Fix Default Reports and Analyses

locale/en_US/LC_MESSAGES/django.po

@@ -120,7 +120,7 @@ msgid "ANALYSIS_DESCRIPTION_BSyncr"
 msgstr "The BSyncr analysis leverages the Normalized Metered Energy Consumption (NMEC) analysis to calculate a change point model. The data are passed to the analysis using BuildingSync. The result of the analysis are the coefficients of the change point model."
 
 msgid "ANALYSIS_DESCRIPTION_BuildingUpgradeRecommendation"
-msgstr "The Building Upgrade Recommendation analysis implements a workflow to identify buildings that may need a deep energy retrofit, equipment replaced or re-tuning based on building attributes such as energy use, year built, and square footage."
+msgstr "The Building Upgrade Recommendation analysis implements a workflow to identify buildings that may need a deep energy retrofit, equipment replaced or re-tuning based on building attributes such as energy use, year built, and square footage. If your organization contains elements, the Element Statistics Analysis should be run prior to running this analysis."
 
 msgid "ANALYSIS_DESCRIPTION_CO2"
 msgstr "This analysis calculates the average annual CO2 emissions for the property's meter data. The analysis requires an eGRID Subregion to be defined in order to accurately determine the emission rates."
@@ -132,7 +132,7 @@ msgid "ANALYSIS_DESCRIPTION_EUI"
 msgstr "The EUI analysis will sum the property's meter readings for the last twelve months to calculate the energy use per square footage per year. If there are missing meter readings, then the analysis will return a less that 100% coverage to alert the user that there is a missing meter reading."
 
 msgid "ANALYSIS_DESCRIPTION_ElementStatistics"
-msgstr "The Element Statistics analysis looks through a property's element data (if any) to count the number of elements of type 'D.D.C. Control Panel' and saves that quantity to the property"
+msgstr "The Element Statistics analysis looks through a property's element data (if any) to count the number of elements of type 'D.D.C. Control Panel'. It also generates the aggregated (average) condition index values for scope 1 emission elements and saves those quantities to the property."
 
 msgid "AND"
 msgstr "AND"
@@ -517,6 +517,9 @@ msgstr "Building Performance Standard (BPS) Compliance Pathway Support"
 msgid "Building Square Footage Threshold"
 msgstr "Building Square Footage Threshold"
 
+msgid "Building has BAS field"
+msgstr "Building has BAS field"
+
 msgid "BuildingSync Recommended Measures"
 msgstr "BuildingSync Recommended Measures"
 
@@ -1814,6 +1817,9 @@ msgstr "Goal Setup"
 msgid "Gross Floor Area"
 msgstr "Gross Floor Area"
 
+msgid "HAS_BAS_HELP"
+msgstr "Select the field that indicates whether or not the building has a Building Automation System (BAS). This analysis expects a boolean field."
+
 msgid "HOW THE SYSTEM AUTO-MATCHES YOUR PROPERTIES AND TAX LOTS:"
 msgstr "HOW THE SYSTEM AUTO-MATCHES YOUR PROPERTIES AND TAX LOTS:"
 

locale/es/LC_MESSAGES/django.po

@@ -5,7 +5,7 @@ msgstr ""
 "Content-Transfer-Encoding: 8bit\n"
 "X-Generator: lokalise.com\n"
 "Project-Id-Version: SEED Platform\n"
-"PO-Revision-Date: 2024-11-14 21:09\n"
+"PO-Revision-Date: 2024-11-25 06:35\n"
 "Last-Translator: lokalise.com\n"
 "Language-Team: lokalise.com\n\n"
 "Language: es\n"
@@ -153,9 +153,8 @@ msgstr "El análisis BETTER aprovecha better.lbl.gov para calcular el ahorro de
 msgid "ANALYSIS_DESCRIPTION_BSyncr"
 msgstr "El análisis BSyncr aprovecha el análisis de consumo energético medido normalizado (NMEC) para calcular un modelo de punto de cambio. Los datos se transmiten al análisis mediante BuildingSync. El resultado del análisis son los coeficientes del modelo de punto de cambio."
 
-#, fuzzy
 msgid "ANALYSIS_DESCRIPTION_BuildingUpgradeRecommendation"
-msgstr "El análisis de recomendaciones de mejora de edificios implementa un flujo de trabajo para identificar los edificios que pueden necesitar una profunda modernización energética, la sustitución de equipos o una nueva puesta a punto basada en atributos del edificio como el uso de la energía, el año de construcción y los metros cuadrados."
+msgstr "El análisis de recomendación de mejora de edificios implementa un flujo de trabajo para identificar los edificios que pueden necesitar una renovación energética profunda, el reemplazo o la puesta a punto de equipos en función de los atributos del edificio, como el uso de energía, el año de construcción y la superficie en metros cuadrados. Si su organización contiene elementos, se debe ejecutar el análisis de estadísticas de elementos antes de ejecutar este análisis."
 
 #, fuzzy
 msgid "ANALYSIS_DESCRIPTION_CO2"
@@ -169,9 +168,8 @@ msgstr "El análisis EEEJ utiliza la dirección de cada propiedad para identific
 msgid "ANALYSIS_DESCRIPTION_EUI"
 msgstr "El análisis EUI sumará las lecturas de los contadores de la propiedad de los últimos doce meses para calcular el consumo de energía por metro cuadrado al año. Si faltan lecturas de contadores, el análisis devolverá una cobertura inferior al 100% para alertar al usuario de que falta una lectura de contador."
 
-#, fuzzy
 msgid "ANALYSIS_DESCRIPTION_ElementStatistics"
-msgstr "El análisis de estadísticas de elementos examina los datos de los elementos de una propiedad (si los hay) para contar el número de elementos de tipo \"Panel de control D.D.C.\" y guarda esa cantidad en la propiedad"
+msgstr "El análisis de estadísticas de elementos analiza los datos de elementos de una propiedad (si los hay) para contar la cantidad de elementos del tipo \"Panel de control DDC\". También genera los valores de índice de condición agregados (promedio) para los elementos de emisión de alcance 1 y guarda esas cantidades en la propiedad."
 
 #, fuzzy
 msgid "AND"
@@ -672,6 +670,9 @@ msgstr "Apoyo para el cumplimiento de las normas de rendimiento de edificios (BP
 msgid "Building Square Footage Threshold"
 msgstr "Umbral de metros cuadrados del edificio"
 
+msgid "Building has BAS field"
+msgstr "El edificio tiene campo BAS"
+
 #, fuzzy
 msgid "BuildingSync Recommended Measures"
 msgstr "BuildingSync Medidas recomendadas"
@@ -2370,6 +2371,9 @@ msgstr "Configuración de la meta"
 msgid "Gross Floor Area"
 msgstr "Superficie bruta"
 
+msgid "HAS_BAS_HELP"
+msgstr "Seleccione el campo que indica si el edificio tiene o no un sistema de automatización de edificios (BAS). Este análisis espera un campo booleano."
+
 #, fuzzy
 msgid "HOW THE SYSTEM AUTO-MATCHES YOUR PROPERTIES AND TAX LOTS:"
 msgstr "CÓMO EL SISTEMA EMPAREJA AUTOMÁTICAMENTE SUS PROPIEDADES Y LOTES FISCALES:"

locale/fr_CA/LC_MESSAGES/django.po

@@ -120,9 +120,8 @@ msgstr "L'analyse BETTER s'appuie sur better.lbl.gov pour calculer les économie
 msgid "ANALYSIS_DESCRIPTION_BSyncr"
 msgstr "L'analyse BSyncr exploite l'analyse de consommation d'énergie normalisée mesurée (NMEC) pour calculer un modèle de point de changement. Les données sont transmises à l'analyse à l'aide de BuildingSync. Le résultat de l'analyse sont les coefficients du modèle de point de changement."
 
-#, fuzzy
 msgid "ANALYSIS_DESCRIPTION_BuildingUpgradeRecommendation"
-msgstr "Cette analyse met en œuvre un flux de travail pour identifier les bâtiments qui peuvent nécessiter une rénovation énergétique en profondeur, un remplacement ou un réajustement de l'équipement en fonction des attributs du bâtiment tels que la consommation d'énergie, l'année de construction et la superficie en pieds carrés."
+msgstr "L'analyse des recommandations de mise à niveau des bâtiments met en œuvre un flux de travail pour identifier les bâtiments qui peuvent nécessiter une rénovation énergétique approfondie, un remplacement de l'équipement ou un réajustement en fonction des attributs du bâtiment tels que la consommation d'énergie, l'année de construction et la superficie en pieds carrés. Si votre organisation contient des éléments, l'analyse des statistiques des éléments doit être exécutée avant d'exécuter cette analyse."
 
 msgid "ANALYSIS_DESCRIPTION_CO2"
 msgstr "Cette analyse calcule les émissions annuelles moyennes de CO2 pour les données des compteurs de la propriété. L’analyse nécessite la définition d’une sous-région eGRID afin de déterminer avec précision les taux d’émission."
@@ -135,7 +134,7 @@ msgid "ANALYSIS_DESCRIPTION_EUI"
 msgstr "L'analyse EUI additionnera les relevés de compteurs de la propriété au cours des douze derniers mois pour calculer la consommation d'énergie par pied carré et par an. S'il manque des relevés de compteur, l'analyse renvoie une couverture inférieure à 100 % pour alerter l'utilisateur qu'il manque un relevé de compteur."
 
 msgid "ANALYSIS_DESCRIPTION_ElementStatistics"
-msgstr "Cette analyse examine les données des éléments d'une propriété (le cas échéant) pour compter le nombre d'éléments de type « Panneau de configuration DDC » et enregistre cette quantité dans la propriété"
+msgstr "L'analyse des statistiques des éléments examine les données des éléments d'une propriété (le cas échéant) pour compter le nombre d'éléments de type « Panneau de contrôle DDC ». Elle génère également les valeurs d'indice de condition agrégées (moyennes) pour les éléments d'émission de portée 1 et enregistre ces quantités dans la propriété."
 
 msgid "AND"
 msgstr "ET"
@@ -523,6 +522,9 @@ msgstr "Assistance au parcours de conformité aux normes de performance des bât
 msgid "Building Square Footage Threshold"
 msgstr "Seuil de superficie en pieds carrés du bâtiment"
 
+msgid "Building has BAS field"
+msgstr "Le bâtiment dispose d'un champ SAB"
+
 msgid "BuildingSync Recommended Measures"
 msgstr "Mesures recommandées par BuildingSync"
 
@@ -1830,6 +1832,9 @@ msgstr "Configuration des objectifs"
 msgid "Gross Floor Area"
 msgstr "Surface brute"
 
+msgid "HAS_BAS_HELP"
+msgstr "Sélectionnez le champ qui indique si le bâtiment est équipé ou non d'un système d'automatisation de bâtiment (SAB). Cette analyse attend un champ booléen."
+
 msgid "HOW THE SYSTEM AUTO-MATCHES YOUR PROPERTIES AND TAX LOTS:"
 msgstr "COMMENT LE SYSTÈME ADAPTE AUTOMATIQUEMENT VOS PROPRIETES ET LOTS D'IMPÔT:"
 

seed/analysis_pipelines/element_statistics.py

@@ -8,7 +8,7 @@
 
 from celery import chain, shared_task
 from django.db import connection
-from django.db.models import Count, Q
+from django.db.models import BooleanField, Count, Q
 
 from seed.analysis_pipelines.pipeline import (
     AnalysisPipeline,
@@ -115,9 +115,10 @@ def _run_analysis(self, analysis_property_view_ids, analysis_id):
         # add ddc count by property id
         if ddc_count_column:
             ddc_count = ddc_count_by_property_id.get(property_view.property_id, 0)
-            property_view.state.extra_data[ddc_count_column.column_name] = ddc_count
-            if ddc_count:
-                analysis_property_view.parsed_results[ddc_count_column.column_name] = ddc_count
+            # convert to true/false
+            has_ddc = bool(ddc_count > 0)
+            property_view.state.extra_data[ddc_count_column.column_name] = has_ddc
+            analysis_property_view.parsed_results[ddc_count_column.column_name] = has_ddc
 
         # add mean condition index by code
         for code, col in existing_columns_names_by_code.items():
@@ -172,7 +173,7 @@ def _create_element_columns(analysis):
 def _create_ddc_count_column(analysis):
     try:
         return Column.objects.get(
-            column_name="Number of D.D.C Control Panels",
+            column_name="Has D.D.C Control Panels",
             organization=analysis.organization,
             table_name="PropertyState",
         )
@@ -181,12 +182,13 @@ def _create_ddc_count_column(analysis):
         if analysis.can_create():
             column = Column.objects.create(
                 is_extra_data=True,
-                column_name="Number of D.D.C Control Panels",
+                column_name="Has D.D.C Control Panels",
                 organization=analysis.organization,
                 table_name="PropertyState",
+                data_type=BooleanField,
             )
-            column.display_name = "Number of D.D.C Control Panels"
-            column.column_description = "Number of D.D.C Control Panels"
+            column.display_name = "Has D.D.C Control Panels"
+            column.column_description = "Has D.D.C Control Panels"
             column.save()
 
             return column

seed/analysis_pipelines/upgrade_recommendation.py

@@ -7,7 +7,6 @@
 import logging
 
 from celery import chain, shared_task
-from django.db.models import Count, Q
 from pint import Quantity
 
 from seed.analysis_pipelines.pipeline import (
@@ -91,6 +90,7 @@ def get_value(name):
     target_gas_eui = get_value("target_gas_eui")
     target_electric_eui = get_value("target_electric_eui")
     condition_index = get_value("condition_index")
+    has_bas = get_value("has_bas")
     year_built = property_view.state.year_built
     gross_floor_area = property_view.state.gross_floor_area
     elements = Element.objects.filter(property=property_view.property_id)
@@ -124,16 +124,17 @@ def get_value(name):
     # if young building:
     retrofit_threshold_year = config.get("year_built_threshold")
     if year_built > retrofit_threshold_year:
+        # comment this out: switched to a boolean field instead of a count
         # if has BAS and actual to benchmark eui ratio is "fair"
-        ddc_control_panel_count = elements.annotate(
-            ddc_control_panel_count=Count("id", filter=Q(extra_data__Component_SubType="D.D.C. Control Panel"))
-        )
-        has_bas = False
-        if ddc_control_panel_count:
-            has_bas = ddc_control_panel_count.order_by("ddc_control_panel_count").first().ddc_control_panel_count > 0
+        # ddc_control_panel_count = elements.annotate(
+        #     ddc_control_panel_count=Count("id", filter=Q(extra_data__Component_SubType="D.D.C. Control Panel"))
+        # )
+        # has_bas = False
+        # if ddc_control_panel_count:
+        #     has_bas = ddc_control_panel_count.order_by("ddc_control_panel_count").first().ddc_control_panel_count > 0
 
         fair_actual_to_benchmark_eui_ratio = config.get("fair_actual_to_benchmark_eui_ratio")
-        if ((eui / benchmark) > fair_actual_to_benchmark_eui_ratio) and has_bas:
+        if ((eui / benchmark) > fair_actual_to_benchmark_eui_ratio) and has_bas is True:
             return "Re-tuning"
         else:
             return "NO DER project recommended"

seed/models/analyses.py

@@ -183,7 +183,13 @@ def get_highlights(self, property_id=None):
 
         # Element Statistics
         elif self.service == self.ELEMENTSTATISTICS:
-            return [{"name": k, "value": round(v, 2)} for k, v in results.items()]
+            res = []
+            for k, v in results.items():
+                if isinstance(v, str):
+                    res.append({"name": k, "value": v})
+                else:
+                    res.append({"name": k, "value": round(v, 2)})
+            return res
         # Building Upgrade Recommendation
         elif self.service == self.UPGRADERECOMMENDATION:
             recommendation = results.get("Building Upgrade Recommendation")

seed/models/filter_group.py

@@ -38,15 +38,13 @@ def views(self, views, columns=[]):
             )
         if not columns:
             columns = Column.retrieve_all(org_id=self.organization_id, inventory_type=related_model, only_used=False, include_related=False)
-
         if self.query_dict:
             qd = QueryDict(mutable=True)
             qd.update(self.query_dict)
 
-            filters, _annotations, _order_by = build_view_filters_and_sorts(
-                qd, columns, related_model, self.organization.access_level_names
-            )
-            filtered_views = views.filter(filters)
+            filters, annotations, _order_by = build_view_filters_and_sorts(qd, columns, related_model, self.organization.access_level_names)
+
+            filtered_views = views.annotate(**annotations).filter(filters)
         else:
             filtered_views = views
 

seed/static/seed/js/controllers/inventory_detail_analyses_controller.js

@@ -18,6 +18,7 @@ angular.module('SEED.controller.inventory_detail_analyses', []).controller('inve
   'Notification',
   'uploader_service',
   'cycle_service',
+  'columns',
   // eslint-disable-next-line func-names
   function (
     $state,
@@ -34,7 +35,8 @@ angular.module('SEED.controller.inventory_detail_analyses', []).controller('inve
     analyses_service,
     Notification,
     uploader_service,
-    cycle_service
+    cycle_service,
+    columns
   ) {
     $scope.item_state = inventory_payload.state;
     $scope.inventory_type = $stateParams.inventory_type;
@@ -139,8 +141,9 @@ angular.module('SEED.controller.inventory_detail_analyses', []).controller('inve
           controller: 'inventory_detail_analyses_modal_controller',
           resolve: {
             inventory_ids: () => [$scope.inventory.view_id],
-            cycles: () => cycle_service.get_cycles().then((result) => result.cycles),
+            property_columns: () => columns.filter((x) => x.table_name === 'PropertyState'),
             current_cycle: () => $scope.cycle,
+            cycles: () => cycle_service.get_cycles().then((result) => result.cycles),
             user: () => $scope.menu.user
           }
         })

seed/static/seed/js/controllers/inventory_detail_analyses_modal_controller.js

@@ -14,19 +14,19 @@ angular.module('SEED.controller.inventory_detail_analyses_modal', []).controller
   'Notification',
   'analyses_service',
   'inventory_ids',
-  'all_columns',
+  'property_columns',
   'current_cycle',
   'cycles',
   'user',
   // eslint-disable-next-line func-names
-  function ($scope, $sce, $log, $uibModalInstance, Notification, analyses_service, inventory_ids, all_columns, current_cycle, cycles, user) {
+  function ($scope, $sce, $log, $uibModalInstance, Notification, analyses_service, inventory_ids, property_columns, current_cycle, cycles, user) {
     $scope.inventory_count = inventory_ids.length;
     // used to disable buttons on submit
     $scope.waiting_for_server = false;
     $scope.cycles = cycles;
     $scope.user = user;
-    $scope.all_columns = all_columns;
-    $scope.eui_columns = $scope.all_columns.filter((o) => o.data_type === 'eui');
+    $scope.property_columns = property_columns;
+    $scope.eui_columns = $scope.property_columns.filter((o) => o.data_type === 'eui');
 
     $scope.new_analysis = {
       name: null,
@@ -125,7 +125,8 @@ angular.module('SEED.controller.inventory_detail_analyses_modal', []).controller
               electric_eui: null,
               target_gas_eui: null,
               target_electric_eui: null,
-              condition_index: null
+              condition_index: null,
+              has_bas: null
             },
             total_eui_goal: null,
             ff_eui_goal: null,

seed/static/seed/js/controllers/inventory_detail_controller.js

@@ -568,6 +568,7 @@ angular.module('SEED.controller.inventory_detail', []).controller('inventory_det
         controller: 'inventory_detail_analyses_modal_controller',
         resolve: {
           inventory_ids: () => [$scope.inventory.view_id],
+          property_columns: () => columns.filter((x) => x.table_name === 'PropertyState'),
           current_cycle: () => $scope.cycle,
           cycles: () => cycle_service.get_cycles().then((result) => result.cycles),
           user: () => $scope.menu.user

seed/static/seed/js/controllers/inventory_list_controller.js

@@ -1643,9 +1643,9 @@ angular.module('SEED.controller.inventory_list', []).controller('inventory_list_
         controller: 'inventory_detail_analyses_modal_controller',
         resolve: {
           inventory_ids: () => ($scope.inventory_type === 'properties' ? selectedViewIds : []),
-          all_columns: () => all_columns.filter((x) => x.table_name === 'PropertyState'),
-          cycles: () => cycles.cycles,
+          property_columns: () => all_columns.filter((x) => x.table_name === 'PropertyState'),
           current_cycle: () => $scope.cycle.selected_cycle,
+          cycles: () => cycles.cycles,
           user: () => $scope.menu.user
         }
       });