average, "info": _(( "Of the total number of children enrolled for Anganwadi Services who are over a year old, " "the percentage of children who have received the complete immunization as per the National " "Immunization Schedule of India that is required by age 1." "<br/><br/>" "This includes the following immunizations:<br/>" "If Pentavalent path: Penta1/2/3, OPV1/2/3, BCG, Measles, VitA1<br/>" "If DPT/HepB path: DPT1/2/3, HepB1/2/3, OPV1/2/3, BCG, Measles, VitA1" )), "extended_info": [ { 'indicator': 'Total number of ICDS Child beneficiaries older than ' '1 year{}:'.format(chosen_filters), 'value': indian_formatted_number(valid_total) }, { 'indicator': ( 'Total number of children who have recieved complete immunizations required ' 'by age 1{}:'.format(chosen_filters) ), 'value': indian_formatted_number(in_month_total) }, { 'indicator': ( '% of children who have recieved complete immunizations required by age 1{}:' .format(chosen_filters) ), 'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1)) } ] }, "data": dict(data_for_map), }
def get_prevalence_of_severe_data_map(domain, config, loc_level, show_test=False, icds_feature_flag=False): config['month'] = datetime(*config['month']) def get_data_for(filters): queryset = AggChildHealthMonthly.objects.filter(**filters).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level).annotate( moderate=Sum(wasting_moderate_column(icds_feature_flag)), severe=Sum(wasting_severe_column(icds_feature_flag)), normal=Sum(wasting_normal_column(icds_feature_flag)), total_height_eligible=Sum('height_eligible'), total_weighed=Sum('nutrition_status_weighed'), total_measured=Sum( wfh_recorded_in_month_column(icds_feature_flag)), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) if 'age_tranche' not in config: queryset = queryset.filter(age_tranche__lt=72) return queryset data_for_map = defaultdict( lambda: { 'moderate': 0, 'severe': 0, 'normal': 0, 'total_weighed': 0, 'total_measured': 0, 'total_height_eligible': 0, 'original_name': [] }) severe_for_all_locations = 0 moderate_for_all_locations = 0 normal_for_all_locations = 0 weighed_for_all_locations = 0 measured_for_all_locations = 0 height_eligible_for_all_locations = 0 values_to_calculate_average = {'numerator': 0, 'denominator': 0} if icds_feature_flag: location_launched_status = get_location_launched_status( config, loc_level) else: location_launched_status = None for row in get_data_for(config): if location_launched_status: launched_status = location_launched_status.get(row['%s_name' % loc_level]) if launched_status is None or launched_status <= 0: continue total_weighed = row['total_weighed'] or 0 total_height_eligible = row['total_height_eligible'] or 0 name = row['%s_name' % loc_level] on_map_name = row['%s_map_location_name' % loc_level] or name severe = row['severe'] or 0 moderate = row['moderate'] or 0 normal = row['normal'] or 0 total_measured = row['total_measured'] or 0 values_to_calculate_average['numerator'] += moderate if moderate else 0 values_to_calculate_average['numerator'] += severe if severe else 0 values_to_calculate_average[ 'denominator'] += total_measured if total_measured else 0 severe_for_all_locations += severe moderate_for_all_locations += moderate normal_for_all_locations += normal weighed_for_all_locations += total_weighed measured_for_all_locations += total_measured height_eligible_for_all_locations += total_height_eligible data_for_map[on_map_name]['severe'] += severe data_for_map[on_map_name]['moderate'] += moderate data_for_map[on_map_name]['normal'] += normal data_for_map[on_map_name]['total_weighed'] += total_weighed data_for_map[on_map_name]['total_measured'] += total_measured data_for_map[on_map_name][ 'total_height_eligible'] += total_height_eligible data_for_map[on_map_name]['original_name'].append(name) for data_for_location in data_for_map.values(): numerator = data_for_location['moderate'] + data_for_location['severe'] value = numerator * 100 / (data_for_location['total_measured'] or 1) if value < 5: data_for_location.update({'fillKey': '0%-5%'}) elif 5 <= value <= 7: data_for_location.update({'fillKey': '5%-7%'}) elif value > 7: data_for_location.update({'fillKey': '7%-100%'}) fills = OrderedDict() fills.update({'0%-5%': MapColors.PINK}) fills.update({'5%-7%': MapColors.ORANGE}) fills.update({'7%-100%': MapColors.RED}) if icds_feature_flag: fills.update({'Not Launched': MapColors.GREY}) fills.update({'defaultFill': MapColors.GREY}) gender_label, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval=default_age_interval(icds_feature_flag)) average = ((values_to_calculate_average['numerator'] * 100) / float(values_to_calculate_average['denominator'] or 1)) return { "slug": "severe", "label": "Percent of Children{gender} Wasted ({age})".format( gender=gender_label, age=age_label), "fills": fills, "rightLegend": { "average": "%.2f" % average, "info": wasting_help_text(age_label), "extended_info": [{ 'indicator': 'Total Children{} weighed in given month:'.format( chosen_filters), 'value': indian_formatted_number(weighed_for_all_locations) }, { 'indicator': 'Total Children{} with weight and height measured in given month:' .format(chosen_filters), 'value': indian_formatted_number(measured_for_all_locations) }, { 'indicator': 'Number of children{} unmeasured:'.format(chosen_filters), 'value': indian_formatted_number(height_eligible_for_all_locations - weighed_for_all_locations) }, { 'indicator': '% Severely Acute Malnutrition{}:'.format(chosen_filters), 'value': '%.2f%%' % (severe_for_all_locations * 100 / float(measured_for_all_locations or 1)) }, { 'indicator': '% Moderately Acute Malnutrition{}:'.format(chosen_filters), 'value': '%.2f%%' % (moderate_for_all_locations * 100 / float(measured_for_all_locations or 1)) }, { 'indicator': '% Normal{}:'.format(chosen_filters), 'value': '%.2f%%' % (normal_for_all_locations * 100 / float(measured_for_all_locations or 1)) }] }, "data": dict(data_for_map), }
def get_children_initiated_data_map(domain, config, loc_level, show_test=False, icds_features_flag=False): config['month'] = datetime(*config['month']) def get_data_for(filters): queryset = AggChildHealthMonthly.objects.filter(**filters).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level).annotate( children=Sum('cf_initiation_in_month'), all=Sum('cf_initiation_eligible'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset if icds_features_flag: location_launched_status = get_location_launched_status( config, loc_level) else: location_launched_status = None data_for_map, valid_total, in_month_total, average, total = generate_data_for_map( get_data_for(config), loc_level, 'children', 'all', 20, 60, location_launched_status=location_launched_status) fills = OrderedDict() fills.update({'0%-20%': MapColors.RED}) fills.update({'20%-60%': MapColors.ORANGE}) fills.update({'60%-100%': MapColors.PINK}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels( config) return { "slug": "severe", "label": "Percent Children (6-8 months) initiated Complementary Feeding{}". format(chosen_filters), "fills": fills, "rightLegend": { "average": average, "info": children_initiated_appropriate_complementary_feeding_help_text( html=True), "extended_info": [{ 'indicator': 'Total number of children between age 6 - 8 months{}:'.format( chosen_filters), 'value': indian_formatted_number(valid_total) }, { 'indicator': ('Total number of children (6-8 months) given timely introduction to sold or ' 'semi-solid food in the given month{}:'.format(chosen_filters) ), 'value': indian_formatted_number(in_month_total) }, { 'indicator': ('% children (6-8 months) given timely introduction to solid or ' 'semi-solid food in the given month{}:'.format(chosen_filters) ), 'value': '%.2f%%' % (in_month_total * 100 / float(valid_total or 1)) }] }, "data": dict(data_for_map), }
def get_prevalence_of_severe_sector_data(domain, config, loc_level, location_id, show_test=False, icds_feature_flag=False): group_by = ['%s_name' % loc_level] config['month'] = datetime(*config['month']) data = AggChildHealthMonthly.objects.filter(**config).values( *group_by).annotate( moderate=Sum(wasting_moderate_column(icds_feature_flag)), severe=Sum(wasting_severe_column(icds_feature_flag)), normal=Sum(wasting_normal_column(icds_feature_flag)), total_height_eligible=Sum('height_eligible'), total_weighed=Sum('nutrition_status_weighed'), total_measured=Sum( wfh_recorded_in_month_column(icds_feature_flag)), ).order_by('%s_name' % loc_level) if not show_test: data = apply_exclude(domain, data) if 'age_tranche' not in config: data = data.filter(age_tranche__lt=72) chart_data = { 'blue': [], } tooltips_data = defaultdict( lambda: { 'severe': 0, 'moderate': 0, 'total_height_eligible': 0, 'normal': 0, 'total_weighed': 0, 'total_measured': 0 }) if icds_feature_flag: location_launched_status = get_location_launched_status( config, loc_level) else: location_launched_status = None for row in data: if location_launched_status: launched_status = location_launched_status.get(row['%s_name' % loc_level]) if launched_status is None or launched_status <= 0: continue total_weighed = row['total_weighed'] or 0 name = row['%s_name' % loc_level] severe = row['severe'] or 0 moderate = row['moderate'] or 0 normal = row['normal'] or 0 total_measured = row['total_measured'] or 0 total_height_eligible = row['total_height_eligible'] or 0 tooltips_data[name]['severe'] += severe tooltips_data[name]['moderate'] += moderate tooltips_data[name]['total_weighed'] += total_weighed tooltips_data[name]['normal'] += normal tooltips_data[name]['total_measured'] += total_measured tooltips_data[name]['total_height_eligible'] += total_height_eligible value = (moderate + severe) / float(total_weighed or 1) chart_data['blue'].append([name, value]) chart_data['blue'] = sorted(chart_data['blue']) gender_label, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval=default_age_interval(icds_feature_flag)) return { "tooltips_data": dict(tooltips_data), "info": _(wasting_help_text(age_label)), "chart_data": [ { "values": chart_data['blue'], "key": "", "strokeWidth": 2, "classed": "dashed", "color": MapColors.BLUE }, ] }
def get_prevalence_of_stunting_data_map(domain, config, loc_level, show_test=False, icds_feature_flag=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter(**filters).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level).annotate( moderate=Sum(stunting_moderate_column(icds_feature_flag)), severe=Sum(stunting_severe_column(icds_feature_flag)), normal=Sum(stunting_normal_column(icds_feature_flag)), total=Sum('height_eligible'), total_measured=Sum('height_measured_in_month'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) if 'age_tranche' not in config: if icds_feature_flag: queryset = queryset.exclude(age_tranche=72) else: queryset = queryset.exclude(age_tranche__in=[0, 6, 72]) return queryset data_for_map = defaultdict( lambda: { 'moderate': 0, 'severe': 0, 'normal': 0, 'total': 0, 'total_measured': 0, 'original_name': [] }) moderate_total = 0 severe_total = 0 normal_total = 0 all_total = 0 measured_total = 0 values_to_calculate_average = [] for row in get_data_for(config): total = row['total'] or 0 name = row['%s_name' % loc_level] on_map_name = row['%s_map_location_name' % loc_level] or name severe = row['severe'] or 0 moderate = row['moderate'] or 0 normal = row['normal'] or 0 total_measured = row['total_measured'] or 0 numerator = moderate + severe values_to_calculate_average.append(numerator * 100 / (total_measured or 1)) severe_total += severe moderate_total += moderate normal_total += normal all_total += total measured_total += total_measured data_for_map[on_map_name]['severe'] += severe data_for_map[on_map_name]['moderate'] += moderate data_for_map[on_map_name]['normal'] += normal data_for_map[on_map_name]['total'] += total data_for_map[on_map_name]['total_measured'] += total_measured data_for_map[on_map_name]['original_name'].append(name) for data_for_location in six.itervalues(data_for_map): numerator = data_for_location['moderate'] + data_for_location['severe'] value = numerator * 100 / (data_for_location['total_measured'] or 1) if value < 25: data_for_location.update({'fillKey': '0%-25%'}) elif 25 <= value < 38: data_for_location.update({'fillKey': '25%-38%'}) elif value >= 38: data_for_location.update({'fillKey': '38%-100%'}) fills = OrderedDict() fills.update({'0%-25%': MapColors.PINK}) fills.update({'25%-38%': MapColors.ORANGE}) fills.update({'38%-100%': MapColors.RED}) fills.update({'defaultFill': MapColors.GREY}) gender_label, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval=default_age_interval(icds_feature_flag)) return { "slug": "severe", "label": "Percent of Children{gender} Stunted ({age})".format( gender=gender_label, age=age_label), "fills": fills, "rightLegend": { "average": "%.2f" % ((sum(values_to_calculate_average)) / float(len(values_to_calculate_average) or 1)), "info": _(("Percentage of children ({}) enrolled for Anganwadi Services with height-for-age below " "-2Z standard deviations of the WHO Child Growth Standards median." "<br/><br/>" "Stunting is a sign of chronic undernutrition and has long lasting harmful " "consequences on the growth of a child".format(age_label))), "extended_info": [{ 'indicator': 'Total Children{} eligible to have height measured:'.format( chosen_filters), 'value': indian_formatted_number(all_total) }, { 'indicator': 'Total Children{} with height measured in given month:'.format( chosen_filters), 'value': indian_formatted_number(measured_total) }, { 'indicator': 'Number of Children{} unmeasured:'.format(chosen_filters), 'value': indian_formatted_number(all_total - measured_total) }, { 'indicator': '% children{} with severely stunted growth:'.format( chosen_filters), 'value': '%.2f%%' % (severe_total * 100 / float(measured_total or 1)) }, { 'indicator': '% children{} with moderate stunted growth:'.format( chosen_filters), 'value': '%.2f%%' % (moderate_total * 100 / float(measured_total or 1)) }, { 'indicator': '% children{} with normal stunted growth:'.format( chosen_filters), 'value': '%.2f%%' % (normal_total * 100 / float(measured_total or 1)) }] }, "data": dict(data_for_map), }
def get_prevalence_of_undernutrition_data_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter(**filters).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level).annotate( moderately_underweight=Sum( 'nutrition_status_moderately_underweight'), severely_underweight=Sum( 'nutrition_status_severely_underweight'), normal=Sum('nutrition_status_normal'), weighed=Sum('nutrition_status_weighed'), total=Sum('wer_eligible'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) if 'age_tranche' not in config: queryset = queryset.exclude(age_tranche=72) return queryset data_for_map = defaultdict( lambda: { 'moderately_underweight': 0, 'severely_underweight': 0, 'normal': 0, 'weighed': 0, 'total': 0, 'original_name': [] }) moderately_underweight_total = 0 severely_underweight_total = 0 normal_total = 0 all_total = 0 weighed_total = 0 values_to_calculate_average = [] for row in get_data_for(config): weighed = row['weighed'] or 0 total = row['total'] or 0 name = row['%s_name' % loc_level] on_map_name = row['%s_map_location_name' % loc_level] or name severely_underweight = row['severely_underweight'] or 0 moderately_underweight = row['moderately_underweight'] or 0 normal = row['normal'] or 0 numerator = moderately_underweight + severely_underweight values_to_calculate_average.append(numerator * 100 / (weighed or 1)) moderately_underweight_total += moderately_underweight severely_underweight_total += severely_underweight normal_total += normal all_total += total weighed_total += weighed data_for_map[on_map_name][ 'severely_underweight'] += severely_underweight data_for_map[on_map_name][ 'moderately_underweight'] += moderately_underweight data_for_map[on_map_name]['normal'] += normal data_for_map[on_map_name]['total'] += total data_for_map[on_map_name]['weighed'] += weighed data_for_map[on_map_name]['original_name'].append(name) for data_for_location in six.itervalues(data_for_map): numerator = data_for_location[ 'moderately_underweight'] + data_for_location[ 'severely_underweight'] value = numerator * 100 / (data_for_location['weighed'] or 1) if value < 20: data_for_location.update({'fillKey': '0%-20%'}) elif 20 <= value < 35: data_for_location.update({'fillKey': '20%-35%'}) elif value >= 35: data_for_location.update({'fillKey': '35%-100%'}) fills = OrderedDict() fills.update({'0%-20%': MapColors.PINK}) fills.update({'20%-35%': MapColors.ORANGE}) fills.update({'35%-100%': MapColors.RED}) fills.update({'defaultFill': MapColors.GREY}) average = ((sum(values_to_calculate_average)) / float(len(values_to_calculate_average) or 1)) gender_label, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval='0 - 5 years') return { "slug": "moderately_underweight", "label": "Percent of Children{gender} Underweight ({age})".format( gender=gender_label, age=age_label), "fills": fills, "rightLegend": { "average": average, "info": _(("Of the total children enrolled for Anganwadi services and weighed, the percentage of " "children between {} years who were moderately/severely underweight in the current month. " "<br/><br/>" "Children who are moderately or severely underweight have a higher risk of mortality." .format(age_label))), "extended_info": [{ 'indicator': 'Total Children{} weighed in given month:'.format( chosen_filters), 'value': indian_formatted_number(weighed_total) }, { 'indicator': 'Number of children unweighed{}:'.format(chosen_filters), 'value': indian_formatted_number(all_total - weighed_total) }, { 'indicator': '% Severely Underweight{}:'.format(chosen_filters), 'value': '%.2f%%' % (severely_underweight_total * 100 / float(weighed_total or 1)) }, { 'indicator': '% Moderately Underweight{}:'.format(chosen_filters), 'value': '%.2f%%' % (moderately_underweight_total * 100 / float(weighed_total or 1)) }, { 'indicator': '% Normal{}:'.format(chosen_filters), 'value': '%.2f%%' % (normal_total * 100 / float(weighed_total or 1)) }] }, "data": dict(data_for_map) }
def get_early_initiation_breastfeeding_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter( **filters ).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level ).annotate( birth=Sum('bf_at_birth'), in_month=Sum('born_in_month'), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset data_for_map, in_month_total, birth_total, average, total = generate_data_for_map( get_data_for(config), loc_level, 'birth', 'in_month', 20, 60 ) fills = OrderedDict() fills.update({'0%-20%': MapColors.RED}) fills.update({'20%-60%': MapColors.ORANGE}) fills.update({'60%-100%': MapColors.PINK}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels(config) return { "slug": "early_initiation", "label": "Percent Early Initiation of Breastfeeding{}".format(chosen_filters), "fills": fills, "rightLegend": { "average": average, "info": _(( "Percentage of children who were put to the breast within one hour of birth." "<br/><br/>" "Early initiation of breastfeeding ensure the newborn recieves the 'first milk' rich in " "nutrients and encourages exclusive breastfeeding practice" )), "extended_info": [ { 'indicator': 'Total Number of Children born in the given month{}:'.format(chosen_filters), 'value': indian_formatted_number(in_month_total) }, { 'indicator': ( 'Total Number of Children who were put to the breast within one hour of birth{}:' .format(chosen_filters) ), 'value': indian_formatted_number(birth_total) }, { 'indicator': '% children who were put to the breast within one hour of ' 'birth{}:'.format(chosen_filters), 'value': '%.2f%%' % (birth_total * 100 / float(in_month_total or 1)) } ] }, "data": dict(data_for_map), }
def get_prevalence_of_severe_data_map(domain, config, loc_level, show_test=False, icds_feature_flag=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter( **filters ).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level ).annotate( moderate=Sum(wasting_moderate_column(icds_feature_flag)), severe=Sum(wasting_severe_column(icds_feature_flag)), normal=Sum(wasting_normal_column(icds_feature_flag)), total_height_eligible=Sum('height_eligible'), total_weighed=Sum('nutrition_status_weighed'), total_measured=Sum(wfh_recorded_in_month_column(icds_feature_flag)), ).order_by('%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) if 'age_tranche' not in config: queryset = queryset.exclude(age_tranche=72) return queryset data_for_map = defaultdict(lambda: { 'moderate': 0, 'severe': 0, 'normal': 0, 'total_weighed': 0, 'total_measured': 0, 'total_height_eligible': 0, 'original_name': [] }) severe_for_all_locations = 0 moderate_for_all_locations = 0 normal_for_all_locations = 0 weighed_for_all_locations = 0 measured_for_all_locations = 0 height_eligible_for_all_locations = 0 values_to_calculate_average = {'numerator': 0, 'denominator': 0} for row in get_data_for(config): total_weighed = row['total_weighed'] or 0 total_height_eligible = row['total_height_eligible'] or 0 name = row['%s_name' % loc_level] on_map_name = row['%s_map_location_name' % loc_level] or name severe = row['severe'] or 0 moderate = row['moderate'] or 0 normal = row['normal'] or 0 total_measured = row['total_measured'] or 0 values_to_calculate_average['numerator'] += moderate if moderate else 0 values_to_calculate_average['numerator'] += severe if severe else 0 values_to_calculate_average['denominator'] += total_measured if total_measured else 0 severe_for_all_locations += severe moderate_for_all_locations += moderate normal_for_all_locations += normal weighed_for_all_locations += total_weighed measured_for_all_locations += total_measured height_eligible_for_all_locations += total_height_eligible data_for_map[on_map_name]['severe'] += severe data_for_map[on_map_name]['moderate'] += moderate data_for_map[on_map_name]['normal'] += normal data_for_map[on_map_name]['total_weighed'] += total_weighed data_for_map[on_map_name]['total_measured'] += total_measured data_for_map[on_map_name]['total_height_eligible'] += total_height_eligible data_for_map[on_map_name]['original_name'].append(name) for data_for_location in six.itervalues(data_for_map): numerator = data_for_location['moderate'] + data_for_location['severe'] value = numerator * 100 / (data_for_location['total_measured'] or 1) if value < 5: data_for_location.update({'fillKey': '0%-5%'}) elif 5 <= value <= 7: data_for_location.update({'fillKey': '5%-7%'}) elif value > 7: data_for_location.update({'fillKey': '7%-100%'}) fills = OrderedDict() fills.update({'0%-5%': MapColors.PINK}) fills.update({'5%-7%': MapColors.ORANGE}) fills.update({'7%-100%': MapColors.RED}) fills.update({'defaultFill': MapColors.GREY}) gender_label, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval=default_age_interval(icds_feature_flag) ) average = ( (values_to_calculate_average['numerator'] * 100) / float(values_to_calculate_average['denominator'] or 1) ) return { "slug": "severe", "label": "Percent of Children{gender} Wasted ({age})".format( gender=gender_label, age=age_label ), "fills": fills, "rightLegend": { "average": "%.2f" % average, "info": wasting_help_text(age_label), "extended_info": [ { 'indicator': 'Total Children{} weighed in given month:'.format(chosen_filters), 'value': indian_formatted_number(weighed_for_all_locations) }, { 'indicator': 'Total Children{} with height measured in given month:' .format(chosen_filters), 'value': indian_formatted_number(measured_for_all_locations) }, { 'indicator': 'Number of children{} unmeasured:'.format(chosen_filters), 'value': indian_formatted_number(height_eligible_for_all_locations - weighed_for_all_locations) }, { 'indicator': '% Severely Acute Malnutrition{}:'.format(chosen_filters), 'value': '%.2f%%' % (severe_for_all_locations * 100 / float(measured_for_all_locations or 1)) }, { 'indicator': '% Moderately Acute Malnutrition{}:'.format(chosen_filters), 'value': '%.2f%%' % (moderate_for_all_locations * 100 / float(measured_for_all_locations or 1)) }, { 'indicator': '% Normal{}:'.format(chosen_filters), 'value': '%.2f%%' % (normal_for_all_locations * 100 / float(measured_for_all_locations or 1)) } ] }, "data": dict(data_for_map), }
def get_newborn_with_low_birth_weight_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter(**filters).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level).annotate(low_birth=Sum('low_birth_weight_in_month'), in_month=Sum('weighed_and_born_in_month'), all=Sum('born_in_month')).order_by( '%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset data_for_map, in_month_total, low_birth_total, average, total = generate_data_for_map( get_data_for(config), loc_level, 'low_birth', 'in_month', 20, 60, 'all') fills = OrderedDict() fills.update({'0%-20%': MapColors.PINK}) fills.update({'20%-60%': MapColors.ORANGE}) fills.update({'60%-100%': MapColors.RED}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_ignored, chosen_filters = chosen_filters_to_labels( config) return { "slug": "low_birth", "label": "Percent Newborns with Low Birth Weight{}".format(chosen_filters), "fills": fills, "rightLegend": { "average": average, "info": _((new_born_with_low_weight_help_text(html=True))), "extended_info": [{ 'indicator': 'Total Number of Newborns born in given month{}:'.format( chosen_filters), 'value': indian_formatted_number(total) }, { 'indicator': 'Number of Newborns with LBW in given month{}:'.format( chosen_filters), 'value': indian_formatted_number(low_birth_total) }, { 'indicator': 'Total Number of children born and weight in given month{}:'. format(chosen_filters), 'value': indian_formatted_number(in_month_total) }, { 'indicator': '% newborns with LBW in given month{}:'.format(chosen_filters), 'value': '%.2f%%' % (low_birth_total * 100 / float(in_month_total or 1)) }, { 'indicator': '% of children with weight in normal{}:'.format( chosen_filters), 'value': '%.2f%%' % ((in_month_total - low_birth_total) * 100 / float(in_month_total or 1)) }, { 'indicator': '% Unweighted{}:'.format(chosen_filters), 'value': '%.2f%%' % ((total - in_month_total) * 100 / float(total or 1)) }] }, "data": dict(data_for_map), }
def get_enrolled_children_data_map(domain, config, loc_level, show_test=False): def get_data_for(filters): filters['month'] = datetime(*filters['month']) queryset = AggChildHealthMonthly.objects.filter(**filters).values( '%s_name' % loc_level, '%s_map_location_name' % loc_level).annotate( valid=Sum('valid_in_month'), all=Sum('valid_all_registered_in_month')).order_by( '%s_name' % loc_level, '%s_map_location_name' % loc_level) if not show_test: queryset = apply_exclude(domain, queryset) return queryset data_for_map = defaultdict(lambda: { 'valid': 0, 'all': 0, 'original_name': [], 'fillKey': 'Children' }) average = [] total_valid = 0 total = 0 for row in get_data_for(config): valid = row['valid'] or 0 name = row['%s_name' % loc_level] all_children = row['all'] or 0 on_map_name = row['%s_map_location_name' % loc_level] or name average.append(valid) total_valid += valid total += all_children data_for_map[on_map_name]['valid'] += valid data_for_map[on_map_name]['all'] += all_children data_for_map[on_map_name]['original_name'].append(name) fills = OrderedDict() fills.update({'Children': MapColors.BLUE}) fills.update({'defaultFill': MapColors.GREY}) gender_ignored, age_label, chosen_filters = chosen_filters_to_labels( config, default_interval='0 - 6 years') return { "slug": "enrolled_children", "label": "", "fills": fills, "rightLegend": { "average": sum(average) / float(len(average) or 1), "average_format": 'number', "info": _(("Total number of children between the age of ({}) who are enrolled for ICDS services" .format(age_label))), "extended_info": [{ 'indicator': 'Number of children{} who are enrolled for ICDS services:'. format(chosen_filters), 'value': indian_formatted_number(total_valid) }, { 'indicator': ('Total number of children{} who are registered: '.format( chosen_filters)), 'value': indian_formatted_number(total) }, { 'indicator': ('Percentage of registered children{} who are enrolled for ICDS services:' .format(chosen_filters)), 'value': '%.2f%%' % (total_valid * 100 / float(total or 1)) }] }, "data": dict(data_for_map), }