diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/.modelkit-config b/residential measures/SWHC049-08 SEER Rated AC HP/.modelkit-config
new file mode 100644
index 000000000..4cc1e8867
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/.modelkit-config	
@@ -0,0 +1,24 @@
+
+# Paths are expanded relative to this modelkit-config file.
+
+prototypes-dir = '../../prototypes/residential'
+templates-dir = '../../templates/energyplus/templates'
+weather-dir = '../../weather'
+codes-dir = '../../codes'
+
+max-workers = 12  # Maximum number of simulations to run in parallel
+
+
+[template-compose]
+annotate = true
+indent = "    "
+esc-line = "! "
+dirs = '~\Documents\Modelkit Caboodle\templates\energyplus\templates'  # Must be an absolute path
+
+[energyplus-run]
+engine = 'C:\EnergyPlusV9-5-0'  # Must be an absolute path
+#engine = '/Applications/EnergyPlus-22-2-0'  # Must be an absolute path
+#engine = '/Applications/EnergyPlus-9-2-0'  # Must be an absolute path
+readvars = true
+keep = false
+output-files = "eplusout.err; eplusout.sql; eplustbl.htm; eplusvar.csv"
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/DEER_EnergyPlus_Modelkit_Measure_list_working_SWHC049-08.xlsx b/residential measures/SWHC049-08 SEER Rated AC HP/DEER_EnergyPlus_Modelkit_Measure_list_working_SWHC049-08.xlsx
new file mode 100644
index 000000000..eef5be61d
Binary files /dev/null and b/residential measures/SWHC049-08 SEER Rated AC HP/DEER_EnergyPlus_Modelkit_Measure_list_working_SWHC049-08.xlsx differ
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/README.md b/residential measures/SWHC049-08 SEER Rated AC HP/README.md
new file mode 100644
index 000000000..375e9aca4
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/README.md	
@@ -0,0 +1,63 @@
+# SWHC049 Ducted AC and HP HVAC Equipment, Residential
+
+## Cohorts and case names
+
+SWHC045 shares some measure case models with SWHC049 but does not have its own subfolder.
+Tables below show the origin of TechIDs present in this folder.
+
+BldgType-BldgVint | Cohort | Prototype root | Introduced
+--- | --- | --- | ---
+DMo | DMo&0&rDXGF&Ex&dxAC_equip | DMo/templates/root.pxt | By DNV for SWHC049 (Dv24)
+DMo | DMo&0&rDXHP&Ex&dxHP_equip | DMo-HP/templates/root.pxt | By DNV for SWHC049 (Dv24)
+DMo | DMo&0&rDXGF&New&dxAC_equip | DMo-DXGF-New/templates/root.pxt | By DNV for SWHC049 (Dv24)
+DMo | DMo&0&rDXHP&New&dxHP_equip | DMo-HP-New/templates/root.pxt | By DNV for SWHC049 (Dv24)
+MFm_Ex | MFm&0&rDXGF&Ex&dxAC_equip | MFm-1985/templates/root_highSEER.pxt | By DNV for SWHC049 (Dv24)
+MFm_Ex | MFm&0&rDXHP&Ex&dxHP_equip | MFm-1985-HP/templates/root.pxt | By DNV for SWHC049 (Dv24)
+MFm_New | MFm&0&rDXGF&New&dxAC_equip | MFm-New/templates/root_highSEER.pxt | By DNV for SWHC049 (Dv24)
+MFm_New | MFm&0&rDXHP&New&dxHP_equip | MFm-New-HP/templates/root_highSEER.pxt | By DNV for SWHC049 (Dv24)
+SFm 1975 | SFm&1&rDXHP&Ex&dxHP_equip | SFm-1 Story-1975-HP/templates/root.pxt | By DNV for SWHC049 (Dv24)
+SFm 1975 | SFm&2&rDXHP&Ex&dxHP_equip | SFm-2 Story-1975-HP/templates/root.pxt | By DNV for SWHC049 (Dv24)
+SFm 1975 | SFm&1&rDXGF&Ex&dxAC_equip | SFm-1 Story-1975/templates/root.pxt | By DNV for SWHC049 (Dv24)
+SFm 1975 | SFm&2&rDXGF&Ex&dxAC_equip | SFm-2 Story-1975/templates/root.pxt | By DNV for SWHC049 (Dv24)
+SFm 1985 | SFm&1&rDXHP&Ex&dxHP_equip | SFm-1 Story-1985-HP/templates/root.pxt | By DNV for SWHC049 (Dv24)
+SFm 1985 | SFm&2&rDXHP&Ex&dxHP_equip | SFm-2 Story-1985-HP/templates/root.pxt | By DNV for SWHC049 (Dv24)
+SFm 1985 | SFm&1&rDXGF&Ex&dxAC_equip | SFm-1 Story-1985/templates/root.pxt | By DNV for SWHC049 (Dv24)
+SFm 1985 | SFm&2&rDXGF&Ex&dxAC_equip | SFm-2 Story-1985/templates/root.pxt | By DNV for SWHC049 (Dv24)
+SFm New | SFm&1&rDXGF&New&dxAC_equip | SFm-1 Story-New/templates/root.pxt | By DNV for SWHC049 (Dv24)
+SFm New | SFm&2&rDXGF&New&dxAC_equip | SFm-2 Story-New/templates/root.pxt | By DNV for SWHC049 (Dv24)
+SFm New | SFm&1&rDXHP&New&dxHP_equip | SFm-1 Story-New-HP/templates/root.pxt | By DNV for SWHC049 (Dv24)
+SFm New | SFm&2&rDXHP&New&dxHP_equip | SFm-2 Story-New-HP/templates/root.pxt | By DNV for SWHC049 (Dv24)
+
+Representative Cohort | Case | Applicable building types | Applicable vintages | Used in measure | Comments
+--- | --- | --- | --- | --- | ---
+DMo&0&rDXGF&Ex&dxAC_equip | dxAC-Res-SEER-13.0 | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXGF&Ex&dxAC_equip | dxAC-Res-SEER-14.0 | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXGF&Ex&dxAC_equip | dxAC-Res-SEER-14.5 | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXGF&Ex&dxAC_equip | dxAC-Res-SEER-15.0 | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXGF&Ex&dxAC_equip | dxAC-Res-SEER-16.0 | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXGF&Ex&dxAC_equip | dxAC-Res-SEER-17.0 | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXGF&Ex&dxAC_equip | dxAC-Res-SEER-18.0 | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXGF&Ex&dxAC_equip | dxAC-Res-SEER-19.0 | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXGF&Ex&dxAC_equip | dxAC-Res-SEER-20.0 | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXGF&Ex&dxAC_equip | dxAC-Res-SEER-21.0 | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXGF&Ex&dxAC_equip | Res-GasFurnace-AFUE80-ECM | DMo, SFm, MFm | Ex | SWHC045 | Added by Solaris Technical for SWHC045
+DMo&0&rDXHP&Ex&dxHP_equip | HSPF_7p0_SEER_13_Pre | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXHP&Ex&dxHP_equip | HSPF_8p0_SEER_14_Std | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXHP&Ex&dxHP_equip | HSPF_8p2_SEER_14.5_Std | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXHP&Ex&dxHP_equip | HSPF_8p8_SEER_15_Std | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXHP&Ex&dxHP_equip | HSPF_9p0_SEER_16_Msr | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXHP&Ex&dxHP_equip | HSPF_9p4_SEER_17_Msr | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXHP&Ex&dxHP_equip | HSPF_9p5_SEER_18_Msr | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXHP&Ex&dxHP_equip | HSPF_9p5_SEER_19_Msr | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXHP&Ex&dxHP_equip | HSPF_10p0_SEER_20_Msr | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXHP&Ex&dxHP_equip | HSPF_10p5_SEER_21_Msr | DMo, SFm, MFm | Ex, New | SWHC049 | Base case for SWHC049. Added by DNV.
+DMo&0&rDXHP&Ex&dxHP_equip | Res-GasFurnace-AFUE80-ECM | DMo, SFm, MFm | Ex | SWHC045 | Added by Solaris Technical for SWHC045
+
+## Query file for normalizing units
+
+The file `query_swhc049.txt` includes queries for cooling and heating capacity for the models generated in this folder. After running simulations, the user may gather outputs using the following command:
+
+```
+cd "residential measures/SWHC049-08 SEER Rated AC HP"
+python result2.py -q query_swhc049.txt
+```
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/cases/DMo&0&rDXGF&Ex&dxAC_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/cases/DMo&0&rDXGF&Ex&dxAC_equip.csv
new file mode 100644
index 000000000..8c7c2befd
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/cases/DMo&0&rDXGF&Ex&dxAC_equip.csv	
@@ -0,0 +1,12 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:burner_eff
+,dxAC-Res-SEER-13.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.5,,0.12,0.24,3.43,,,unitary_system
+,dxAC-Res-SEER-15.0,,0.12,0.24,3.58,,,unitary_system
+,dxAC-Res-SEER-16.0,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system
+,dxAC-Res-SEER-17.0,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system
+,dxAC-Res-SEER-18.0,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system
+,dxAC-Res-SEER-19.0,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system
+,dxAC-Res-SEER-20.0,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system
+,dxAC-Res-SEER-21.0,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system
+,Res-GasFurnace-AFUE80-ECM,,0.12,0.24,,,,,0.8
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/cases/DMo&0&rDXGF&New&dxAC_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/cases/DMo&0&rDXGF&New&dxAC_equip.csv
new file mode 100644
index 000000000..8c7c2befd
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/cases/DMo&0&rDXGF&New&dxAC_equip.csv	
@@ -0,0 +1,12 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:burner_eff
+,dxAC-Res-SEER-13.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.5,,0.12,0.24,3.43,,,unitary_system
+,dxAC-Res-SEER-15.0,,0.12,0.24,3.58,,,unitary_system
+,dxAC-Res-SEER-16.0,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system
+,dxAC-Res-SEER-17.0,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system
+,dxAC-Res-SEER-18.0,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system
+,dxAC-Res-SEER-19.0,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system
+,dxAC-Res-SEER-20.0,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system
+,dxAC-Res-SEER-21.0,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system
+,Res-GasFurnace-AFUE80-ECM,,0.12,0.24,,,,,0.8
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/cases/DMo&0&rDXHP&Ex&dxHP_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/cases/DMo&0&rDXHP&Ex&dxHP_equip.csv
new file mode 100644
index 000000000..2e046a4d1
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/cases/DMo&0&rDXHP&Ex&dxHP_equip.csv	
@@ -0,0 +1,11 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:heating_coil_cop,:burner_eff
+,HSPF_7p0_SEER_13_Pre,,0.12,0.24,3.23,,,unitary_system,2.05
+,HSPF_8p0_SEER_14_Std,,0.12,0.24,3.23,,,unitary_system,2.34
+,HSPF_8p2_SEER_14.5_Std,,0.12,0.24,3.43,,,unitary_system,2.4
+,HSPF_8p8_SEER_15_Std,,0.12,0.24,3.58,,,unitary_system,2.58
+,HSPF_9p0_SEER_16_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system,2.64
+,HSPF_9p4_SEER_17_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system,2.75
+,HSPF_9p5_SEER_18_Msr,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system,2.79
+,HSPF_9p5_SEER_19_Msr,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system,2.79
+,HSPF_10p0_SEER_20_Msr,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system,2.92
+,HSPF_10p5_SEER_21_Msr,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system,3.09
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/cases/DMo&0&rDXHP&New&dxHP_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/cases/DMo&0&rDXHP&New&dxHP_equip.csv
new file mode 100644
index 000000000..fe7340439
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/cases/DMo&0&rDXHP&New&dxHP_equip.csv	
@@ -0,0 +1,10 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:heating_coil_cop,:burner_eff
+,HSPF_8p0_SEER_14_Std,,0.12,0.24,3.23,,,unitary_system,2.34
+,HSPF_8p2_SEER_14.5_Std,,0.12,0.24,3.43,,,unitary_system,2.4
+,HSPF_8p8_SEER_15_Std,,0.12,0.24,3.58,,,unitary_system,2.58
+,HSPF_9p0_SEER_16_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system,2.64
+,HSPF_9p4_SEER_17_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system,2.75
+,HSPF_9p5_SEER_18_Msr,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system,2.79
+,HSPF_9p5_SEER_19_Msr,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system,2.79
+,HSPF_10p0_SEER_20_Msr,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system,2.92
+,HSPF_10p5_SEER_21_Msr,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system,3.09
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/climates.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/climates.csv
new file mode 100644
index 000000000..3ed717387
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/climates.csv	
@@ -0,0 +1,17 @@
+skip,climate,hz_cz,weather_file,climate_zone,codes_file
+,CZ01,,CA_EUREKA_725940S_CZ2022.epw,1,deer-dmo-1972-1985.csv
+,CZ02,,CA_NAPA-CO_724955S_CZ2022.epw,2,deer-dmo-1972-1985.csv
+,CZ03,,CA_OAKLAND-METRO-AP_724930S_CZ2022.epw,3,deer-dmo-1972-1985.csv
+,CZ04,,CA_SAN-JOSE-IAP_724945S_CZ2022.epw,4,deer-dmo-1972-1985.csv
+,CZ05,,CA_SANTA-MARIA-PUBLIC-AP_723940S_CZ2022.epw,5,deer-dmo-1972-1985.csv
+,CZ06,,CA_LOS-ANGELES-IAP_722950S_CZ2022.epw,6,deer-dmo-1972-1985.csv
+,CZ07,,CA_SAN-DIEGO-LINDBERGH-FLD_722900S_CZ2022.epw,7,deer-dmo-1972-1985.csv
+,CZ08,,CA_LONG-BEACH-DAUGHERTY-FLD_722970S_CZ2022.epw,8,deer-dmo-1972-1985.csv
+,CZ09,,CA_LOS-ANGELES-DOWNTOWN-USC_722874S_CZ2022.epw,9,deer-dmo-1972-1985.csv
+,CZ10,,CA_RIVERSIDE-MUNI_722869S_CZ2022.epw,10,deer-dmo-1972-1985.csv
+,CZ11,,CA_RED-BLUFF-MUNI-AP_725910S_CZ2022.epw,11,deer-dmo-1972-1985.csv
+,CZ12,,CA_STOCKTON-METRO-AP_724920S_CZ2022.epw,12,deer-dmo-1972-1985.csv
+,CZ13,,CA_FRESNO-YOSEMITE-IAP_723890S_CZ2022.epw,13,deer-dmo-1972-1985.csv
+,CZ14,,CA_DAGGETT-BARSTOW-AP_723815S_CZ2022.epw,14,deer-dmo-1972-1985.csv
+,CZ15,,CA_EL-CENTRO-NAF_722810S_CZ2022.epw,15,deer-dmo-1972-1985.csv
+,CZ16,,CA_BISHOP-AP_724800S_CZ2022.epw,16,deer-dmo-1972-1985.csv
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/cohorts.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/cohorts.csv
new file mode 100644
index 000000000..4483d33e1
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/cohorts.csv	
@@ -0,0 +1,5 @@
+skip,cohort,root,:wall_consol_layer_thickness,:wall_consol_layer_conductivity,:floor_consol_layer_thickness,:floor_consol_layer_conductivity,:ceil_consol_layer_thickness,:ceil_consol_layer_conductivity,:door_thermal_resistance,:climate_zone,:infiltration_ACH,:shading_control,:cool_coil_capacity,:heating_coil_capacity
+,DMo&0&rDXGF&Ex&dxAC_equip,DMo/templates/root.pxt,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""floor_consol_layer_thickness"", ""Value"")['m']",,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""door_thermal_resistance"", ""R-Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Infiltration"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""shading_control"", ""Value"")",,
+,DMo&0&rDXHP&Ex&dxHP_equip,Dmo-HP/templates/root.pxt,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""floor_consol_layer_thickness"", ""Value"")['m']",,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""door_thermal_resistance"", ""R-Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Infiltration"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""shading_control"", ""Value"")",,
+,DMo&0&rDXGF&New&dxAC_equip,Dmo-DXGF-New/templates/root.pxt,,,,,,,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")",,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""shading_control"", ""Value"")",,
+,DMo&0&rDXHP&New&dxHP_equip,Dmo-HP-New/templates/root.pxt,,,,,,,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")",,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""shading_control"", ""Value"")",,
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/query.txt b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/query.txt
new file mode 100644
index 000000000..b7a670dfb
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo/query.txt	
@@ -0,0 +1,29 @@
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Energy Per Total Building Area/Net Site Energy, Net Site EUI
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Energy Per Total Building Area/Net Source Energy, Net Source EUI
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Total Energy/Net Site Energy
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Total Energy/Net Source Energy
+
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Total End Uses, Total
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heating, Heating
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Cooling, Cooling
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Interior Lighting, Interior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Exterior Lighting, Exterior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Interior Equipment, Interior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Exterior Equipment, Exterior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Fans, Fans
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Pumps, Pumps
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heat Rejection, Heat Rejection
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Humidification, Humidification
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heat Recovery, Heat Recovery
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Water Systems, Water Systems
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Refrigeration, Refrigeration
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Generators, Generators
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Heating, Heating Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Cooling, Cooling Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Heating, Heating NG
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Cooling, Cooling NG
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Interior Equipment, Interior Equipment Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Interior Equipment, Interior Equipment NG
+
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Comfort and Setpoint Not Met Summary/Facility/Time Setpoint Not Met During Occupied Heating, Unmet Heating
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Comfort and Setpoint Not Met Summary/Facility/Time Setpoint Not Met During Occupied Cooling, Unmet Cooling
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_Ex/cases/MFm&0&rDXGF&Ex&dxAC_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_Ex/cases/MFm&0&rDXGF&Ex&dxAC_equip.csv
new file mode 100644
index 000000000..8c7c2befd
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_Ex/cases/MFm&0&rDXGF&Ex&dxAC_equip.csv	
@@ -0,0 +1,12 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:burner_eff
+,dxAC-Res-SEER-13.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.5,,0.12,0.24,3.43,,,unitary_system
+,dxAC-Res-SEER-15.0,,0.12,0.24,3.58,,,unitary_system
+,dxAC-Res-SEER-16.0,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system
+,dxAC-Res-SEER-17.0,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system
+,dxAC-Res-SEER-18.0,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system
+,dxAC-Res-SEER-19.0,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system
+,dxAC-Res-SEER-20.0,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system
+,dxAC-Res-SEER-21.0,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system
+,Res-GasFurnace-AFUE80-ECM,,0.12,0.24,,,,,0.8
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_Ex/cases/MFm&0&rDXHP&Ex&dxHP_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_Ex/cases/MFm&0&rDXHP&Ex&dxHP_equip.csv
new file mode 100644
index 000000000..2e046a4d1
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_Ex/cases/MFm&0&rDXHP&Ex&dxHP_equip.csv	
@@ -0,0 +1,11 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:heating_coil_cop,:burner_eff
+,HSPF_7p0_SEER_13_Pre,,0.12,0.24,3.23,,,unitary_system,2.05
+,HSPF_8p0_SEER_14_Std,,0.12,0.24,3.23,,,unitary_system,2.34
+,HSPF_8p2_SEER_14.5_Std,,0.12,0.24,3.43,,,unitary_system,2.4
+,HSPF_8p8_SEER_15_Std,,0.12,0.24,3.58,,,unitary_system,2.58
+,HSPF_9p0_SEER_16_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system,2.64
+,HSPF_9p4_SEER_17_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system,2.75
+,HSPF_9p5_SEER_18_Msr,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system,2.79
+,HSPF_9p5_SEER_19_Msr,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system,2.79
+,HSPF_10p0_SEER_20_Msr,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system,2.92
+,HSPF_10p5_SEER_21_Msr,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system,3.09
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_Ex/climates.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_Ex/climates.csv
new file mode 100644
index 000000000..d7a1be524
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_Ex/climates.csv	
@@ -0,0 +1,17 @@
+skip,climate,hz_cz,weather_file,climate_zone,codes_file
+,CZ01,,CA_EUREKA_725940S_CZ2022.epw,1,deer-mfm-1985.csv
+,CZ02,,CA_NAPA-CO_724955S_CZ2022.epw,2,deer-mfm-1985.csv
+,CZ03,,CA_OAKLAND-METRO-AP_724930S_CZ2022.epw,3,deer-mfm-1985.csv
+,CZ04,,CA_SAN-JOSE-IAP_724945S_CZ2022.epw,4,deer-mfm-1985.csv
+,CZ05,,CA_SANTA-MARIA-PUBLIC-AP_723940S_CZ2022.epw,5,deer-mfm-1985.csv
+,CZ06,,CA_LOS-ANGELES-IAP_722950S_CZ2022.epw,6,deer-mfm-1985.csv
+,CZ07,,CA_SAN-DIEGO-LINDBERGH-FLD_722900S_CZ2022.epw,7,deer-mfm-1985.csv
+,CZ08,,CA_LONG-BEACH-DAUGHERTY-FLD_722970S_CZ2022.epw,8,deer-mfm-1985.csv
+,CZ09,,CA_LOS-ANGELES-DOWNTOWN-USC_722874S_CZ2022.epw,9,deer-mfm-1985.csv
+,CZ10,,CA_RIVERSIDE-MUNI_722869S_CZ2022.epw,10,deer-mfm-1985.csv
+,CZ11,,CA_RED-BLUFF-MUNI-AP_725910S_CZ2022.epw,11,deer-mfm-1985.csv
+,CZ12,,CA_STOCKTON-METRO-AP_724920S_CZ2022.epw,12,deer-mfm-1985.csv
+,CZ13,,CA_FRESNO-YOSEMITE-IAP_723890S_CZ2022.epw,13,deer-mfm-1985.csv
+,CZ14,,CA_DAGGETT-BARSTOW-AP_723815S_CZ2022.epw,14,deer-mfm-1985.csv
+,CZ15,,CA_EL-CENTRO-NAF_722810S_CZ2022.epw,15,deer-mfm-1985.csv
+,CZ16,,CA_BISHOP-AP_724800S_CZ2022.epw,16,deer-mfm-1985.csv
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_Ex/cohorts.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_Ex/cohorts.csv
new file mode 100644
index 000000000..301b00b02
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_Ex/cohorts.csv	
@@ -0,0 +1,3 @@
+skip,cohort,root,:glass_u,:glass_shgc,:wall_consol_layer_thickness,:wall_consol_layer_conductivity,:ceil_consol_layer_thickness,:ceil_consol_layer_conductivity,:heating_sch_DEER_mrn,:heating_sch_DEER_day,:heating_sch_DEER_eve,:heating_sch_DEER_nit,:cooling_sch_DEER_mrn,:cooling_sch_DEER_day,:cooling_sch_DEER_eve,:cooling_sch_DEER_nit,:climate_zone,:infiltration_ACH,:total_leakage_ratio,:shading_control
+,MFm&0&rDXGF&Ex&dxAC_equip,MFm-1985/templates/root_highSEER.pxt,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Infiltration"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""total_leakage_ratio"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""shading_control"", ""Value"")"
+,MFm&0&rDXHP&Ex&dxHP_equip,MFm-1985-HP/templates/root.pxt,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Infiltration"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""total_leakage_ratio"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""shading_control"", ""Value"")"
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_Ex/query.txt b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_Ex/query.txt
new file mode 100644
index 000000000..b7a670dfb
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_Ex/query.txt	
@@ -0,0 +1,29 @@
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Energy Per Total Building Area/Net Site Energy, Net Site EUI
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Energy Per Total Building Area/Net Source Energy, Net Source EUI
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Total Energy/Net Site Energy
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Total Energy/Net Source Energy
+
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Total End Uses, Total
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heating, Heating
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Cooling, Cooling
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Interior Lighting, Interior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Exterior Lighting, Exterior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Interior Equipment, Interior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Exterior Equipment, Exterior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Fans, Fans
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Pumps, Pumps
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heat Rejection, Heat Rejection
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Humidification, Humidification
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heat Recovery, Heat Recovery
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Water Systems, Water Systems
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Refrigeration, Refrigeration
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Generators, Generators
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Heating, Heating Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Cooling, Cooling Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Heating, Heating NG
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Cooling, Cooling NG
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Interior Equipment, Interior Equipment Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Interior Equipment, Interior Equipment NG
+
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Comfort and Setpoint Not Met Summary/Facility/Time Setpoint Not Met During Occupied Heating, Unmet Heating
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Comfort and Setpoint Not Met Summary/Facility/Time Setpoint Not Met During Occupied Cooling, Unmet Cooling
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New/cases/MFm&0&rDXGF&New&dxAC_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New/cases/MFm&0&rDXGF&New&dxAC_equip.csv
new file mode 100644
index 000000000..8c7c2befd
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New/cases/MFm&0&rDXGF&New&dxAC_equip.csv	
@@ -0,0 +1,12 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:burner_eff
+,dxAC-Res-SEER-13.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.5,,0.12,0.24,3.43,,,unitary_system
+,dxAC-Res-SEER-15.0,,0.12,0.24,3.58,,,unitary_system
+,dxAC-Res-SEER-16.0,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system
+,dxAC-Res-SEER-17.0,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system
+,dxAC-Res-SEER-18.0,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system
+,dxAC-Res-SEER-19.0,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system
+,dxAC-Res-SEER-20.0,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system
+,dxAC-Res-SEER-21.0,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system
+,Res-GasFurnace-AFUE80-ECM,,0.12,0.24,,,,,0.8
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New/cases/MFm&0&rDXHP&New&dxHP_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New/cases/MFm&0&rDXHP&New&dxHP_equip.csv
new file mode 100644
index 000000000..fe7340439
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New/cases/MFm&0&rDXHP&New&dxHP_equip.csv	
@@ -0,0 +1,10 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:heating_coil_cop,:burner_eff
+,HSPF_8p0_SEER_14_Std,,0.12,0.24,3.23,,,unitary_system,2.34
+,HSPF_8p2_SEER_14.5_Std,,0.12,0.24,3.43,,,unitary_system,2.4
+,HSPF_8p8_SEER_15_Std,,0.12,0.24,3.58,,,unitary_system,2.58
+,HSPF_9p0_SEER_16_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system,2.64
+,HSPF_9p4_SEER_17_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system,2.75
+,HSPF_9p5_SEER_18_Msr,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system,2.79
+,HSPF_9p5_SEER_19_Msr,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system,2.79
+,HSPF_10p0_SEER_20_Msr,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system,2.92
+,HSPF_10p5_SEER_21_Msr,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system,3.09
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New/climates.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New/climates.csv
new file mode 100644
index 000000000..92c625f0e
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New/climates.csv	
@@ -0,0 +1,17 @@
+skip,climate,hz_cz,weather_file,climate_zone,codes_file
+,CZ01,,CA_EUREKA_725940S_CZ2022.epw,1,T24_2025_MFm.csv
+,CZ02,,CA_NAPA-CO_724955S_CZ2022.epw,2,T24_2025_MFm.csv
+,CZ03,,CA_OAKLAND-METRO-AP_724930S_CZ2022.epw,3,T24_2025_MFm.csv
+,CZ04,,CA_SAN-JOSE-IAP_724945S_CZ2022.epw,4,T24_2025_MFm.csv
+,CZ05,,CA_SANTA-MARIA-PUBLIC-AP_723940S_CZ2022.epw,5,T24_2025_MFm.csv
+,CZ06,,CA_LOS-ANGELES-IAP_722950S_CZ2022.epw,6,T24_2025_MFm.csv
+,CZ07,,CA_SAN-DIEGO-LINDBERGH-FLD_722900S_CZ2022.epw,7,T24_2025_MFm.csv
+,CZ08,,CA_LONG-BEACH-DAUGHERTY-FLD_722970S_CZ2022.epw,8,T24_2025_MFm.csv
+,CZ09,,CA_LOS-ANGELES-DOWNTOWN-USC_722874S_CZ2022.epw,9,T24_2025_MFm.csv
+,CZ10,,CA_RIVERSIDE-MUNI_722869S_CZ2022.epw,10,T24_2025_MFm.csv
+,CZ11,,CA_RED-BLUFF-MUNI-AP_725910S_CZ2022.epw,11,T24_2025_MFm.csv
+,CZ12,,CA_STOCKTON-METRO-AP_724920S_CZ2022.epw,12,T24_2025_MFm.csv
+,CZ13,,CA_FRESNO-YOSEMITE-IAP_723890S_CZ2022.epw,13,T24_2025_MFm.csv
+,CZ14,,CA_DAGGETT-BARSTOW-AP_723815S_CZ2022.epw,14,T24_2025_MFm.csv
+,CZ15,,CA_EL-CENTRO-NAF_722810S_CZ2022.epw,15,T24_2025_MFm.csv
+,CZ16,,CA_BISHOP-AP_724800S_CZ2022.epw,16,T24_2025_MFm.csv
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New/cohorts.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New/cohorts.csv
new file mode 100644
index 000000000..d8ad4c28f
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New/cohorts.csv	
@@ -0,0 +1,3 @@
+skip,cohort,root,:glass_u,:glass_shgc,:wall_consol_layer_thickness,:wall_consol_layer_conductivity,:wall_insulation_layer_thickness,:wall_insulation_layer_conductivity,:ceil_consol_layer_thickness,:ceil_consol_layer_conductivity,:climate_zone,:slab_perimeter_insulation,:roofing,:below_roof_deck_insulation,:radiant_barrier,:duct_r_value
+,MFm&0&rDXGF&New&dxAC_equip,MFm-New/templates/root_highSEER.pxt,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_insulation_layer_thickness"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_insulation_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Slab Floor"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Roofing"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Below Roof Deck Insulation"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Radiant Barrier"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Duct Insulation"", ""Value"")['R-IP']"
+,MFm&0&rDXHP&New&dxHP_equip,MFm-New-HP/templates/root_highSEER.pxt,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_insulation_layer_thickness"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_insulation_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Slab Floor"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Roofing"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Below Roof Deck Insulation"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Radiant Barrier"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Duct Insulation"", ""Value"")['R-IP']"
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New/query.txt b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New/query.txt
new file mode 100644
index 000000000..b7a670dfb
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New/query.txt	
@@ -0,0 +1,29 @@
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Energy Per Total Building Area/Net Site Energy, Net Site EUI
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Energy Per Total Building Area/Net Source Energy, Net Source EUI
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Total Energy/Net Site Energy
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Total Energy/Net Source Energy
+
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Total End Uses, Total
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heating, Heating
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Cooling, Cooling
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Interior Lighting, Interior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Exterior Lighting, Exterior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Interior Equipment, Interior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Exterior Equipment, Exterior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Fans, Fans
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Pumps, Pumps
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heat Rejection, Heat Rejection
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Humidification, Humidification
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heat Recovery, Heat Recovery
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Water Systems, Water Systems
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Refrigeration, Refrigeration
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Generators, Generators
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Heating, Heating Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Cooling, Cooling Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Heating, Heating NG
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Cooling, Cooling NG
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Interior Equipment, Interior Equipment Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Interior Equipment, Interior Equipment NG
+
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Comfort and Setpoint Not Met Summary/Facility/Time Setpoint Not Met During Occupied Heating, Unmet Heating
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Comfort and Setpoint Not Met Summary/Facility/Time Setpoint Not Met During Occupied Cooling, Unmet Cooling
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/cases/SFm&1&rDXGF&Ex&dxAC_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/cases/SFm&1&rDXGF&Ex&dxAC_equip.csv
new file mode 100644
index 000000000..8c7c2befd
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/cases/SFm&1&rDXGF&Ex&dxAC_equip.csv	
@@ -0,0 +1,12 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:burner_eff
+,dxAC-Res-SEER-13.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.5,,0.12,0.24,3.43,,,unitary_system
+,dxAC-Res-SEER-15.0,,0.12,0.24,3.58,,,unitary_system
+,dxAC-Res-SEER-16.0,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system
+,dxAC-Res-SEER-17.0,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system
+,dxAC-Res-SEER-18.0,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system
+,dxAC-Res-SEER-19.0,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system
+,dxAC-Res-SEER-20.0,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system
+,dxAC-Res-SEER-21.0,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system
+,Res-GasFurnace-AFUE80-ECM,,0.12,0.24,,,,,0.8
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/cases/SFm&1&rDXHP&Ex&dxHP_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/cases/SFm&1&rDXHP&Ex&dxHP_equip.csv
new file mode 100644
index 000000000..2e046a4d1
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/cases/SFm&1&rDXHP&Ex&dxHP_equip.csv	
@@ -0,0 +1,11 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:heating_coil_cop,:burner_eff
+,HSPF_7p0_SEER_13_Pre,,0.12,0.24,3.23,,,unitary_system,2.05
+,HSPF_8p0_SEER_14_Std,,0.12,0.24,3.23,,,unitary_system,2.34
+,HSPF_8p2_SEER_14.5_Std,,0.12,0.24,3.43,,,unitary_system,2.4
+,HSPF_8p8_SEER_15_Std,,0.12,0.24,3.58,,,unitary_system,2.58
+,HSPF_9p0_SEER_16_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system,2.64
+,HSPF_9p4_SEER_17_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system,2.75
+,HSPF_9p5_SEER_18_Msr,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system,2.79
+,HSPF_9p5_SEER_19_Msr,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system,2.79
+,HSPF_10p0_SEER_20_Msr,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system,2.92
+,HSPF_10p5_SEER_21_Msr,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system,3.09
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/cases/SFm&2&rDXGF&Ex&dxAC_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/cases/SFm&2&rDXGF&Ex&dxAC_equip.csv
new file mode 100644
index 000000000..8c7c2befd
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/cases/SFm&2&rDXGF&Ex&dxAC_equip.csv	
@@ -0,0 +1,12 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:burner_eff
+,dxAC-Res-SEER-13.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.5,,0.12,0.24,3.43,,,unitary_system
+,dxAC-Res-SEER-15.0,,0.12,0.24,3.58,,,unitary_system
+,dxAC-Res-SEER-16.0,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system
+,dxAC-Res-SEER-17.0,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system
+,dxAC-Res-SEER-18.0,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system
+,dxAC-Res-SEER-19.0,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system
+,dxAC-Res-SEER-20.0,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system
+,dxAC-Res-SEER-21.0,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system
+,Res-GasFurnace-AFUE80-ECM,,0.12,0.24,,,,,0.8
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/cases/SFm&2&rDXHP&Ex&dxHP_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/cases/SFm&2&rDXHP&Ex&dxHP_equip.csv
new file mode 100644
index 000000000..2e046a4d1
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/cases/SFm&2&rDXHP&Ex&dxHP_equip.csv	
@@ -0,0 +1,11 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:heating_coil_cop,:burner_eff
+,HSPF_7p0_SEER_13_Pre,,0.12,0.24,3.23,,,unitary_system,2.05
+,HSPF_8p0_SEER_14_Std,,0.12,0.24,3.23,,,unitary_system,2.34
+,HSPF_8p2_SEER_14.5_Std,,0.12,0.24,3.43,,,unitary_system,2.4
+,HSPF_8p8_SEER_15_Std,,0.12,0.24,3.58,,,unitary_system,2.58
+,HSPF_9p0_SEER_16_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system,2.64
+,HSPF_9p4_SEER_17_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system,2.75
+,HSPF_9p5_SEER_18_Msr,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system,2.79
+,HSPF_9p5_SEER_19_Msr,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system,2.79
+,HSPF_10p0_SEER_20_Msr,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system,2.92
+,HSPF_10p5_SEER_21_Msr,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system,3.09
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/climates.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/climates.csv
new file mode 100644
index 000000000..9b41ce076
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/climates.csv	
@@ -0,0 +1,10 @@
+skip,climate,hz_cz,weather_file,climate_zone,codes_file
+,CZ01,,CA_EUREKA_725940S_CZ2022.epw,1,deer-sfm-1975-1985.csv
+,CZ02,,CA_NAPA-CO_724955S_CZ2022.epw,2,deer-sfm-1975-1985.csv
+,CZ03,,CA_OAKLAND-METRO-AP_724930S_CZ2022.epw,3,deer-sfm-1975-1985.csv
+,CZ04,,CA_SAN-JOSE-IAP_724945S_CZ2022.epw,4,deer-sfm-1975-1985.csv
+,CZ05,,CA_SANTA-MARIA-PUBLIC-AP_723940S_CZ2022.epw,5,deer-sfm-1975-1985.csv
+,CZ06,,CA_LOS-ANGELES-IAP_722950S_CZ2022.epw,6,deer-sfm-1975-1985.csv
+,CZ07,,CA_SAN-DIEGO-LINDBERGH-FLD_722900S_CZ2022.epw,7,deer-sfm-1975-1985.csv
+,CZ08,,CA_LONG-BEACH-DAUGHERTY-FLD_722970S_CZ2022.epw,8,deer-sfm-1975-1985.csv
+,CZ09,,CA_LOS-ANGELES-DOWNTOWN-USC_722874S_CZ2022.epw,9,deer-sfm-1975-1985.csv
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/cohorts.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/cohorts.csv
new file mode 100644
index 000000000..339adc8f3
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/cohorts.csv	
@@ -0,0 +1,5 @@
+skip,cohort,root,:glass_u,:glass_shgc,:sheathing_consol_layer_thickness,:sheathing_consol_layer_conductivity,:wall_consol_layer_thickness,:wall_consol_layer_conductivity,:floor_consol_layer_thickness,:floor_consol_layer_conductivity,:ceil_consol_layer_thickness,:ceil_consol_layer_conductivity,:heating_sch_DEER_mrn,:heating_sch_DEER_day,:heating_sch_DEER_eve,:heating_sch_DEER_nit,:cooling_sch_DEER_mrn,:cooling_sch_DEER_day,:cooling_sch_DEER_eve,:cooling_sch_DEER_nit,:climate_zone,:infiltration_ACH,:total_leakage_ratio,:shading_control,:floor_abv_crawl_insulation
+,SFm&1&rDXHP&Ex&dxHP_equip,SFm-1 Story-1975-HP/templates/root.pxt,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Infiltration"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""total_leakage_ratio"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""shading_control"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""floor_abv_crawl_insulation"", ""Value"")"
+,SFm&2&rDXHP&Ex&dxHP_equip,SFm-2 Story-1975-HP/templates/root.pxt,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Infiltration"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""total_leakage_ratio"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""shading_control"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""floor_abv_crawl_insulation"", ""Value"")"
+,SFm&1&rDXGF&Ex&dxAC_equip,SFm-1 Story-1975/templates/root.pxt,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Infiltration"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""total_leakage_ratio"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""shading_control"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""floor_abv_crawl_insulation"", ""Value"")"
+,SFm&2&rDXGF&Ex&dxAC_equip,SFm-2 Story-1975/templates/root.pxt,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Infiltration"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""total_leakage_ratio"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""shading_control"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""floor_abv_crawl_insulation"", ""Value"")"
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/query.txt b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/query.txt
new file mode 100644
index 000000000..b7a670dfb
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975/query.txt	
@@ -0,0 +1,29 @@
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Energy Per Total Building Area/Net Site Energy, Net Site EUI
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Energy Per Total Building Area/Net Source Energy, Net Source EUI
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Total Energy/Net Site Energy
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Total Energy/Net Source Energy
+
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Total End Uses, Total
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heating, Heating
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Cooling, Cooling
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Interior Lighting, Interior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Exterior Lighting, Exterior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Interior Equipment, Interior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Exterior Equipment, Exterior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Fans, Fans
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Pumps, Pumps
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heat Rejection, Heat Rejection
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Humidification, Humidification
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heat Recovery, Heat Recovery
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Water Systems, Water Systems
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Refrigeration, Refrigeration
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Generators, Generators
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Heating, Heating Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Cooling, Cooling Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Heating, Heating NG
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Cooling, Cooling NG
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Interior Equipment, Interior Equipment Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Interior Equipment, Interior Equipment NG
+
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Comfort and Setpoint Not Met Summary/Facility/Time Setpoint Not Met During Occupied Heating, Unmet Heating
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Comfort and Setpoint Not Met Summary/Facility/Time Setpoint Not Met During Occupied Cooling, Unmet Cooling
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/cases/SFm&1&rDXGF&Ex&dxAC_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/cases/SFm&1&rDXGF&Ex&dxAC_equip.csv
new file mode 100644
index 000000000..8c7c2befd
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/cases/SFm&1&rDXGF&Ex&dxAC_equip.csv	
@@ -0,0 +1,12 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:burner_eff
+,dxAC-Res-SEER-13.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.5,,0.12,0.24,3.43,,,unitary_system
+,dxAC-Res-SEER-15.0,,0.12,0.24,3.58,,,unitary_system
+,dxAC-Res-SEER-16.0,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system
+,dxAC-Res-SEER-17.0,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system
+,dxAC-Res-SEER-18.0,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system
+,dxAC-Res-SEER-19.0,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system
+,dxAC-Res-SEER-20.0,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system
+,dxAC-Res-SEER-21.0,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system
+,Res-GasFurnace-AFUE80-ECM,,0.12,0.24,,,,,0.8
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/cases/SFm&1&rDXHP&Ex&dxHP_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/cases/SFm&1&rDXHP&Ex&dxHP_equip.csv
new file mode 100644
index 000000000..2e046a4d1
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/cases/SFm&1&rDXHP&Ex&dxHP_equip.csv	
@@ -0,0 +1,11 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:heating_coil_cop,:burner_eff
+,HSPF_7p0_SEER_13_Pre,,0.12,0.24,3.23,,,unitary_system,2.05
+,HSPF_8p0_SEER_14_Std,,0.12,0.24,3.23,,,unitary_system,2.34
+,HSPF_8p2_SEER_14.5_Std,,0.12,0.24,3.43,,,unitary_system,2.4
+,HSPF_8p8_SEER_15_Std,,0.12,0.24,3.58,,,unitary_system,2.58
+,HSPF_9p0_SEER_16_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system,2.64
+,HSPF_9p4_SEER_17_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system,2.75
+,HSPF_9p5_SEER_18_Msr,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system,2.79
+,HSPF_9p5_SEER_19_Msr,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system,2.79
+,HSPF_10p0_SEER_20_Msr,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system,2.92
+,HSPF_10p5_SEER_21_Msr,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system,3.09
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/cases/SFm&2&rDXGF&Ex&dxAC_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/cases/SFm&2&rDXGF&Ex&dxAC_equip.csv
new file mode 100644
index 000000000..8c7c2befd
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/cases/SFm&2&rDXGF&Ex&dxAC_equip.csv	
@@ -0,0 +1,12 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:burner_eff
+,dxAC-Res-SEER-13.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.5,,0.12,0.24,3.43,,,unitary_system
+,dxAC-Res-SEER-15.0,,0.12,0.24,3.58,,,unitary_system
+,dxAC-Res-SEER-16.0,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system
+,dxAC-Res-SEER-17.0,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system
+,dxAC-Res-SEER-18.0,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system
+,dxAC-Res-SEER-19.0,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system
+,dxAC-Res-SEER-20.0,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system
+,dxAC-Res-SEER-21.0,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system
+,Res-GasFurnace-AFUE80-ECM,,0.12,0.24,,,,,0.8
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/cases/SFm&2&rDXHP&Ex&dxHP_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/cases/SFm&2&rDXHP&Ex&dxHP_equip.csv
new file mode 100644
index 000000000..2e046a4d1
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/cases/SFm&2&rDXHP&Ex&dxHP_equip.csv	
@@ -0,0 +1,11 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:heating_coil_cop,:burner_eff
+,HSPF_7p0_SEER_13_Pre,,0.12,0.24,3.23,,,unitary_system,2.05
+,HSPF_8p0_SEER_14_Std,,0.12,0.24,3.23,,,unitary_system,2.34
+,HSPF_8p2_SEER_14.5_Std,,0.12,0.24,3.43,,,unitary_system,2.4
+,HSPF_8p8_SEER_15_Std,,0.12,0.24,3.58,,,unitary_system,2.58
+,HSPF_9p0_SEER_16_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system,2.64
+,HSPF_9p4_SEER_17_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system,2.75
+,HSPF_9p5_SEER_18_Msr,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system,2.79
+,HSPF_9p5_SEER_19_Msr,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system,2.79
+,HSPF_10p0_SEER_20_Msr,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system,2.92
+,HSPF_10p5_SEER_21_Msr,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system,3.09
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/climates.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/climates.csv
new file mode 100644
index 000000000..3878aebed
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/climates.csv	
@@ -0,0 +1,8 @@
+skip,climate,hz_cz,weather_file,climate_zone,codes_file
+,CZ10,,CA_RIVERSIDE-MUNI_722869S_CZ2022.epw,10,deer-sfm-1975-1985.csv
+,CZ11,,CA_RED-BLUFF-MUNI-AP_725910S_CZ2022.epw,11,deer-sfm-1975-1985.csv
+,CZ12,,CA_STOCKTON-METRO-AP_724920S_CZ2022.epw,12,deer-sfm-1975-1985.csv
+,CZ13,,CA_FRESNO-YOSEMITE-IAP_723890S_CZ2022.epw,13,deer-sfm-1975-1985.csv
+,CZ14,,CA_DAGGETT-BARSTOW-AP_723815S_CZ2022.epw,14,deer-sfm-1975-1985.csv
+,CZ15,,CA_EL-CENTRO-NAF_722810S_CZ2022.epw,15,deer-sfm-1975-1985.csv
+,CZ16,,CA_BISHOP-AP_724800S_CZ2022.epw,16,deer-sfm-1975-1985.csv
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/cohorts.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/cohorts.csv
new file mode 100644
index 000000000..868116792
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/cohorts.csv	
@@ -0,0 +1,5 @@
+skip,cohort,root,:roof_base_cavity_insul,:wall_base_type,:wall_base_cavity_insul,:wall_base_cont_insul,:glass_u,:glass_shgc,:sheathing_consol_layer_thickness,:sheathing_consol_layer_conductivity,:wall_consol_layer_thickness,:wall_consol_layer_conductivity,:floor_consol_layer_thickness,:floor_consol_layer_conductivity,:ceil_consol_layer_thickness,:ceil_consol_layer_conductivity,:heating_sch_DEER_mrn,:heating_sch_DEER_day,:heating_sch_DEER_eve,:heating_sch_DEER_nit,:cooling_sch_DEER_mrn,:cooling_sch_DEER_day,:cooling_sch_DEER_eve,:cooling_sch_DEER_nit,:climate_zone,:infiltration_ACH,:total_leakage_ratio,:shading_control,:floor_abv_crawl_insulation
+,SFm&1&rDXHP&Ex&dxHP_equip,SFm-1 Story-1985-HP/templates/root.pxt,,,,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Infiltration"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""total_leakage_ratio"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""shading_control"", ""Value"")",
+,SFm&2&rDXHP&Ex&dxHP_equip,SFm-2 Story-1985-HP/templates/root.pxt,,,,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Infiltration"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""total_leakage_ratio"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""shading_control"", ""Value"")",
+,SFm&1&rDXGF&Ex&dxAC_equip,SFm-1 Story-1985/templates/root.pxt,,,,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Infiltration"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""total_leakage_ratio"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""shading_control"", ""Value"")",
+,SFm&2&rDXGF&Ex&dxAC_equip,SFm-2 Story-1985/templates/root.pxt,,,,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")",,,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""heating_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_mrn"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_day"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_eve"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""cooling_sch_DEER_nit"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Infiltration"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""total_leakage_ratio"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""shading_control"", ""Value"")",
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/query.txt b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/query.txt
new file mode 100644
index 000000000..b7a670dfb
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985/query.txt	
@@ -0,0 +1,29 @@
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Energy Per Total Building Area/Net Site Energy, Net Site EUI
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Energy Per Total Building Area/Net Source Energy, Net Source EUI
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Total Energy/Net Site Energy
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Total Energy/Net Source Energy
+
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Total End Uses, Total
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heating, Heating
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Cooling, Cooling
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Interior Lighting, Interior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Exterior Lighting, Exterior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Interior Equipment, Interior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Exterior Equipment, Exterior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Fans, Fans
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Pumps, Pumps
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heat Rejection, Heat Rejection
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Humidification, Humidification
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heat Recovery, Heat Recovery
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Water Systems, Water Systems
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Refrigeration, Refrigeration
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Generators, Generators
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Heating, Heating Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Cooling, Cooling Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Heating, Heating NG
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Cooling, Cooling NG
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Interior Equipment, Interior Equipment Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Interior Equipment, Interior Equipment NG
+
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Comfort and Setpoint Not Met Summary/Facility/Time Setpoint Not Met During Occupied Heating, Unmet Heating
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Comfort and Setpoint Not Met Summary/Facility/Time Setpoint Not Met During Occupied Cooling, Unmet Cooling
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/cases/SFm&1&rDXGF&New&dxAC_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/cases/SFm&1&rDXGF&New&dxAC_equip.csv
new file mode 100644
index 000000000..8c7c2befd
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/cases/SFm&1&rDXGF&New&dxAC_equip.csv	
@@ -0,0 +1,12 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:burner_eff
+,dxAC-Res-SEER-13.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.5,,0.12,0.24,3.43,,,unitary_system
+,dxAC-Res-SEER-15.0,,0.12,0.24,3.58,,,unitary_system
+,dxAC-Res-SEER-16.0,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system
+,dxAC-Res-SEER-17.0,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system
+,dxAC-Res-SEER-18.0,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system
+,dxAC-Res-SEER-19.0,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system
+,dxAC-Res-SEER-20.0,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system
+,dxAC-Res-SEER-21.0,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system
+,Res-GasFurnace-AFUE80-ECM,,0.12,0.24,,,,,0.8
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/cases/SFm&1&rDXHP&New&dxHP_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/cases/SFm&1&rDXHP&New&dxHP_equip.csv
new file mode 100644
index 000000000..fe7340439
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/cases/SFm&1&rDXHP&New&dxHP_equip.csv	
@@ -0,0 +1,10 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:heating_coil_cop,:burner_eff
+,HSPF_8p0_SEER_14_Std,,0.12,0.24,3.23,,,unitary_system,2.34
+,HSPF_8p2_SEER_14.5_Std,,0.12,0.24,3.43,,,unitary_system,2.4
+,HSPF_8p8_SEER_15_Std,,0.12,0.24,3.58,,,unitary_system,2.58
+,HSPF_9p0_SEER_16_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system,2.64
+,HSPF_9p4_SEER_17_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system,2.75
+,HSPF_9p5_SEER_18_Msr,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system,2.79
+,HSPF_9p5_SEER_19_Msr,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system,2.79
+,HSPF_10p0_SEER_20_Msr,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system,2.92
+,HSPF_10p5_SEER_21_Msr,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system,3.09
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/cases/SFm&2&rDXGF&New&dxAC_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/cases/SFm&2&rDXGF&New&dxAC_equip.csv
new file mode 100644
index 000000000..8c7c2befd
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/cases/SFm&2&rDXGF&New&dxAC_equip.csv	
@@ -0,0 +1,12 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:burner_eff
+,dxAC-Res-SEER-13.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.0,,0.12,0.24,3.23,,,unitary_system
+,dxAC-Res-SEER-14.5,,0.12,0.24,3.43,,,unitary_system
+,dxAC-Res-SEER-15.0,,0.12,0.24,3.58,,,unitary_system
+,dxAC-Res-SEER-16.0,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system
+,dxAC-Res-SEER-17.0,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system
+,dxAC-Res-SEER-18.0,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system
+,dxAC-Res-SEER-19.0,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system
+,dxAC-Res-SEER-20.0,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system
+,dxAC-Res-SEER-21.0,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system
+,Res-GasFurnace-AFUE80-ECM,,0.12,0.24,,,,,0.8
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/cases/SFm&2&rDXHP&New&dxHP_equip.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/cases/SFm&2&rDXHP&New&dxHP_equip.csv
new file mode 100644
index 000000000..fe7340439
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/cases/SFm&2&rDXHP&New&dxHP_equip.csv	
@@ -0,0 +1,10 @@
+skip,case_name,:vfd_curve_name,:supplyfan_total_eff,:motor_eff,:cooling_coil_cop,:cooling_coil_cop2,:coil_type,:hvac_type,:heating_coil_cop,:burner_eff
+,HSPF_8p0_SEER_14_Std,,0.12,0.24,3.23,,,unitary_system,2.34
+,HSPF_8p2_SEER_14.5_Std,,0.12,0.24,3.43,,,unitary_system,2.4
+,HSPF_8p8_SEER_15_Std,,0.12,0.24,3.58,,,unitary_system,2.58
+,HSPF_9p0_SEER_16_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.75,multispeed,unitary_system,2.64
+,HSPF_9p4_SEER_17_Msr,VFD_Fan_Curve,0.25,0.5,3.58,3.89,multispeed,unitary_system,2.75
+,HSPF_9p5_SEER_18_Msr,VFD_Fan_Curve,0.25,0.5,3.58,4.01,multispeed,unitary_system,2.79
+,HSPF_9p5_SEER_19_Msr,VFD_Fan_Curve,0.25,0.5,3.72,4.12,multispeed,unitary_system,2.79
+,HSPF_10p0_SEER_20_Msr,VFD_Fan_Curve,0.25,0.5,3.82,4.22,multispeed,unitary_system,2.92
+,HSPF_10p5_SEER_21_Msr,VFD_Fan_Curve,0.25,0.5,3.94,4.31,multispeed,unitary_system,3.09
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/climates.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/climates.csv
new file mode 100644
index 000000000..71328a5b9
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/climates.csv	
@@ -0,0 +1,17 @@
+skip,climate,hz_cz,weather_file,climate_zone,codes_file
+,CZ01,,CA_EUREKA_725940S_CZ2022.epw,1,T24_2025_SFm.csv
+,CZ02,,CA_NAPA-CO_724955S_CZ2022.epw,2,T24_2025_SFm.csv
+,CZ03,,CA_OAKLAND-METRO-AP_724930S_CZ2022.epw,3,T24_2025_SFm.csv
+,CZ04,,CA_SAN-JOSE-IAP_724945S_CZ2022.epw,4,T24_2025_SFm.csv
+,CZ05,,CA_SANTA-MARIA-PUBLIC-AP_723940S_CZ2022.epw,5,T24_2025_SFm.csv
+,CZ06,,CA_LOS-ANGELES-IAP_722950S_CZ2022.epw,6,T24_2025_SFm.csv
+,CZ07,,CA_SAN-DIEGO-LINDBERGH-FLD_722900S_CZ2022.epw,7,T24_2025_SFm.csv
+,CZ08,,CA_LONG-BEACH-DAUGHERTY-FLD_722970S_CZ2022.epw,8,T24_2025_SFm.csv
+,CZ09,,CA_LOS-ANGELES-DOWNTOWN-USC_722874S_CZ2022.epw,9,T24_2025_SFm.csv
+,CZ10,,CA_RIVERSIDE-MUNI_722869S_CZ2022.epw,10,T24_2025_SFm.csv
+,CZ11,,CA_RED-BLUFF-MUNI-AP_725910S_CZ2022.epw,11,T24_2025_SFm.csv
+,CZ12,,CA_STOCKTON-METRO-AP_724920S_CZ2022.epw,12,T24_2025_SFm.csv
+,CZ13,,CA_FRESNO-YOSEMITE-IAP_723890S_CZ2022.epw,13,T24_2025_SFm.csv
+,CZ14,,CA_DAGGETT-BARSTOW-AP_723815S_CZ2022.epw,14,T24_2025_SFm.csv
+,CZ15,,CA_EL-CENTRO-NAF_722810S_CZ2022.epw,15,T24_2025_SFm.csv
+,CZ16,,CA_BISHOP-AP_724800S_CZ2022.epw,16,T24_2025_SFm.csv
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/cohorts.csv b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/cohorts.csv
new file mode 100644
index 000000000..43cb536b5
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/cohorts.csv	
@@ -0,0 +1,5 @@
+skip,cohort,root,:glass_u,:glass_shgc,:wall_consol_layer_thickness,:wall_consol_layer_conductivity,:wall_insulation_layer_thickness,:wall_insulation_layer_conductivity,:ceil_consol_layer_thickness,:ceil_consol_layer_conductivity,:climate_zone,:slab_perimeter_insulation,:roofing,:below_roof_deck_insulation,:radiant_barrier,:whole_house_fan,:duct_r_value
+,SFm&1&rDXGF&New&dxAC_equip,SFm-1 Story-New/templates/root.pxt,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_insulation_layer_thickness"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_insulation_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Slab Floor"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Roofing"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Below Roof Deck Insulation"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Radiant Barrier"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""whole house fan"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Duct Insulation"", ""Value"")['R-IP']"
+,SFm&2&rDXGF&New&dxAC_equip,SFm-2 Story-New/templates/root.pxt,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_insulation_layer_thickness"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_insulation_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Slab Floor"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Roofing"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Below Roof Deck Insulation"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Radiant Barrier"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""whole house fan"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Duct Insulation"", ""Value"")['R-IP']"
+,SFm&1&rDXHP&New&dxHP_equip,SFm-1 Story-New-HP/templates/root.pxt,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_insulation_layer_thickness"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_insulation_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Slab Floor"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Roofing"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Below Roof Deck Insulation"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Radiant Barrier"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""whole house fan"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Duct Insulation"", ""Value"")['R-IP']"
+,SFm&2&rDXHP&New&dxHP_equip,SFm-2 Story-New-HP/templates/root.pxt,"%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration U-Factor"", ""Value"")['U-IP']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Vertical Fenestration SHGC"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_insulation_layer_thickness"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""wall_insulation_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_thickness"", ""Value"")['m']","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""ceil_consol_layer_conductivity"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""climate_zone"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Slab Floor"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Roofing"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Below Roof Deck Insulation"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Radiant Barrier"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""whole house fan"", ""Value"")","%= codes.lookup(""Res Key Prototype Values | Climate Zone #{climate_zone}"", ""Duct Insulation"", ""Value"")['R-IP']"
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/query.txt b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/query.txt
new file mode 100644
index 000000000..b7a670dfb
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New/query.txt	
@@ -0,0 +1,29 @@
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Energy Per Total Building Area/Net Site Energy, Net Site EUI
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Energy Per Total Building Area/Net Source Energy, Net Source EUI
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Total Energy/Net Site Energy
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Total Energy/Net Source Energy
+
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Total End Uses, Total
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heating, Heating
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Cooling, Cooling
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Interior Lighting, Interior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Exterior Lighting, Exterior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Interior Equipment, Interior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Exterior Equipment, Exterior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Fans, Fans
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Pumps, Pumps
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heat Rejection, Heat Rejection
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Humidification, Humidification
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Heat Recovery, Heat Recovery
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Water Systems, Water Systems
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Refrigeration, Refrigeration
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Total Energy/Generators, Generators
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Heating, Heating Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Cooling, Cooling Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Heating, Heating NG
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Cooling, Cooling NG
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Interior Equipment, Interior Equipment Elec
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Interior Equipment, Interior Equipment NG
+
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Comfort and Setpoint Not Met Summary/Facility/Time Setpoint Not Met During Occupied Heating, Unmet Heating
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Comfort and Setpoint Not Met Summary/Facility/Time Setpoint Not Met During Occupied Cooling, Unmet Cooling
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/global.pxv b/residential measures/SWHC049-08 SEER Rated AC HP/global.pxv
new file mode 100644
index 000000000..c707bb604
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/global.pxv	
@@ -0,0 +1,2 @@
+:timestep => 6,
+:run_control => "ANNUAL"  # ANNUAL | SIZING
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/post-process/DMo.py b/residential measures/SWHC049-08 SEER Rated AC HP/post-process/DMo.py
new file mode 100644
index 000000000..3223441f3
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/post-process/DMo.py	
@@ -0,0 +1,647 @@
+#%%
+##STEP 0: Setup (import all necessary libraries)
+import pandas as pd
+import numpy as np
+import os
+import sys
+import datetime as dt
+os.chdir(os.path.dirname(__file__)) #resets to current script directory
+# %%
+#Read master workbook for measure / tech list
+df_master = pd.read_excel('DEER_EnergyPlus_Modelkit_Measure_list_working.xlsx', sheet_name='Measure_list', skiprows=4)
+
+measure_group_names = list(df_master['Measure Group Name'].unique())
+
+# %%
+#generate unique list of measure names
+measures = list(df_master['Measure (general name)'].unique())
+# %%
+#Shows list of measure names 
+print(measures)
+#%%
+#Define measure name here
+measure_name = 'SEER Rated AC HP'
+
+# %%
+#DMo only script
+####Define path
+os.chdir(os.path.dirname(__file__)) #resets to current script directory
+print(os.path.abspath(os.curdir))
+os.chdir("../..") #go up two directory
+print(os.path.abspath(os.curdir))
+
+#12/20/2023 After finishing Com, try to condense Res script so one script takes care of one measure folder?
+#to do: use for loop to loop over each folder, using if-else to process different building types for Res
+path = 'residential measures/SWHC049-06 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_DMo'
+
+# %%
+#extract only the 5th portion of the measure group name for expected_att
+#split argument 4 means only split 4 times maximum
+techgroup_techtypes = [i.split('&', 4)[-1] for i in measure_group_names]
+tech_uniques = list(np.unique((np.array(techgroup_techtypes))))
+# Name parsing function per Amine
+# This dictionary of attributes should be updated
+expected_att = {
+    'BldgType': ['MFm','SFm', 'DMo'],
+    'Story': ['0','1','2'], # NA for Not Applicable
+    'BldgHVAC': ['rDXGF','rDXHP','rNCEH','rNCGF'],
+    'BldgVint': ['Ex','New'],
+    'Measure': tech_uniques
+}
+# function to parse meta data from & delimited case file names (Measure Group Name from master spreadsheet)
+def parse_measure_name(measure_name):
+    #split at most 4 times for 5 descriptor fields
+    measure_name_split = measure_name.split('&', 4) 
+    # Check here if the presented name has 5 attributes as expected:
+    if not len(measure_name_split) == 5:
+        sys.exit('The case name must have at least 5 attributes similar to < BldgType&Story&BldgHVAC&BldgVint&TechGroup__TechType >')
+    
+    attributes = list(expected_att.keys())
+    measure_name_dict = {attributes[i]: measure_name_split[i] for i in range(0,5)}
+
+    # Check here if the presented attributes are as expected:
+    for att in attributes:
+        if measure_name_dict[att] not in expected_att[att]:
+            sys.exit(f'Attribute <{measure_name_dict[att]}> was not expected')
+            
+
+    return measure_name_dict # returns a dictionary
+
+def parse_measure_name2(cohort_names: pd.Series, verify: bool = False) -> pd.DataFrame:
+    '''Returns a DataFrame with five columns (all type string):
+        ["BldgType","Story","BldgHVAC","BldgVint","TechGroup__TechType"]
+    Each cohort name must match the pattern:
+        "BldgType&Story&BldgHVAC&BldgVint&TechGroup__TechType"
+    Only alphanumeric characters are allowed  [a-zA-Z0-9_], except TechGroup__TechType may contain ampersand (&).
+
+    Parameters
+    ----------
+    cohort_names : pandas.Series
+        The cohort names as from cohorts.csv.
+    verify : bool, default=False
+        If true and name parts do not match `expected_att`, raise an exception.
+
+    Returns
+    -------
+    pandas.DataFrame
+        Structure containing the parts of cohort name.
+    '''
+    result = cohort_names.str.extract(
+        r'(?P<BldgType>\w+)&(?P<Story>\w+)&(?P<BldgHVAC>\w+)&(?P<BldgVint>\w+)&(?P<Measure>[^/]+)'
+    )
+    if verify:
+        # Check for missing descriptor fields
+        missing = result.isna()
+        if missing.any().any():
+            example = cohort_names[missing.any(axis=1)].iloc[0]
+            raise ValueError(f'Missing descriptor field, e.g. cohort = "{example}"')
+        # Check for unrecognized fields
+        for attr_name,attr_val in expected_att.items():
+            unrecognized = ~result[attr_name].isin(attr_val)
+            if unrecognized.any():
+                example = result[attr_name][unrecognized].iloc[0]
+                raise ValueError(f'Unrecognized descriptor field, e.g. {attr_name} = "{example}"')
+    result.rename({'Measure':'TechGroup__TechType'},axis=1,inplace=True)
+    return result
+
+#function to melt long 8760 col into 24col x365row format
+def long2wide_pivot(df, name):
+    '''
+    customized function. 
+    input df is long 8760, 1 column format, with the daynum(of365) and hour of day(of24) mapped.
+    id is the unique identifier.
+    
+    output df is the 24col x 365row format of the 8760, with the corresponding id.
+    '''
+    df_wide = df.pivot(index='daynum',columns='Hour', values=df.columns[0]).reset_index().rename_axis('', axis=1)
+    df_wide['ID']=name
+    #df_wide = pd.merge(df_wide, df_ag_key, on='ID')
+    
+    return df_wide
+
+#function to uses the “File Name” column from the results-summary csv to identify directory structure (an organized table) of a batch run 
+# and uses the structure to construct a semi-organized annual outputs table.
+def annual_raw_parsing(df, cohort_dict):
+    #create separated meta data cols
+    df['BldgLoc'] = split_meta_cols_eu[0]
+
+    df['BldgType'] = cohort_dict['BldgType']
+    df['BldgHVAC'] = cohort_dict['BldgHVAC']
+    df['BldgVint'] = cohort_dict['BldgVint']
+
+    df['Story'] = cohort_dict['Story']
+
+    df['TechGroup_TechType'] = cohort_dict['Measure']
+
+    df['TechID'] = split_meta_cols_eu[2]
+    df['file'] = split_meta_cols_eu[3]
+    
+    annual_df_v1 = df[['TechID', 'file', 'BldgLoc', 'BldgType','BldgHVAC','BldgVint','Story', 'TechGroup_TechType','Total (kWh)', 'Heating (kWh)', 'Cooling (kWh)',
+       'Interior Lighting (kWh)', 'Exterior Lighting (kWh)',
+       'Interior Equipment (kWh)', 'Exterior Equipment (kWh)', 'Fans (kWh)',
+       'Pumps (kWh)', 'Heat Rejection (kWh)', 'Humidification (kWh)',
+       'Heat Recovery (kWh)', 'Water Systems (kWh)', 'Refrigeration (kWh)',
+       'Generators (kWh)', 'Heating Elec (kWh)', 'Cooling Elec (kWh)',
+       'Heating NG (kWh)', 'Cooling NG (kWh)', 'Interior Equipment Elec (kWh)',
+       'Interior Equipment NG (kWh)']]
+    
+    return annual_df_v1
+
+#function to merge and rearrange specific annual consumption end-use fields into the format required
+def end_use_rearrange(df_in):
+    df_in['kwh_tot'] = (df_in['Heating Elec (kWh)'] + \
+                            df_in['Cooling Elec (kWh)'] +\
+                            df_in['Interior Equipment Elec (kWh)'] +\
+                            df_in['Interior Lighting (kWh)'] +\
+                            df_in['Exterior Lighting (kWh)'] +\
+                            df_in['Fans (kWh)']+\
+                            df_in['Pumps (kWh)'])
+
+    df_in['kwh_ltg'] = (df_in['Interior Lighting (kWh)'] +\
+                                    df_in['Exterior Lighting (kWh)'])
+
+    df_in['kwh_task'] = 0 # placeholder (task lighting load?)
+
+    df_in['kwh_equip'] = df_in['Interior Equipment Elec (kWh)'] +\
+                                    df_in['Exterior Equipment (kWh)']
+
+    df_in['kwh_htg'] = df_in['Heating Elec (kWh)']
+    df_in['kwh_clg'] = df_in['Cooling Elec (kWh)']
+    df_in['kwh_twr'] = 0 #place holder (tower kwh load?)
+    df_in['kwh_aux'] = 0 #place holder (aux equipment kwh load?)
+
+    df_in['kwh_vent'] = df_in['Fans (kWh)'] #use fan kWh as vent load for now
+
+    df_in['kwh_venthtg'] =0 #placeholders fields for now
+    df_in['kwh_ventclg'] =0
+    df_in['kwh_refg'] = 0 
+    df_in['kwh_hpsup'] = 0
+    df_in['kwh_shw'] = 0
+    df_in['kwh_ext'] = 0
+
+    df_in['thm_tot'] = (df_in['Heating NG (kWh)'] +\
+                                df_in['Cooling NG (kWh)'] +\
+                                df_in['Interior Equipment NG (kWh)'] +\
+                                df_in['Water Systems (kWh)'])/29.3
+
+    df_in['thm_equip'] = df_in['Interior Equipment NG (kWh)']/29.3
+
+    df_in['thm_htg'] = df_in['Heating NG (kWh)']/29.3
+
+    df_in['thm_shw'] = df_in['Water Systems (kWh)']/29.3
+
+    df_in['deskw_ltg'] = 1 #placeholders fields for now
+    df_in['deskw_equ'] = 1
+
+    return df_in
+
+
+# %%
+#create measure specific Master table based on Measure selected
+df_measure = df_master[df_master['Measure (general name)'] == measure_name]
+case_cohort_list = df_measure['Measure Group Name'].unique()
+
+# %%
+##STEP 1: Annual data extraction / transformation
+
+df_raw = pd.read_csv(path+'/results-summary.csv', usecols=['File Name'])
+num_runs = len(df_raw['File Name'].dropna().unique()) - 1
+#Read annual data
+annual_df = pd.read_csv(path+'/results-summary.csv', nrows=num_runs, skiprows=num_runs+2)
+split_meta_cols_eu = annual_df['File Name'].str.split('/', expand=True)
+
+#if looping over multiple folders/cohort cases, use a list
+cohort_cases = list(split_meta_cols_eu[1].unique())
+
+sim_annual_proto = pd.DataFrame()
+for case in cohort_cases:
+    print(f'processing all annual data that are grouped in {case}')
+    cohort_dict = parse_measure_name(case)
+    sim_annual_i = annual_raw_parsing(annual_df[annual_df['File Name'].str.contains(case)].copy(), cohort_dict)
+    sim_annual_proto = pd.concat([sim_annual_proto, sim_annual_i])
+    print('ok.')
+sim_annual_proto = end_use_rearrange(sim_annual_proto)
+sim_annual_v1 = sim_annual_proto[['TechID', 'BldgLoc', 'BldgType', 'BldgHVAC', 'BldgVint', 'kwh_tot', 'kwh_ltg', 'kwh_task',
+    'kwh_equip', 'kwh_htg', 'kwh_clg', 'kwh_twr', 'kwh_aux', 'kwh_vent',
+    'kwh_venthtg', 'kwh_ventclg',
+    'kwh_refg', 'kwh_hpsup', 'kwh_shw', 'kwh_ext', 'thm_tot', 'thm_equip',
+    'thm_htg', 'thm_shw', 'deskw_ltg', 'deskw_equ']].drop_duplicates().copy()
+
+# %%
+##STEP 2: Hourly data extraction / transformation
+#Read 8760 map
+os.chdir(os.path.dirname(__file__)) #resets to current script directory
+print(os.path.abspath(os.curdir))
+annual_map = pd.read_excel('annual8760map.xlsx')
+
+# %%
+os.chdir("../..") #go up two directory
+print(os.path.abspath(os.curdir))
+hrly_path = path + '/runs' 
+
+#extract data per bldgtype-bldghvac-bldgvint group
+hourly_df = pd.DataFrame(index=range(0,8760))
+#extract num_runs / split_meta_cols_eu
+df_raw = pd.read_csv(path+'/'+'/results-summary.csv', usecols=['File Name'])
+num_runs = len(df_raw['File Name'].dropna().unique()) - 1
+annual_df = pd.read_csv(path+'/'+'/results-summary.csv', nrows=num_runs, skiprows=num_runs+2)
+split_meta_cols_eu = annual_df['File Name'].str.split('/', expand=True)
+for i in range(0,num_runs):
+    print(f"merging record {i}")
+    
+    #loop path of each file, read corresponding file
+    full_path = hrly_path + "/" + split_meta_cols_eu.iloc[i][0] + "/" + split_meta_cols_eu.iloc[i][1] + "/" + split_meta_cols_eu.iloc[i][2] + "/instance-var.csv"
+    df = pd.read_csv(full_path, low_memory=False)
+    
+    #remove traling spaces on col headers
+    df.columns = df.columns.str.rstrip()
+
+    #extract the last column (the total elec hrly profile)
+    #if for enduse hourly, then extract the relevant end use column
+    extracted_df = pd.DataFrame(df['Electricity:Facility [J](Hourly)'])
+    
+    #create the column name based on the permutations
+    col_name = split_meta_cols_eu.iloc[i][0] + "/" + split_meta_cols_eu.iloc[i][1] + "/" + split_meta_cols_eu.iloc[i][2] + "/instance-var.csv"
+    
+    #change column name
+    extracted_df = extracted_df.set_axis([col_name],axis=1)
+    if len(extracted_df)!=8760:
+        #8/31/2022 update, need to make the final length 8808. Snip data based on difference to 8760
+        record_count_diff = len(extracted_df) - 8760
+        print(f'extra records: {str(len(extracted_df))}, snipping away {record_count_diff} records and changing to 8760')
+        extracted_df = extracted_df.iloc[record_count_diff:].reset_index(drop=True)
+    
+    #left-merge onto big df
+    hourly_df = hourly_df.merge(extracted_df, left_index=True, right_index=True)
+# %%
+fyr_hrly = hourly_df
+#rearrange 1-column 8760 format to 365x24 wide format for all runs in hourly_df
+converted_df = pd.DataFrame()
+
+for i in range(0,len(fyr_hrly.columns)):
+    
+    #isolate single column
+    hrly_df = pd.DataFrame(fyr_hrly.iloc[:,i])
+    
+    #create separate metadata columns
+    col_names = hrly_df.columns[0].split('/')
+    
+    #create new key column for merge
+    hrly_df['hr in 8760'] = (hrly_df.index) + 1
+    
+    #merge based on "hr in 8760" column, the 8760 map
+    hrly_mapped = pd.merge(hrly_df, annual_map, on='hr in 8760')
+    
+    #transform data format
+    hrly_wide = long2wide_pivot(hrly_mapped, hrly_mapped.columns[0])
+    
+    #add meta data col
+    hrly_wide['BldgLoc'] = col_names[0]
+    hrly_wide['BldgType'] = col_names[1]
+    hrly_wide['TechID'] = col_names[2]
+    hrly_wide['file'] = col_names[3]
+    
+    #append to master df
+    #converted_df = converted_df.append(hrly_wide) #deprecated method
+    converted_df = pd.concat([converted_df, hrly_wide])
+    print(f"col {i} transformed.")
+#%%
+#rearrange columns
+sim_hourly_wb_proto = converted_df[['TechID','file','BldgLoc','BldgType','ID','daynum',1,          2,          3,          4,          5,
+                6,          7,          8,          9,         10,         11,
+            12,         13,         14,         15,         16,         17,
+            18,         19,         20,         21,         22,         23,
+            24]].copy()
+#hourly data conversion
+#convert unit (J) to (kWh) for hourly
+
+sim_hourly_wb_proto['hr01'] = sim_hourly_wb_proto[1]/3600000
+sim_hourly_wb_proto['hr02'] = sim_hourly_wb_proto[2]/3600000
+sim_hourly_wb_proto['hr03'] = sim_hourly_wb_proto[3]/3600000
+sim_hourly_wb_proto['hr04'] = sim_hourly_wb_proto[4]/3600000
+sim_hourly_wb_proto['hr05'] = sim_hourly_wb_proto[5]/3600000
+sim_hourly_wb_proto['hr06'] = sim_hourly_wb_proto[6]/3600000
+sim_hourly_wb_proto['hr07'] = sim_hourly_wb_proto[7]/3600000
+sim_hourly_wb_proto['hr08'] = sim_hourly_wb_proto[8]/3600000
+sim_hourly_wb_proto['hr09'] = sim_hourly_wb_proto[9]/3600000
+sim_hourly_wb_proto['hr10'] = sim_hourly_wb_proto[10]/3600000
+sim_hourly_wb_proto['hr11'] = sim_hourly_wb_proto[11]/3600000
+sim_hourly_wb_proto['hr12'] = sim_hourly_wb_proto[12]/3600000
+sim_hourly_wb_proto['hr13'] = sim_hourly_wb_proto[13]/3600000
+sim_hourly_wb_proto['hr14'] = sim_hourly_wb_proto[14]/3600000
+sim_hourly_wb_proto['hr15'] = sim_hourly_wb_proto[15]/3600000
+sim_hourly_wb_proto['hr16'] = sim_hourly_wb_proto[16]/3600000
+sim_hourly_wb_proto['hr17'] = sim_hourly_wb_proto[17]/3600000
+sim_hourly_wb_proto['hr18'] = sim_hourly_wb_proto[18]/3600000
+sim_hourly_wb_proto['hr19'] = sim_hourly_wb_proto[19]/3600000
+sim_hourly_wb_proto['hr20'] = sim_hourly_wb_proto[20]/3600000
+sim_hourly_wb_proto['hr21'] = sim_hourly_wb_proto[21]/3600000
+sim_hourly_wb_proto['hr22'] = sim_hourly_wb_proto[22]/3600000
+sim_hourly_wb_proto['hr23'] = sim_hourly_wb_proto[23]/3600000
+sim_hourly_wb_proto['hr24'] = sim_hourly_wb_proto[24]/3600000
+
+#rearrange columns
+sim_hourly_wb_v1 = sim_hourly_wb_proto[['TechID','file','BldgLoc','BldgType','ID','daynum','hr01','hr02','hr03','hr04','hr05','hr06',
+        'hr07',     'hr08',     'hr09',     'hr10',     'hr11',     'hr12',
+        'hr13',     'hr14',     'hr15',     'hr16',     'hr17',     'hr18',
+        'hr19',     'hr20',     'hr21',     'hr22',     'hr23',     'hr24']].copy()
+# %%
+##STEP 3: Normalizing Units
+bldgtype = 'DMo'
+os.chdir(os.path.dirname(__file__)) #resets to current script directory
+print(os.path.abspath(os.curdir))
+df_normunits = pd.read_excel('Normunits.xlsx', sheet_name=bldgtype)
+numunits_vals = df_normunits[df_normunits['Normunit'] == df_measure['Normunit'].unique()[0]][['Value', 'Msr']]
+
+if len(numunits_vals) == 1:
+    numunits = list(numunits_vals['Value'])[0]
+elif (measure_name == 'Wall Insulation') or (measure_name == 'Ceiling Insulation'):
+    numunits = list(numunits_vals[numunits_vals['Msr'] == measure_name]['Value'])[0]
+else:
+    pass
+# %%
+##Annual Data final field fixes
+#note normunit = building area (conditioned)
+sim_annual_v1['SizingID'] = 'None'
+sim_annual_v1['tstat'] = 0
+#now Norm unit is read from measure master table
+sim_annual_v1['normunit'] = df_measure['Normunit'].unique()[0]
+#make this automatic as well
+sim_annual_v1['measarea'] = 2484 #from DMo model outputs htmls
+
+#apply normunits where appropriate
+#num unit will be per dwelling, so use roof area / num of dwellings (2 for SFM, DMo, 24 for MFm)
+sim_annual_v1['numunits'] = numunits/2
+sim_annual_v1['lastmod']=dt.datetime.now()
+
+#rearrange columns
+sim_annual_f = sim_annual_v1[['TechID', 'SizingID', 'BldgType','BldgVint','BldgLoc','BldgHVAC','tstat',
+       'normunit', 'numunits', 'measarea', 'kwh_tot', 'kwh_ltg', 'kwh_task',
+       'kwh_equip', 'kwh_htg', 'kwh_clg', 'kwh_twr', 'kwh_aux', 'kwh_vent',
+       'kwh_venthtg', 'kwh_ventclg', 'kwh_refg', 'kwh_hpsup', 'kwh_shw',
+       'kwh_ext', 'thm_tot', 'thm_equip', 'thm_htg', 'thm_shw', 'deskw_ltg',
+       'deskw_equ', 'lastmod']]
+# %%
+##Hourly Data final field fixes
+
+#update field names based on what it contains
+df_tmp = parse_measure_name2(sim_hourly_wb_v1['ID'],verify=True)
+sim_hourly_wb_v1['BldgVint'] = df_tmp['BldgVint']
+sim_hourly_wb_v1['BldgHVAC'] = df_tmp['BldgHVAC']
+sim_hourly_wb_v1['BldgType'] = df_tmp['BldgType']
+del df_tmp
+sim_hourly_wb_v1['SizingID'] = 'None'
+sim_hourly_wb_v1['tstat'] = 0
+sim_hourly_wb_v1['enduse'] = 0
+sim_hourly_wb_v1['lastmod']=dt.datetime.now()
+
+#rearrange columns
+sim_hourly_f = sim_hourly_wb_v1[['TechID', 'SizingID', 'BldgType', 'BldgVint', 'BldgLoc','BldgHVAC','tstat', 'enduse', 'daynum', 
+                                 'hr01', 'hr02', 'hr03', 'hr04', 'hr05', 'hr06', 'hr07', 'hr08', 'hr09', 'hr10', 'hr11',
+                                'hr12', 'hr13', 'hr14', 'hr15', 'hr16', 'hr17', 'hr18', 'hr19', 'hr20',
+                                'hr21', 'hr22', 'hr23', 'hr24', 'lastmod']]
+# %%
+##STEP 4: Measure setup file (current_msr_mat.csv)
+
+# Creating current_msr_mat and finalzing TechID's
+
+metadata_cols = sim_annual_f[['TechID', 'BldgLoc', 'BldgType', 'BldgVint', 'BldgHVAC', 'SizingID',
+       'tstat', 'normunit']]
+
+#check unique TechID cases
+metadata_cols['TechID'].unique()
+# %%
+#TechID identification from Master table
+#if looping over all HVAC types, ignore BldgHVAC filter
+PreTechIDs = df_measure[['PreTechID','Common_PreTechID']].drop_duplicates()
+StdTechIDs = df_measure[['StdTechID','Common_StdTechID']].drop_duplicates()
+MeasTechIDs = df_measure[['MeasTechID','Common_MeasTechID']].drop_duplicates()
+# %%
+#filter out each pre, std, msr using the Common TechIDs from master table
+metadata_pre = metadata_cols[metadata_cols['TechID'].isin(PreTechIDs['Common_PreTechID'].unique())]
+metadata_std = metadata_cols[metadata_cols['TechID'].isin(StdTechIDs['Common_StdTechID'].unique())]
+metadata_msr = metadata_cols[metadata_cols['TechID'].isin(MeasTechIDs['Common_MeasTechID'].unique())]
+
+# %%
+#rename to Pre, Std or Msr 
+#both Std and Pre are baseline for SEER rated AC measures
+metadata_pre = metadata_pre.rename(columns={'TechID':'PreTechID'})
+metadata_std = metadata_std.rename(columns={'TechID':'StdTechID'})
+metadata_msr = metadata_msr.rename(columns={'TechID':'MeasTechID'})
+# %%
+#Changing common TechID to actual TechIDs if needed.
+#might only apply to SEER AC/HP
+
+#create full pre_metadata sets for different names but the same TechID
+# commom_preTechID = PreTechIDs['Common_PreTechID'].unique()[0]
+if False in list(PreTechIDs['PreTechID']==PreTechIDs['Common_PreTechID']):
+    metadata_pre_full = pd.DataFrame()
+    for _, (common_id, new_id) in PreTechIDs[['Common_PreTechID', 'PreTechID']].iterrows():
+        print(f'changing to specific PreTechID {new_id}')
+        metadata_pre_mod = metadata_pre[metadata_pre['PreTechID']==common_id].copy()
+        metadata_pre_mod['PreTechID'] = new_id
+        #merge to final df
+        metadata_pre_full = pd.concat([metadata_pre_full, metadata_pre_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    metadata_pre_full = metadata_pre.copy()
+
+
+# %%
+#create std_metadata sets, assigning appropriate final TechIDs
+if (False in list(StdTechIDs['StdTechID']==StdTechIDs['Common_StdTechID'])):
+    metadata_std_full = pd.DataFrame()
+    for common_id, new_id in zip(StdTechIDs['Common_StdTechID'], StdTechIDs['StdTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Isolate specific common id (old)
+        metadata_std_mod = metadata_std[metadata_std['StdTechID']==common_id].copy()
+        #Change into final techID name (new)
+        metadata_std_mod['StdTechID'] = new_id
+        #merge to final df
+        metadata_std_full = pd.concat([metadata_std_full, metadata_std_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    metadata_std_full = metadata_std.copy()
+# %%
+#create msr_metadata sets, assigning appropriate final TechIDs
+if False in list(MeasTechIDs['MeasTechID']==MeasTechIDs['Common_MeasTechID']):
+    metadata_msr_full = pd.DataFrame()
+    for common_id, new_id in zip(MeasTechIDs['Common_MeasTechID'], MeasTechIDs['MeasTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Identify corresponding common TechID (the last 9 characters indicating SEER levels)
+        metadata_msr_mod = metadata_msr[metadata_msr['MeasTechID']==common_id].copy()
+        #Change into final TechID name
+        metadata_msr_mod['MeasTechID'] = new_id
+        #merge to final df
+        metadata_msr_full = pd.concat([metadata_msr_full, metadata_msr_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    metadata_msr_full = metadata_msr.copy()
+# %%
+#create raw merged current_msr_mat
+#need to delete/drop incorrect sets
+if np.NaN in list(StdTechIDs['StdTechID'].unique()):
+    df_measure_set_full = pd.merge(metadata_pre_full, metadata_msr_full, on=['BldgLoc','BldgType','BldgVint','BldgHVAC','SizingID','tstat','normunit'])
+elif np.NaN in list(PreTechIDs['PreTechID'].unique()):
+    df_measure_set_full = pd.merge(metadata_std_full, metadata_msr_full, on=['BldgLoc','BldgType','BldgVint','BldgHVAC','SizingID','tstat','normunit'])
+else:
+    df_measure_baseline_full = pd.merge(metadata_pre_full, metadata_std_full, on=['BldgLoc','BldgType','BldgVint','BldgHVAC','SizingID','tstat','normunit'])
+    df_measure_set_full = pd.merge(df_measure_baseline_full, metadata_msr_full, on=['BldgLoc','BldgType','BldgVint','BldgHVAC','SizingID','tstat','normunit'])
+
+# %%
+#Unique sets of each MeasureID with their TechID triplets
+TechID_triplets = df_measure[['EnergyImpactID','MeasureID', 'PreTechID', 'StdTechID','MeasTechID']].drop_duplicates()
+# %%
+#to match TechID triplets, merge on these 3 fields, keeping only valid TechID Triplets
+if np.NaN in list(StdTechIDs['StdTechID'].unique()):
+    current_msr_mat_proto = pd.merge(df_measure_set_full, TechID_triplets, on=['PreTechID','MeasTechID'])
+elif np.NaN in list(PreTechIDs['PreTechID'].unique()):
+    current_msr_mat_proto = pd.merge(df_measure_set_full, TechID_triplets, on=['StdTechID','MeasTechID'])
+else:
+    current_msr_mat_proto = pd.merge(df_measure_set_full, TechID_triplets, on=['PreTechID','StdTechID','MeasTechID'])
+
+
+
+# %%
+#add placeholders, rearrange fields
+current_msr_mat_proto['PreSizingID']='None'
+current_msr_mat_proto['StdSizingID']='None'
+current_msr_mat_proto['MsrSizingID']='None'
+current_msr_mat_proto['SizingSrc']=np.nan
+
+#to be worked on: need to add corresponding indicator for what enduse it is for end use loadshape connections
+current_msr_mat_proto['EU_HrRepVar']=np.nan
+
+current_msr_mat = current_msr_mat_proto[['MeasureID', 'BldgType', 'BldgVint','BldgLoc','BldgHVAC','tstat','PreTechID','PreSizingID',
+                             'StdTechID', 'StdSizingID','MeasTechID','MsrSizingID','SizingSrc','EU_HrRepVar','normunit']]
+current_msr_mat = current_msr_mat.rename(columns={'normunit':'NormUnit'})
+
+#%%
+#check length of current_msr_mat
+len(current_msr_mat)
+
+# %%
+##STEP 5: Clean Up Sequence
+#Creating updated Sim_annual and Sim_hourly data with distinguished TechID names if needed. 
+sim_annual_pre_common = sim_annual_f[sim_annual_f['TechID'].isin(PreTechIDs['Common_PreTechID'].unique())]
+sim_annual_std_common = sim_annual_f[sim_annual_f['TechID'].isin(StdTechIDs['Common_StdTechID'].unique())]
+sim_annual_msr_common = sim_annual_f[sim_annual_f['TechID'].isin(MeasTechIDs['Common_MeasTechID'].unique())]
+# %%
+#Add a TechID col renaming the common TechID to the specific TechID using PreTechIDs, StdTechIDs, MeasTechIDs
+
+#create full pre sim_annual sets for different names but the same TechID
+# commom_preTechID = PreTechIDs['Common_PreTechID'].unique()[0]
+if False in list(PreTechIDs['PreTechID']==PreTechIDs['Common_PreTechID']):
+    sim_annual_pre = pd.DataFrame()
+    for _, (common_id, new_id) in PreTechIDs[['Common_PreTechID', 'PreTechID']].iterrows():
+        print(f'changing to specific PreTechID {new_id}')
+        sim_annual_pre_mod = sim_annual_pre_common[sim_annual_pre_common['TechID']==common_id].copy()
+        sim_annual_pre_mod['TechID'] = new_id
+        #merge to final df
+        sim_annual_pre = pd.concat([sim_annual_pre, sim_annual_pre_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_annual_pre = sim_annual_pre_common.copy()
+
+# %%
+# create full std sim_annual sets for different names but the same TechID
+if False in list(StdTechIDs['StdTechID']==StdTechIDs['Common_StdTechID']):
+    sim_annual_std = pd.DataFrame()
+    for common_id, new_id in zip(StdTechIDs['Common_StdTechID'], StdTechIDs['StdTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Isolate specific common id (old)
+        sim_annual_std_mod = sim_annual_std_common[sim_annual_std_common['TechID']==common_id].copy()
+        #Change into final techID name (new)
+        sim_annual_std_mod['TechID'] = new_id
+        #merge to final df
+        sim_annual_std = pd.concat([sim_annual_std, sim_annual_std_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_annual_std = sim_annual_std_common.copy()
+# %%
+# create full msr sim_annual sets for different names but the same TechID
+if False in list(MeasTechIDs['MeasTechID']==MeasTechIDs['Common_MeasTechID']):
+    sim_annual_msr = pd.DataFrame()
+    for common_id, new_id in zip(MeasTechIDs['Common_MeasTechID'], MeasTechIDs['MeasTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Isolate specific common id (old)
+        sim_annual_msr_mod = sim_annual_msr_common[sim_annual_msr_common['TechID']==common_id].copy()
+        #Change into final techID name (new)
+        sim_annual_msr_mod['TechID'] = new_id
+        #merge to final df
+        sim_annual_msr = pd.concat([sim_annual_msr, sim_annual_msr_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_annual_msr = sim_annual_msr_common.copy()
+
+# %%
+#final merge sim_annual
+sim_annual_final = pd.concat([sim_annual_pre, sim_annual_std, sim_annual_msr])
+# %%
+
+###same deal with with hourly data, separate into pre std msr, change into specific TechID
+sim_hourly_pre_common = sim_hourly_f[sim_hourly_f['TechID'].isin(PreTechIDs['Common_PreTechID'].unique())]
+sim_hourly_std_common = sim_hourly_f[sim_hourly_f['TechID'].isin(StdTechIDs['Common_StdTechID'].unique())]
+sim_hourly_msr_common = sim_hourly_f[sim_hourly_f['TechID'].isin(MeasTechIDs['Common_MeasTechID'].unique())]
+
+# %%
+#Pre hourly
+if False in list(PreTechIDs['PreTechID']==PreTechIDs['Common_PreTechID']):
+    sim_hourly_pre = pd.DataFrame()
+    for _, (common_id, new_id) in PreTechIDs[['Common_PreTechID', 'PreTechID']].iterrows():
+        print(f'changing to specific PreTechID {new_id}')
+        sim_hourly_pre_mod = sim_hourly_pre_common[sim_hourly_pre_common['TechID']==common_id].copy()
+        sim_hourly_pre_mod['TechID'] = new_id
+        #merge to final df
+        sim_hourly_pre = pd.concat([sim_hourly_pre, sim_hourly_pre_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_hourly_pre = sim_hourly_pre_common.copy()
+# %%
+#Std hourly
+if False in list(StdTechIDs['StdTechID']==StdTechIDs['Common_StdTechID']):
+    sim_hourly_std = pd.DataFrame()
+    for common_id, new_id in zip(StdTechIDs['Common_StdTechID'], StdTechIDs['StdTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Isolate specific common id (old)
+        sim_hourly_std_mod = sim_hourly_std_common[sim_hourly_std_common['TechID']==common_id].copy()
+        #Change into final techID name (new)
+        sim_hourly_std_mod['TechID'] = new_id
+        #merge to final df
+        sim_hourly_std = pd.concat([sim_hourly_std, sim_hourly_std_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_hourly_std = sim_hourly_std_common.copy()
+# %%
+#Msr hourly
+if False in list(MeasTechIDs['MeasTechID']==MeasTechIDs['Common_MeasTechID']):
+    sim_hourly_msr = pd.DataFrame()
+    for common_id, new_id in zip(MeasTechIDs['Common_MeasTechID'], MeasTechIDs['MeasTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Isolate specific common id (old)
+        sim_hourly_msr_mod = sim_hourly_msr_common[sim_hourly_msr_common['TechID']==common_id].copy()
+        #Change into final techID name (new)
+        sim_hourly_msr_mod['TechID'] = new_id
+        #merge to final df
+        sim_hourly_msr = pd.concat([sim_hourly_msr, sim_hourly_msr_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_hourly_msr = sim_hourly_msr_common.copy()
+
+# %%
+#final merge sim_hourly
+sim_hourly_final = pd.concat([sim_hourly_pre, sim_hourly_std, sim_hourly_msr])
+
+# %%
+##Final export of all processed data pre-SQL process
+#change directory to wherever desired, if needed
+
+os.chdir(os.path.dirname(__file__)) #resets to current script directory
+print(os.path.abspath(os.curdir))
+
+current_msr_mat.to_csv('current_msr_mat.csv', index=False)
+sim_annual_final.to_csv('sim_annual.csv', index=False)
+sim_hourly_final.to_csv('sim_hourly_wb.csv', index=False)
+
+
+# %%
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/post-process/MFm.py b/residential measures/SWHC049-08 SEER Rated AC HP/post-process/MFm.py
new file mode 100644
index 000000000..0cd1e45e0
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/post-process/MFm.py	
@@ -0,0 +1,654 @@
+#%%
+##STEP 0: Setup (import all necessary libraries)
+import pandas as pd
+import numpy as np
+import os
+import sys
+import datetime as dt
+os.chdir(os.path.dirname(__file__)) #resets to current script directory
+# %%
+#Read master workbook for measure / tech list
+df_master = pd.read_excel('DEER_EnergyPlus_Modelkit_Measure_list_working.xlsx', sheet_name='Measure_list', skiprows=4)
+
+measure_group_names = list(df_master['Measure Group Name'].unique())
+
+# %%
+#generate unique list of measure names
+measures = list(df_master['Measure (general name)'].unique())
+# %%
+#Shows list of measure names 
+print(measures)
+#%%
+#Define measure name here
+measure_name = 'SEER Rated AC HP'
+
+# %%
+#MFm only script
+####Define path
+os.chdir(os.path.dirname(__file__)) #resets to current script directory
+print(os.path.abspath(os.curdir))
+os.chdir("../..") #go up two directory
+print(os.path.abspath(os.curdir))
+
+path = 'residential measures/SWHC049-06 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_MFm_New'
+# %%
+#extract only the 5th portion of the measure group name for expected_att
+#split argument 4 means only split 4 times maximum
+techgroup_techtypes = [i.split('&', 4)[-1] for i in measure_group_names]
+tech_uniques = list(np.unique((np.array(techgroup_techtypes))))
+# Name parsing function per Amine
+# This dictionary of attributes should be updated
+expected_att = {
+    'BldgType': ['MFm','SFm', 'DMo'],
+    'Story': ['0','1','2'], # NA for Not Applicable
+    'BldgHVAC': ['rDXGF','rDXHP','rNCEH','rNCGF'],
+    'BldgVint': ['Ex','New'],
+    'Measure': tech_uniques
+}
+
+# function to parse meta data from & delimited case file names (Measure Group Name from master spreadsheet)
+def parse_measure_name(measure_name):
+    #split at most 4 times for 5 descriptor fields
+    measure_name_split = measure_name.split('&', 4) 
+    # Check here if the presented name has 5 attributes as expected:
+    if not len(measure_name_split) == 5:
+        sys.exit('The case name must have at least 5 attributes similar to < BldgType&Story&BldgHVAC&BldgVint&TechGroup__TechType >')
+    
+    attributes = list(expected_att.keys())
+    measure_name_dict = {attributes[i]: measure_name_split[i] for i in range(0,5)}
+
+    # Check here if the presented attributes are as expected:
+    for att in attributes:
+        if measure_name_dict[att] not in expected_att[att]:
+            sys.exit(f'Attribute <{measure_name_dict[att]}> was not expected')
+            
+
+    return measure_name_dict # returns a dictionary
+
+def parse_measure_name2(cohort_names: pd.Series, verify: bool = False) -> pd.DataFrame:
+    '''Returns a DataFrame with five columns (all type string):
+        ["BldgType","Story","BldgHVAC","BldgVint","TechGroup__TechType"]
+    Each cohort name must match the pattern:
+        "BldgType&Story&BldgHVAC&BldgVint&TechGroup__TechType"
+    Only alphanumeric characters are allowed  [a-zA-Z0-9_], except TechGroup__TechType may contain ampersand (&).
+
+    Parameters
+    ----------
+    cohort_names : pandas.Series
+        The cohort names as from cohorts.csv.
+    verify : bool, default=False
+        If true and name parts do not match `expected_att`, raise an exception.
+
+    Returns
+    -------
+    pandas.DataFrame
+        Structure containing the parts of cohort name.
+    '''
+    result = cohort_names.str.extract(
+        r'(?P<BldgType>\w+)&(?P<Story>\w+)&(?P<BldgHVAC>\w+)&(?P<BldgVint>\w+)&(?P<Measure>[^/]+)'
+    )
+    if verify:
+        # Check for missing descriptor fields
+        missing = result.isna()
+        if missing.any().any():
+            example = cohort_names[missing.any(axis=1)].iloc[0]
+            raise ValueError(f'Missing descriptor field, e.g. cohort = "{example}"')
+        # Check for unrecognized fields
+        for attr_name,attr_val in expected_att.items():
+            unrecognized = ~result[attr_name].isin(attr_val)
+            if unrecognized.any():
+                example = result[attr_name][unrecognized].iloc[0]
+                raise ValueError(f'Unrecognized descriptor field, e.g. {attr_name} = "{example}"')
+    result.rename({'Measure':'TechGroup__TechType'},axis=1,inplace=True)
+    return result
+
+#function to melt long 8760 col into 24col x365row format
+def long2wide_pivot(df, name):
+    '''
+    customized function. 
+    input df is long 8760, 1 column format, with the daynum(of365) and hour of day(of24) mapped.
+    id is the unique identifier.
+    
+    output df is the 24col x 365row format of the 8760, with the corresponding id.
+    '''
+    df_wide = df.pivot(index='daynum',columns='Hour', values=df.columns[0]).reset_index().rename_axis('', axis=1)
+    df_wide['ID']=name
+    #df_wide = pd.merge(df_wide, df_ag_key, on='ID')
+    
+    return df_wide
+
+#function to uses the “File Name” column from the results-summary csv to identify directory structure (an organized table) of a batch run 
+# and uses the structure to construct a semi-organized annual outputs table.
+def annual_raw_parsing(df, cohort_dict):
+    #create separated meta data cols
+    df['BldgLoc'] = split_meta_cols_eu[0]
+
+    df['BldgType'] = cohort_dict['BldgType']
+    df['BldgHVAC'] = cohort_dict['BldgHVAC']
+    df['BldgVint'] = cohort_dict['BldgVint']
+
+    df['Story'] = cohort_dict['Story']
+
+    df['TechGroup_TechType'] = cohort_dict['Measure']
+
+    df['TechID'] = split_meta_cols_eu[2]
+    df['file'] = split_meta_cols_eu[3]
+    
+    annual_df_v1 = df[['TechID', 'file', 'BldgLoc', 'BldgType','BldgHVAC','BldgVint','Story', 'TechGroup_TechType','Total (kWh)', 'Heating (kWh)', 'Cooling (kWh)',
+       'Interior Lighting (kWh)', 'Exterior Lighting (kWh)',
+       'Interior Equipment (kWh)', 'Exterior Equipment (kWh)', 'Fans (kWh)',
+       'Pumps (kWh)', 'Heat Rejection (kWh)', 'Humidification (kWh)',
+       'Heat Recovery (kWh)', 'Water Systems (kWh)', 'Refrigeration (kWh)',
+       'Generators (kWh)', 'Heating Elec (kWh)', 'Cooling Elec (kWh)',
+       'Heating NG (kWh)', 'Cooling NG (kWh)', 'Interior Equipment Elec (kWh)',
+       'Interior Equipment NG (kWh)']]
+    
+    return annual_df_v1
+
+#function to merge and rearrange specific annual consumption end-use fields into the format required
+def end_use_rearrange(df_in):
+    df_in['kwh_tot'] = (df_in['Heating Elec (kWh)'] + \
+                            df_in['Cooling Elec (kWh)'] +\
+                            df_in['Interior Equipment Elec (kWh)'] +\
+                            df_in['Interior Lighting (kWh)'] +\
+                            df_in['Exterior Lighting (kWh)'] +\
+                            df_in['Fans (kWh)']+\
+                            df_in['Pumps (kWh)'])
+
+    df_in['kwh_ltg'] = (df_in['Interior Lighting (kWh)'] +\
+                                    df_in['Exterior Lighting (kWh)'])
+
+    df_in['kwh_task'] = 0 # placeholder (task lighting load?)
+
+    df_in['kwh_equip'] = df_in['Interior Equipment Elec (kWh)'] +\
+                                    df_in['Exterior Equipment (kWh)']
+
+    df_in['kwh_htg'] = df_in['Heating Elec (kWh)']
+    df_in['kwh_clg'] = df_in['Cooling Elec (kWh)']
+    df_in['kwh_twr'] = 0 #place holder (tower kwh load?)
+    df_in['kwh_aux'] = 0 #place holder (aux equipment kwh load?)
+
+    df_in['kwh_vent'] = df_in['Fans (kWh)'] #use fan kWh as vent load for now
+
+    df_in['kwh_venthtg'] =0 #placeholders fields for now
+    df_in['kwh_ventclg'] =0
+    df_in['kwh_refg'] = 0 
+    df_in['kwh_hpsup'] = 0
+    df_in['kwh_shw'] = 0
+    df_in['kwh_ext'] = 0
+
+    df_in['thm_tot'] = (df_in['Heating NG (kWh)'] +\
+                                df_in['Cooling NG (kWh)'] +\
+                                df_in['Interior Equipment NG (kWh)'] +\
+                                df_in['Water Systems (kWh)'])/29.3
+
+    df_in['thm_equip'] = df_in['Interior Equipment NG (kWh)']/29.3
+
+    df_in['thm_htg'] = df_in['Heating NG (kWh)']/29.3
+
+    df_in['thm_shw'] = df_in['Water Systems (kWh)']/29.3
+
+    df_in['deskw_ltg'] = 1 #placeholders fields for now
+    df_in['deskw_equ'] = 1
+
+    return df_in
+
+# %%
+#create measure specific Master table based on Measure selected
+df_measure = df_master[df_master['Measure (general name)'] == measure_name]
+case_cohort_list = df_measure['Measure Group Name'].unique()
+
+
+# %%
+##STEP 1: Annual data extraction / transformation
+
+df_raw = pd.read_csv(path+'/results-summary.csv', usecols=['File Name'])
+num_runs = len(df_raw['File Name'].dropna().unique()) - 1
+#Read annual data
+annual_df = pd.read_csv(path+'/results-summary.csv', nrows=num_runs, skiprows=num_runs+2)
+split_meta_cols_eu = annual_df['File Name'].str.split('/', expand=True)
+
+#if looping over multiple folders/cohort cases, use a list
+cohort_cases = list(split_meta_cols_eu[1].unique())
+
+sim_annual_proto = pd.DataFrame()
+for case in cohort_cases:
+    print(f'processing all annual data that are grouped in {case}')
+    cohort_dict = parse_measure_name(case)
+    sim_annual_i = annual_raw_parsing(annual_df[annual_df['File Name'].str.contains(case)].copy(), cohort_dict)
+    sim_annual_proto = pd.concat([sim_annual_proto, sim_annual_i])
+    print('ok.')
+sim_annual_proto = end_use_rearrange(sim_annual_proto)
+sim_annual_v1 = sim_annual_proto[['TechID', 'BldgLoc', 'BldgType', 'BldgHVAC', 'BldgVint', 'kwh_tot', 'kwh_ltg', 'kwh_task',
+    'kwh_equip', 'kwh_htg', 'kwh_clg', 'kwh_twr', 'kwh_aux', 'kwh_vent',
+    'kwh_venthtg', 'kwh_ventclg',
+    'kwh_refg', 'kwh_hpsup', 'kwh_shw', 'kwh_ext', 'thm_tot', 'thm_equip',
+    'thm_htg', 'thm_shw', 'deskw_ltg', 'deskw_equ']].drop_duplicates().copy()
+
+# %%
+##STEP 2: Hourly data extraction / transformation
+#Read 8760 map
+os.chdir(os.path.dirname(__file__)) #resets to current script directory
+print(os.path.abspath(os.curdir))
+annual_map = pd.read_excel('annual8760map.xlsx')
+
+# %%
+os.chdir("../..") #go up two directory
+print(os.path.abspath(os.curdir))
+hrly_path = path + '/runs' 
+
+#extract data per bldgtype-bldghvac-bldgvint group
+hourly_df = pd.DataFrame(index=range(0,8760))
+#extract num_runs / split_meta_cols_eu
+df_raw = pd.read_csv(path+'/'+'/results-summary.csv', usecols=['File Name'])
+num_runs = len(df_raw['File Name'].dropna().unique()) - 1
+annual_df = pd.read_csv(path+'/'+'/results-summary.csv', nrows=num_runs, skiprows=num_runs+2)
+split_meta_cols_eu = annual_df['File Name'].str.split('/', expand=True)
+for i in range(0,num_runs):
+    print(f"merging record {i}")
+    
+    #loop path of each file, read corresponding file
+    full_path = hrly_path + "/" + split_meta_cols_eu.iloc[i][0] + "/" + split_meta_cols_eu.iloc[i][1] + "/" + split_meta_cols_eu.iloc[i][2] + "/instance-var.csv"
+    df = pd.read_csv(full_path, low_memory=False)
+    
+    #remove traling spaces on col headers
+    df.columns = df.columns.str.rstrip()
+
+    #extract the last column (the total elec hrly profile)
+    #if for enduse hourly, then extract the relevant end use column
+    extracted_df = pd.DataFrame(df['Electricity:Facility [J](Hourly)'])
+    
+    #create the column name based on the permutations
+    col_name = split_meta_cols_eu.iloc[i][0] + "/" + split_meta_cols_eu.iloc[i][1] + "/" + split_meta_cols_eu.iloc[i][2] + "/instance-var.csv"
+    
+    #change column name
+    extracted_df = extracted_df.set_axis([col_name],axis=1)
+    if len(extracted_df)!=8760:
+        #8/31/2022 update, need to make the final length 8808. Snip data based on difference to 8760
+        record_count_diff = len(extracted_df) - 8760
+        print(f'extra records: {str(len(extracted_df))}, snipping away {record_count_diff} records and changing to 8760')
+        extracted_df = extracted_df.iloc[record_count_diff:].reset_index(drop=True)
+    
+    #left-merge onto big df
+    hourly_df = hourly_df.merge(extracted_df, left_index=True, right_index=True)
+# %%
+fyr_hrly = hourly_df
+#rearrange 1-column 8760 format to 365x24 wide format for all runs in hourly_df
+converted_df = pd.DataFrame()
+
+for i in range(0,len(fyr_hrly.columns)):
+    
+    #isolate single column
+    hrly_df = pd.DataFrame(fyr_hrly.iloc[:,i])
+    
+    #create separate metadata columns
+    col_names = hrly_df.columns[0].split('/')
+    
+    #create new key column for merge
+    hrly_df['hr in 8760'] = (hrly_df.index) + 1
+    
+    #merge based on "hr in 8760" column, the 8760 map
+    hrly_mapped = pd.merge(hrly_df, annual_map, on='hr in 8760')
+    
+    #transform data format
+    hrly_wide = long2wide_pivot(hrly_mapped, hrly_mapped.columns[0])
+    
+    #add meta data col
+    hrly_wide['BldgLoc'] = col_names[0]
+    hrly_wide['BldgType'] = col_names[1]
+    hrly_wide['TechID'] = col_names[2]
+    hrly_wide['file'] = col_names[3]
+    
+    #append to master df
+    #converted_df = converted_df.append(hrly_wide) #deprecated method
+    converted_df = pd.concat([converted_df, hrly_wide])
+    print(f"col {i} transformed.")
+#%%
+#rearrange columns
+sim_hourly_wb_proto = converted_df[['TechID','file','BldgLoc','BldgType','ID','daynum',1,          2,          3,          4,          5,
+                6,          7,          8,          9,         10,         11,
+            12,         13,         14,         15,         16,         17,
+            18,         19,         20,         21,         22,         23,
+            24]].copy()
+#hourly data conversion
+#convert unit (J) to (kWh) for hourly
+
+sim_hourly_wb_proto['hr01'] = sim_hourly_wb_proto[1]/3600000
+sim_hourly_wb_proto['hr02'] = sim_hourly_wb_proto[2]/3600000
+sim_hourly_wb_proto['hr03'] = sim_hourly_wb_proto[3]/3600000
+sim_hourly_wb_proto['hr04'] = sim_hourly_wb_proto[4]/3600000
+sim_hourly_wb_proto['hr05'] = sim_hourly_wb_proto[5]/3600000
+sim_hourly_wb_proto['hr06'] = sim_hourly_wb_proto[6]/3600000
+sim_hourly_wb_proto['hr07'] = sim_hourly_wb_proto[7]/3600000
+sim_hourly_wb_proto['hr08'] = sim_hourly_wb_proto[8]/3600000
+sim_hourly_wb_proto['hr09'] = sim_hourly_wb_proto[9]/3600000
+sim_hourly_wb_proto['hr10'] = sim_hourly_wb_proto[10]/3600000
+sim_hourly_wb_proto['hr11'] = sim_hourly_wb_proto[11]/3600000
+sim_hourly_wb_proto['hr12'] = sim_hourly_wb_proto[12]/3600000
+sim_hourly_wb_proto['hr13'] = sim_hourly_wb_proto[13]/3600000
+sim_hourly_wb_proto['hr14'] = sim_hourly_wb_proto[14]/3600000
+sim_hourly_wb_proto['hr15'] = sim_hourly_wb_proto[15]/3600000
+sim_hourly_wb_proto['hr16'] = sim_hourly_wb_proto[16]/3600000
+sim_hourly_wb_proto['hr17'] = sim_hourly_wb_proto[17]/3600000
+sim_hourly_wb_proto['hr18'] = sim_hourly_wb_proto[18]/3600000
+sim_hourly_wb_proto['hr19'] = sim_hourly_wb_proto[19]/3600000
+sim_hourly_wb_proto['hr20'] = sim_hourly_wb_proto[20]/3600000
+sim_hourly_wb_proto['hr21'] = sim_hourly_wb_proto[21]/3600000
+sim_hourly_wb_proto['hr22'] = sim_hourly_wb_proto[22]/3600000
+sim_hourly_wb_proto['hr23'] = sim_hourly_wb_proto[23]/3600000
+sim_hourly_wb_proto['hr24'] = sim_hourly_wb_proto[24]/3600000
+
+#rearrange columns
+sim_hourly_wb_v1 = sim_hourly_wb_proto[['TechID','file','BldgLoc','BldgType','ID','daynum','hr01','hr02','hr03','hr04','hr05','hr06',
+        'hr07',     'hr08',     'hr09',     'hr10',     'hr11',     'hr12',
+        'hr13',     'hr14',     'hr15',     'hr16',     'hr17',     'hr18',
+        'hr19',     'hr20',     'hr21',     'hr22',     'hr23',     'hr24']].copy()
+# %%
+##STEP 3: Normalizing Units
+bldgtype = 'MFm'
+os.chdir(os.path.dirname(__file__)) #resets to current script directory
+print(os.path.abspath(os.curdir))
+df_normunits = pd.read_excel('Normunits.xlsx', sheet_name=bldgtype)
+numunits_vals = df_normunits[df_normunits['Normunit'] == df_measure['Normunit'].unique()[0]][['CZ','Value', 'Msr','BldgVint']]
+#%%
+#create numunits object based on what normunit it uses. 
+#numunits can be a single value, or a dictionary
+if len(numunits_vals) == 1:
+    numunits = list(numunits_vals['Value'])[0]
+elif (measure_name == 'Wall Insulation') or (measure_name == 'Ceiling Insulation'):
+    numunits = list(numunits_vals[numunits_vals['Msr'] == measure_name]['Value'])[0]
+elif measure_name == 'PTAC / PTHP':
+    #create aligned lists for numunit dictionary
+    cz = list(numunits_vals['CZ'])
+    vint = list(numunits_vals['BldgVint'])
+    nvals = list(numunits_vals['Value'])
+    #create dictionary of {(cz,vintage):numunits}
+    numunits = {(cz[i],vint[i]):nvals[i] for i in range(len(cz))}
+else:
+    pass
+
+# %%
+##Annual Data final field fixes
+#note normunit = building area (conditioned)
+sim_annual_v1['SizingID'] = 'None'
+sim_annual_v1['tstat'] = 0
+#now Norm unit is read from measure master table
+sim_annual_v1['normunit'] = df_measure['Normunit'].unique()[0]
+#make this automatic as well
+sim_annual_v1['measarea'] = 24576 #from MFm model outputs htmls
+
+#apply normunits where appropriate
+#num unit will be per dwelling, so divide by num of dwellings (2 for SFM, DMo, 24 for MFm)
+
+if measure_name == 'PTAC / PTHP':
+    #Map special dictionary to the correct values
+    sim_annual_v1['numunits'] = pd.Series(list(zip(sim_annual_v1['BldgLoc'],sim_annual_v1['BldgVint']))).map(numunits)/24
+else:
+    sim_annual_v1['numunits'] = numunits/24
+
+
+sim_annual_v1['lastmod']=dt.datetime.now()
+
+#rearrange columns
+sim_annual_f = sim_annual_v1[['TechID', 'SizingID', 'BldgType','BldgVint','BldgLoc','BldgHVAC','tstat',
+       'normunit', 'numunits', 'measarea', 'kwh_tot', 'kwh_ltg', 'kwh_task',
+       'kwh_equip', 'kwh_htg', 'kwh_clg', 'kwh_twr', 'kwh_aux', 'kwh_vent',
+       'kwh_venthtg', 'kwh_ventclg', 'kwh_refg', 'kwh_hpsup', 'kwh_shw',
+       'kwh_ext', 'thm_tot', 'thm_equip', 'thm_htg', 'thm_shw', 'deskw_ltg',
+       'deskw_equ', 'lastmod']]
+# %%
+##Hourly Data final field fixes
+
+#update field names based on what it contains
+df_tmp = parse_measure_name2(sim_hourly_wb_v1['ID'],verify=True)
+sim_hourly_wb_v1['BldgVint'] = df_tmp['BldgVint']
+sim_hourly_wb_v1['BldgHVAC'] = df_tmp['BldgHVAC']
+sim_hourly_wb_v1['BldgType'] = df_tmp['BldgType']
+del df_tmp
+sim_hourly_wb_v1['SizingID'] = 'None'
+sim_hourly_wb_v1['tstat'] = 0
+sim_hourly_wb_v1['enduse'] = 0
+sim_hourly_wb_v1['lastmod']=dt.datetime.now()
+
+#rearrange columns
+sim_hourly_f = sim_hourly_wb_v1[['TechID', 'SizingID', 'BldgType', 'BldgVint', 'BldgLoc','BldgHVAC','tstat', 'enduse', 'daynum', 
+                                 'hr01', 'hr02', 'hr03', 'hr04', 'hr05', 'hr06', 'hr07', 'hr08', 'hr09', 'hr10', 'hr11',
+                                'hr12', 'hr13', 'hr14', 'hr15', 'hr16', 'hr17', 'hr18', 'hr19', 'hr20',
+                                'hr21', 'hr22', 'hr23', 'hr24', 'lastmod']]
+# %%
+##STEP 4: Measure setup file (current_msr_mat.csv)
+
+# Creating current_msr_mat and finalzing TechID's
+
+metadata_cols = sim_annual_f[['TechID', 'BldgLoc', 'BldgType', 'BldgVint', 'BldgHVAC', 'SizingID',
+       'tstat', 'normunit']]
+
+#check unique TechID cases
+metadata_cols['TechID'].unique()
+# %%
+#TechID identification from Master table
+#if looping over all HVAC types, ignore BldgHVAC filter
+PreTechIDs = df_measure[['PreTechID','Common_PreTechID']].drop_duplicates()
+StdTechIDs = df_measure[['StdTechID','Common_StdTechID']].drop_duplicates()
+MeasTechIDs = df_measure[['MeasTechID','Common_MeasTechID']].drop_duplicates()
+# %%
+#filter out each pre, std, msr using the Common TechIDs from master table
+metadata_pre = metadata_cols[metadata_cols['TechID'].isin(PreTechIDs['Common_PreTechID'].unique())]
+metadata_std = metadata_cols[metadata_cols['TechID'].isin(StdTechIDs['Common_StdTechID'].unique())]
+metadata_msr = metadata_cols[metadata_cols['TechID'].isin(MeasTechIDs['Common_MeasTechID'].unique())]
+
+# %%
+#rename to Pre, Std or Msr 
+#both Std and Pre are baseline for SEER rated AC measures
+metadata_pre = metadata_pre.rename(columns={'TechID':'PreTechID'})
+metadata_std = metadata_std.rename(columns={'TechID':'StdTechID'})
+metadata_msr = metadata_msr.rename(columns={'TechID':'MeasTechID'})
+# %%
+#Changing common TechID to actual TechIDs if needed.
+#might only apply to SEER AC/HP
+
+#create full pre_metadata sets for different names but the same TechID
+# commom_preTechID = PreTechIDs['Common_PreTechID'].unique()[0]
+if False in list(PreTechIDs['PreTechID']==PreTechIDs['Common_PreTechID']):
+    metadata_pre_full = pd.DataFrame()
+    for _, (common_id, new_id) in PreTechIDs[['Common_PreTechID', 'PreTechID']].iterrows():
+        print(f'changing to specific PreTechID {new_id}')
+        metadata_pre_mod = metadata_pre[metadata_pre['PreTechID']==common_id].copy()
+        metadata_pre_mod['PreTechID'] = new_id
+        #merge to final df
+        metadata_pre_full = pd.concat([metadata_pre_full, metadata_pre_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    metadata_pre_full = metadata_pre.copy()
+
+
+# %%
+#create std_metadata sets, assigning appropriate final TechIDs
+if False in list(StdTechIDs['StdTechID']==StdTechIDs['Common_StdTechID']):
+    metadata_std_full = pd.DataFrame()
+    for common_id, new_id in zip(StdTechIDs['Common_StdTechID'], StdTechIDs['StdTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Isolate specific common id (old)
+        metadata_std_mod = metadata_std[metadata_std['StdTechID']==common_id].copy()
+        #Change into final techID name (new)
+        metadata_std_mod['StdTechID'] = new_id
+        #merge to final df
+        metadata_std_full = pd.concat([metadata_std_full, metadata_std_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    metadata_std_full = metadata_std.copy()
+# %%
+#create msr_metadata sets, assigning appropriate final TechIDs
+if False in list(MeasTechIDs['MeasTechID']==MeasTechIDs['Common_MeasTechID']):
+    metadata_msr_full = pd.DataFrame()
+    for common_id, new_id in zip(MeasTechIDs['Common_MeasTechID'], MeasTechIDs['MeasTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Identify corresponding common TechID (the last 9 characters indicating SEER levels)
+        metadata_msr_mod = metadata_msr[metadata_msr['MeasTechID']==common_id].copy()
+        #Change into final TechID name
+        metadata_msr_mod['MeasTechID'] = new_id
+        #merge to final df
+        metadata_msr_full = pd.concat([metadata_msr_full, metadata_msr_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    metadata_msr_full = metadata_msr.copy()
+# %%
+#create raw merged current_msr_mat
+#need to delete/drop incorrect sets
+if np.NaN in list(StdTechIDs['StdTechID'].unique()):
+    df_measure_set_full = pd.merge(metadata_pre_full, metadata_msr_full, on=['BldgLoc','BldgType','BldgVint','BldgHVAC','SizingID','tstat','normunit'])
+elif np.NaN in list(PreTechIDs['PreTechID'].unique()):
+    df_measure_set_full = pd.merge(metadata_std_full, metadata_msr_full, on=['BldgLoc','BldgType','BldgVint','BldgHVAC','SizingID','tstat','normunit'])
+else:
+    df_measure_baseline_full = pd.merge(metadata_pre_full, metadata_std_full, on=['BldgLoc','BldgType','BldgVint','BldgHVAC','SizingID','tstat','normunit'])
+    df_measure_set_full = pd.merge(df_measure_baseline_full, metadata_msr_full, on=['BldgLoc','BldgType','BldgVint','BldgHVAC','SizingID','tstat','normunit'])
+
+# %%
+#Unique sets of each MeasureID with their TechID triplets
+TechID_triplets = df_measure[['EnergyImpactID','MeasureID', 'PreTechID', 'StdTechID','MeasTechID']].drop_duplicates()
+# %%
+#to match TechID triplets, merge on these 3 fields, keeping only valid TechID Triplets
+if np.NaN in list(StdTechIDs['StdTechID'].unique()):
+    current_msr_mat_proto = pd.merge(df_measure_set_full, TechID_triplets, on=['PreTechID','MeasTechID'])
+elif np.NaN in list(PreTechIDs['PreTechID'].unique()):
+    current_msr_mat_proto = pd.merge(df_measure_set_full, TechID_triplets, on=['StdTechID','MeasTechID'])
+else:
+    current_msr_mat_proto = pd.merge(df_measure_set_full, TechID_triplets, on=['PreTechID','StdTechID','MeasTechID'])
+
+# %%
+#add placeholders, rearrange fields
+current_msr_mat_proto['PreSizingID']='None'
+current_msr_mat_proto['StdSizingID']='None'
+current_msr_mat_proto['MsrSizingID']='None'
+current_msr_mat_proto['SizingSrc']=np.nan
+
+#to be worked on: need to add corresponding indicator for what enduse it is for end use loadshape connections
+current_msr_mat_proto['EU_HrRepVar']=np.nan
+
+current_msr_mat = current_msr_mat_proto[['MeasureID', 'BldgType', 'BldgVint','BldgLoc','BldgHVAC','tstat','PreTechID','PreSizingID',
+                             'StdTechID', 'StdSizingID','MeasTechID','MsrSizingID','SizingSrc','EU_HrRepVar','normunit']]
+current_msr_mat = current_msr_mat.rename(columns={'normunit':'NormUnit'})
+
+#%%
+#check length of current_msr_mat
+len(current_msr_mat)
+
+# %%
+##STEP 5: Clean Up Sequence
+#Creating updated Sim_annual and Sim_hourly data with distinguished TechID names
+sim_annual_pre_common = sim_annual_f[sim_annual_f['TechID'].isin(PreTechIDs['Common_PreTechID'].unique())]
+sim_annual_std_common = sim_annual_f[sim_annual_f['TechID'].isin(StdTechIDs['Common_StdTechID'].unique())]
+sim_annual_msr_common = sim_annual_f[sim_annual_f['TechID'].isin(MeasTechIDs['Common_MeasTechID'].unique())]
+# %%
+#Add a TechID col renaming the common TechID to the specific TechID using PreTechIDs, StdTechIDs, MeasTechIDs
+
+#create full pre sim_annual sets for different names but the same TechID
+# commom_preTechID = PreTechIDs['Common_PreTechID'].unique()[0]
+if False in list(PreTechIDs['PreTechID']==PreTechIDs['Common_PreTechID']):
+    sim_annual_pre = pd.DataFrame()
+    for _, (common_id, new_id) in PreTechIDs[['Common_PreTechID', 'PreTechID']].iterrows():
+        print(f'changing to specific PreTechID {new_id}')
+        sim_annual_pre_mod = sim_annual_pre_common[sim_annual_pre_common['TechID']==common_id].copy()
+        sim_annual_pre_mod['TechID'] = new_id
+        #merge to final df
+        sim_annual_pre = pd.concat([sim_annual_pre, sim_annual_pre_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_annual_pre = sim_annual_pre_common.copy()
+
+# %%
+# create full std sim_annual sets for different names but the same TechID
+if False in list(StdTechIDs['StdTechID']==StdTechIDs['Common_StdTechID']):
+    sim_annual_std = pd.DataFrame()
+    for common_id, new_id in zip(StdTechIDs['Common_StdTechID'], StdTechIDs['StdTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Isolate specific common id (old)
+        sim_annual_std_mod = sim_annual_std_common[sim_annual_std_common['TechID']==common_id].copy()
+        #Change into final techID name (new)
+        sim_annual_std_mod['TechID'] = new_id
+        #merge to final df
+        sim_annual_std = pd.concat([sim_annual_std, sim_annual_std_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_annual_std = sim_annual_std_common.copy()
+# %%
+# create full msr sim_annual sets for different names but the same TechID
+if False in list(MeasTechIDs['MeasTechID']==MeasTechIDs['Common_MeasTechID']):
+    sim_annual_msr = pd.DataFrame()
+    for common_id, new_id in zip(MeasTechIDs['Common_MeasTechID'], MeasTechIDs['MeasTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Isolate specific common id (old)
+        sim_annual_msr_mod = sim_annual_msr_common[sim_annual_msr_common['TechID']==common_id].copy()
+        #Change into final techID name (new)
+        sim_annual_msr_mod['TechID'] = new_id
+        #merge to final df
+        sim_annual_msr = pd.concat([sim_annual_msr, sim_annual_msr_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_annual_msr = sim_annual_msr_common.copy()
+
+# %%
+#final merge sim_annual
+sim_annual_final = pd.concat([sim_annual_pre, sim_annual_std, sim_annual_msr])
+# %%
+
+###same deal with with hourly data, separate into pre std msr, change into specific TechID
+sim_hourly_pre_common = sim_hourly_f[sim_hourly_f['TechID'].isin(PreTechIDs['Common_PreTechID'].unique())]
+sim_hourly_std_common = sim_hourly_f[sim_hourly_f['TechID'].isin(StdTechIDs['Common_StdTechID'].unique())]
+sim_hourly_msr_common = sim_hourly_f[sim_hourly_f['TechID'].isin(MeasTechIDs['Common_MeasTechID'].unique())]
+
+# %%
+#Pre hourly
+if False in list(PreTechIDs['PreTechID']==PreTechIDs['Common_PreTechID']):
+    sim_hourly_pre = pd.DataFrame()
+    for _, (common_id, new_id) in PreTechIDs[['Common_PreTechID', 'PreTechID']].iterrows():
+        print(f'changing to specific PreTechID {new_id}')
+        sim_hourly_pre_mod = sim_hourly_pre_common[sim_hourly_pre_common['TechID']==common_id].copy()
+        sim_hourly_pre_mod['TechID'] = new_id
+        #merge to final df
+        sim_hourly_pre = pd.concat([sim_hourly_pre, sim_hourly_pre_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_hourly_pre = sim_hourly_pre_common.copy()
+# %%
+#Std hourly
+if False in list(StdTechIDs['StdTechID']==StdTechIDs['Common_StdTechID']):
+    sim_hourly_std = pd.DataFrame()
+    for common_id, new_id in zip(StdTechIDs['Common_StdTechID'], StdTechIDs['StdTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Isolate specific common id (old)
+        sim_hourly_std_mod = sim_hourly_std_common[sim_hourly_std_common['TechID']==common_id].copy()
+        #Change into final techID name (new)
+        sim_hourly_std_mod['TechID'] = new_id
+        #merge to final df
+        sim_hourly_std = pd.concat([sim_hourly_std, sim_hourly_std_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_hourly_std = sim_hourly_std_common.copy()
+# %%
+#Msr hourly
+if False in list(MeasTechIDs['MeasTechID']==MeasTechIDs['Common_MeasTechID']):
+    sim_hourly_msr = pd.DataFrame()
+    for common_id, new_id in zip(MeasTechIDs['Common_MeasTechID'], MeasTechIDs['MeasTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Isolate specific common id (old)
+        sim_hourly_msr_mod = sim_hourly_msr_common[sim_hourly_msr_common['TechID']==common_id].copy()
+        #Change into final techID name (new)
+        sim_hourly_msr_mod['TechID'] = new_id
+        #merge to final df
+        sim_hourly_msr = pd.concat([sim_hourly_msr, sim_hourly_msr_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_hourly_msr = sim_hourly_msr_common.copy()
+
+# %%
+#final merge sim_hourly
+sim_hourly_final = pd.concat([sim_hourly_pre, sim_hourly_std, sim_hourly_msr])
+
+# %%
+##Final export of all processed data pre-SQL process
+#change directory to wherever desired, if needed
+current_msr_mat.to_csv('current_msr_mat.csv', index=False)
+sim_annual_final.to_csv('sim_annual.csv', index=False)
+sim_hourly_final.to_csv('sim_hourly_wb.csv', index=False)
+
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/post-process/SFm.py b/residential measures/SWHC049-08 SEER Rated AC HP/post-process/SFm.py
new file mode 100644
index 000000000..115c7d919
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/post-process/SFm.py	
@@ -0,0 +1,884 @@
+#%%
+##STEP 0: Setup (import all necessary libraries)
+import pandas as pd
+import numpy as np
+import os
+import sys
+import datetime as dt
+os.chdir(os.path.dirname(__file__)) #resets to current script directory
+# %%
+#Read master workbook for measure / tech list
+df_master = pd.read_excel('DEER_EnergyPlus_Modelkit_Measure_list_working.xlsx', sheet_name='Measure_list', skiprows=4)
+
+measure_group_names = list(df_master['Measure Group Name'].unique())
+
+# %%
+#generate unique list of measure names
+measures = list(df_master['Measure (general name)'].unique())
+# %%
+#Shows list of measure names 
+print(measures)
+#%%
+#Define measure name here
+measure_name = 'SEER Rated AC HP'
+
+# %%
+#SFm only script
+####Define path
+os.chdir(os.path.dirname(__file__)) #resets to current script directory
+print(os.path.abspath(os.curdir))
+os.chdir("../..") #go up two directory
+print(os.path.abspath(os.curdir))
+
+#input the two subdirectory of SFm, one being 1975, the other 1985. If New vintage, input path at path_new and leave other blank.
+path_1975 = 'residential measures/SWHC049-06 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1975'
+path_1985 = 'residential measures/SWHC049-06 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_1985'
+path_new = 'residential measures/SWHC049-06 SEER Rated AC HP/SWHC049-06 SEER Rated AC HP_SFm_New'
+
+MODE_NEW_VINTAGE = False
+if MODE_NEW_VINTAGE:
+    paths = [path_new]
+else:
+    paths = [path_1975, path_1985]
+
+# %%
+#extract only the 5th portion of the measure group name for expected_att
+#split argument 4 means only split 4 times maximum
+techgroup_techtypes = [i.split('&', 4)[-1] for i in measure_group_names]
+tech_uniques = list(np.unique((np.array(techgroup_techtypes))))
+# Name parsing function per Amine
+# This dictionary of attributes should be updated
+expected_att = {
+    'BldgType': ['MFm','SFm', 'DMo'],
+    'Story': ['0','1','2'], # NA for Not Applicable
+    'BldgHVAC': ['rDXGF','rDXHP','rNCEH','rNCGF'],
+    'BldgVint': ['Ex','New'],
+    'Measure': tech_uniques
+}
+# function to parse meta data from & delimited case file names (Measure Group Name from master spreadsheet)
+def parse_measure_name(measure_name):
+    #split at most 4 times for 5 descriptor fields
+    measure_name_split = measure_name.split('&', 4) 
+    # Check here if the presented name has 5 attributes as expected:
+    if not len(measure_name_split) == 5:
+        sys.exit('The case name must have at least 5 attributes similar to < BldgType&Story&BldgHVAC&BldgVint&TechGroup__TechType >')
+    
+    attributes = list(expected_att.keys())
+    measure_name_dict = {attributes[i]: measure_name_split[i] for i in range(0,5)}
+
+    # Check here if the presented attributes are as expected:
+    for att in attributes:
+        if measure_name_dict[att] not in expected_att[att]:
+            sys.exit(f'Attribute <{measure_name_dict[att]}> was not expected')
+            
+
+    return measure_name_dict # returns a dictionary
+
+def parse_measure_name2(cohort_names: pd.Series, verify: bool = False) -> pd.DataFrame:
+    '''Returns a DataFrame with five columns (all type string):
+        ["BldgType","Story","BldgHVAC","BldgVint","TechGroup__TechType"]
+    Each cohort name must match the pattern:
+        "BldgType&Story&BldgHVAC&BldgVint&TechGroup__TechType"
+    Only alphanumeric characters are allowed  [a-zA-Z0-9_], except TechGroup__TechType may contain ampersand (&).
+
+    Parameters
+    ----------
+    cohort_names : pandas.Series
+        The cohort names as from cohorts.csv.
+    verify : bool, default=False
+        If true and name parts do not match `expected_att`, raise an exception.
+
+    Returns
+    -------
+    pandas.DataFrame
+        Structure containing the parts of cohort name.
+    '''
+    result = cohort_names.str.extract(
+        r'(?P<BldgType>\w+)&(?P<Story>\w+)&(?P<BldgHVAC>\w+)&(?P<BldgVint>\w+)&(?P<Measure>[^/]+)'
+    )
+    if verify:
+        # Check for missing descriptor fields
+        missing = result.isna()
+        if missing.any().any():
+            example = cohort_names[missing.any(axis=1)].iloc[0]
+            raise ValueError(f'Missing descriptor field, e.g. cohort = "{example}"')
+        # Check for unrecognized fields
+        for attr_name,attr_val in expected_att.items():
+            unrecognized = ~result[attr_name].isin(attr_val)
+            if unrecognized.any():
+                example = result[attr_name][unrecognized].iloc[0]
+                raise ValueError(f'Unrecognized descriptor field, e.g. {attr_name} = "{example}"')
+    result.rename({'Measure':'TechGroup__TechType'},axis=1,inplace=True)
+    return result
+
+#function to melt long 8760 col into 24col x365row format
+def long2wide_pivot(df, name):
+    '''
+    customized function. 
+    input df is long 8760, 1 column format, with the daynum(of365) and hour of day(of24) mapped.
+    id is the unique identifier.
+    
+    output df is the 24col x 365row format of the 8760, with the corresponding id.
+    '''
+    df_wide = df.pivot(index='daynum',columns='Hour', values=df.columns[0]).reset_index().rename_axis('', axis=1)
+    df_wide['ID']=name
+    #df_wide = pd.merge(df_wide, df_ag_key, on='ID')
+    
+    return df_wide
+
+#function to uses the “File Name” column from the results-summary csv to identify directory structure (an organized table) of a batch run 
+# and uses the structure to construct a semi-organized annual outputs table.
+def annual_raw_parsing(df, cohort_dict):
+    #create separated meta data cols
+    df['BldgLoc'] = split_meta_cols_eu[0]
+
+    df['BldgType'] = cohort_dict['BldgType']
+    df['BldgHVAC'] = cohort_dict['BldgHVAC']
+    df['BldgVint'] = cohort_dict['BldgVint']
+
+    df['Story'] = cohort_dict['Story']
+
+    df['TechGroup_TechType'] = cohort_dict['Measure']
+
+    df['TechID'] = split_meta_cols_eu[2]
+    df['file'] = split_meta_cols_eu[3]
+    
+    annual_df_v1 = df[['TechID', 'file', 'BldgLoc', 'BldgType','BldgHVAC','BldgVint','Story', 'TechGroup_TechType','Total (kWh)', 'Heating (kWh)', 'Cooling (kWh)',
+       'Interior Lighting (kWh)', 'Exterior Lighting (kWh)',
+       'Interior Equipment (kWh)', 'Exterior Equipment (kWh)', 'Fans (kWh)',
+       'Pumps (kWh)', 'Heat Rejection (kWh)', 'Humidification (kWh)',
+       'Heat Recovery (kWh)', 'Water Systems (kWh)', 'Refrigeration (kWh)',
+       'Generators (kWh)', 'Heating Elec (kWh)', 'Cooling Elec (kWh)',
+       'Heating NG (kWh)', 'Cooling NG (kWh)', 'Interior Equipment Elec (kWh)',
+       'Interior Equipment NG (kWh)']]
+    
+    return annual_df_v1
+
+#function to merge and rearrange specific annual consumption end-use fields into the format required
+def end_use_rearrange(df):
+        #end use rearrangement
+    df['kwh_tot'] = (df['Heating Elec (kWh)'] + \
+                                df['Cooling Elec (kWh)'] +\
+                                df['Interior Equipment Elec (kWh)'] +\
+                                df['Interior Lighting (kWh)'] +\
+                                df['Exterior Lighting (kWh)'] +\
+                                df['Fans (kWh)']+\
+                                df['Pumps (kWh)'])
+
+    df['kwh_ltg'] = (df['Interior Lighting (kWh)'] +\
+                                    df['Exterior Lighting (kWh)'])
+
+    df['kwh_task'] = 0 # placeholder (task lighting load?)
+
+    df['kwh_equip'] = df['Interior Equipment Elec (kWh)'] +\
+                                    df['Exterior Equipment (kWh)']
+
+    df['kwh_htg'] = df['Heating Elec (kWh)']
+
+    df['kwh_clg'] = df['Cooling Elec (kWh)']
+
+    df['kwh_twr'] = 0 #place holder (tower kwh load?)
+    df['kwh_aux'] = 0 #place holder (aux equipment kwh load?)
+    df['kwh_vent'] = df['Fans (kWh)'] #use fan kWh as vent load for now
+
+    df['kwh_venthtga'] =0 #placeholders fields..
+    df['kwh_ventclga'] =0
+
+    df['kwh_venthtgb'] =0
+    df['kwh_ventclgb'] =0
+
+    df['kwh_refg'] = 0 
+    df['kwh_hpsup'] = 0
+    df['kwh_shw'] = 0
+    df['kwh_ext'] = 0
+
+    df['thm_tot'] = (df['Heating NG (kWh)'] +\
+                                df['Cooling NG (kWh)'] +\
+                                df['Interior Equipment NG (kWh)'] +\
+                                df['Water Systems (kWh)'])/29.3
+
+    df['thm_equip'] = df['Interior Equipment NG (kWh)']/29.3
+
+    df['thm_htg'] = df['Heating NG (kWh)']/29.3
+
+    df['thm_shw'] = df['Water Systems (kWh)']/29.3
+
+    df['deskw_ltg'] = 1 #placeholders fields for now
+    df['deskw_equ'] = 1
+
+    annual_df_final = df[['TechID', 'BldgLoc', 'BldgType','BldgHVAC', 'BldgVint', 'kwh_tot', 'kwh_ltg', 'kwh_task',
+       'kwh_equip', 'kwh_htg', 'kwh_clg', 'kwh_twr', 'kwh_aux', 'kwh_vent',
+       'kwh_venthtga', 'kwh_ventclga', 'kwh_venthtgb', 'kwh_ventclgb',
+       'kwh_refg', 'kwh_hpsup', 'kwh_shw', 'kwh_ext', 'thm_tot', 'thm_equip',
+       'thm_htg', 'thm_shw']]
+    
+    return annual_df_final
+
+# %%
+#create measure specific Master table based on Measure selected
+df_measure = df_master[df_master['Measure (general name)'] == measure_name]
+case_cohort_list = df_measure['Measure Group Name'].unique()
+
+# %%
+##STEP 1: Annual data extraction / transformation
+sim_annual_raw = pd.DataFrame()
+for path in paths:
+    print(f'processing data in {path}')
+    df_raw = pd.read_csv(path+'/results-summary.csv', usecols=['File Name'])
+    num_runs = len(df_raw['File Name'].dropna().unique()) - 1
+    #Read annual data
+    annual_df = pd.read_csv(path+'/results-summary.csv', nrows=num_runs, skiprows=num_runs+2)
+    split_meta_cols_eu = annual_df['File Name'].str.split('/', expand=True)
+
+    #looping over multiple folders/cohort cases, use a list
+    cohort_cases = list(split_meta_cols_eu[1].unique())
+    sim_annual_proto = pd.DataFrame()
+    for case in cohort_cases:
+        print(f'processing all annual data that are grouped in {case}')
+        cohort_dict = parse_measure_name(case)
+        sim_annual_i = annual_raw_parsing(annual_df[annual_df['File Name'].str.contains(case)].copy(), cohort_dict)
+        sim_annual_proto = pd.concat([sim_annual_proto, sim_annual_i])
+        print('ok.')
+    sim_annual_raw = pd.concat([sim_annual_raw, sim_annual_proto])
+
+#%%
+
+#seperate into 1-story and 2-story, SFm, New vintage, (no need to separate DXGF and NCGF if combined)
+
+sim_annual_1s = sim_annual_raw[sim_annual_raw['Story']=='1'].copy()
+sim_annual_2s = sim_annual_raw[sim_annual_raw['Story']=='2'].copy()
+# %%
+#apply enduse rearrangement
+sim_annual_1s_v1 = end_use_rearrange(sim_annual_1s)
+sim_annual_2s_v1 = end_use_rearrange(sim_annual_2s)
+
+
+# %%
+##STEP 2: Hourly data extraction / transformation
+#Read 8760 map
+os.chdir(os.path.dirname(__file__)) #resets to current script directory
+print(os.path.abspath(os.curdir))
+annual_map = pd.read_excel('annual8760map.xlsx')
+hrly_paths = paths
+
+
+#%%
+os.chdir("../..") #go up two directories
+print(os.path.abspath(os.curdir))
+
+sim_hourly_raw = pd.DataFrame()
+
+for path in paths:
+    print(f'processing data in {path}')
+    hrly_path = path + '/runs' 
+
+    #extract data per bldgtype-bldghvac-bldgvint group
+    hourly_df = pd.DataFrame(index=range(0,8760))
+    #extract num_runs / split_meta_cols_eu
+    df_raw = pd.read_csv(path+'/results-summary.csv', usecols=['File Name'])
+    num_runs = len(df_raw['File Name'].dropna().unique()) - 1
+    annual_df = pd.read_csv(path+'/results-summary.csv', nrows=num_runs, skiprows=num_runs+2)
+    split_meta_cols_eu = annual_df['File Name'].str.split('/', expand=True)
+    for i in range(0,num_runs):
+        print(f"merging record {i}")
+        
+        #loop path of each file, read corresponding file
+        full_path = hrly_path + "/" + split_meta_cols_eu.iloc[i][0] + "/" + split_meta_cols_eu.iloc[i][1] + "/" + split_meta_cols_eu.iloc[i][2] + "/instance-var.csv"
+        df = pd.read_csv(full_path, low_memory=False)
+        
+        #remove traling spaces on col headers
+        df.columns = df.columns.str.rstrip()
+
+        #extract the last column (the total elec hrly profile)
+        #if for enduse hourly, then extract the relevant end use column
+        extracted_df = pd.DataFrame(df['Electricity:Facility [J](Hourly)'])
+        
+        #create the column name based on the permutations
+        col_name = split_meta_cols_eu.iloc[i][0] + "/" + split_meta_cols_eu.iloc[i][1] + "/" + split_meta_cols_eu.iloc[i][2] + "/instance-var.csv"
+        
+        #change column name
+        extracted_df = extracted_df.set_axis([col_name],axis=1)
+        if len(extracted_df)!=8760:
+            #8/31/2022 update, need to make the final length 8808. Snip data based on difference to 8760
+            record_count_diff = len(extracted_df) - 8760
+            print(f'extra records: {str(len(extracted_df))}, snipping away {record_count_diff} records and changing to 8760')
+            extracted_df = extracted_df.iloc[record_count_diff:].reset_index(drop=True)
+        
+        #left-merge onto big df
+        hourly_df = hourly_df.merge(extracted_df, left_index=True, right_index=True)
+    
+
+    fyr_hrly = hourly_df
+    #rearrange 1-column 8760 format to 365x24 wide format for all runs in hourly_df
+    converted_df = pd.DataFrame()
+
+    for i in range(0,len(fyr_hrly.columns)):
+        
+        #isolate single column
+        hrly_df = pd.DataFrame(fyr_hrly.iloc[:,i])
+        
+        #create separate metadata columns
+        col_names = hrly_df.columns[0].split('/')
+        
+        #create new key column for merge
+        hrly_df['hr in 8760'] = (hrly_df.index) + 1
+        
+        #merge based on "hr in 8760" column, the 8760 map
+        hrly_mapped = pd.merge(hrly_df, annual_map, on='hr in 8760')
+        
+        #transform data format
+        hrly_wide = long2wide_pivot(hrly_mapped, hrly_mapped.columns[0])
+        
+        #add meta data col
+        hrly_wide['BldgLoc'] = col_names[0]
+        hrly_wide['BldgType'] = col_names[1]
+        hrly_wide['TechID'] = col_names[2]
+        hrly_wide['file'] = col_names[3]
+        
+        #append to master df
+        #converted_df = converted_df.append(hrly_wide) #deprecated method
+        converted_df = pd.concat([converted_df, hrly_wide])
+        print(f"col {i} transformed.")
+
+    sim_hourly_raw = pd.concat([sim_hourly_raw, converted_df])
+
+
+# %%
+#rearrange columns
+sim_hourly_wb_proto = sim_hourly_raw[['TechID','file','BldgLoc','BldgType','ID','daynum',1,          2,          3,          4,          5,
+                6,          7,          8,          9,         10,         11,
+               12,         13,         14,         15,         16,         17,
+               18,         19,         20,         21,         22,         23,
+               24]].copy()
+
+#hourly data conversion
+#convert unit (J) to (kWh) for hourly
+
+sim_hourly_wb_proto['hr01'] = sim_hourly_wb_proto[1]/3600000
+sim_hourly_wb_proto['hr02'] = sim_hourly_wb_proto[2]/3600000
+sim_hourly_wb_proto['hr03'] = sim_hourly_wb_proto[3]/3600000
+sim_hourly_wb_proto['hr04'] = sim_hourly_wb_proto[4]/3600000
+sim_hourly_wb_proto['hr05'] = sim_hourly_wb_proto[5]/3600000
+sim_hourly_wb_proto['hr06'] = sim_hourly_wb_proto[6]/3600000
+sim_hourly_wb_proto['hr07'] = sim_hourly_wb_proto[7]/3600000
+sim_hourly_wb_proto['hr08'] = sim_hourly_wb_proto[8]/3600000
+sim_hourly_wb_proto['hr09'] = sim_hourly_wb_proto[9]/3600000
+sim_hourly_wb_proto['hr10'] = sim_hourly_wb_proto[10]/3600000
+sim_hourly_wb_proto['hr11'] = sim_hourly_wb_proto[11]/3600000
+sim_hourly_wb_proto['hr12'] = sim_hourly_wb_proto[12]/3600000
+sim_hourly_wb_proto['hr13'] = sim_hourly_wb_proto[13]/3600000
+sim_hourly_wb_proto['hr14'] = sim_hourly_wb_proto[14]/3600000
+sim_hourly_wb_proto['hr15'] = sim_hourly_wb_proto[15]/3600000
+sim_hourly_wb_proto['hr16'] = sim_hourly_wb_proto[16]/3600000
+sim_hourly_wb_proto['hr17'] = sim_hourly_wb_proto[17]/3600000
+sim_hourly_wb_proto['hr18'] = sim_hourly_wb_proto[18]/3600000
+sim_hourly_wb_proto['hr19'] = sim_hourly_wb_proto[19]/3600000
+sim_hourly_wb_proto['hr20'] = sim_hourly_wb_proto[20]/3600000
+sim_hourly_wb_proto['hr21'] = sim_hourly_wb_proto[21]/3600000
+sim_hourly_wb_proto['hr22'] = sim_hourly_wb_proto[22]/3600000
+sim_hourly_wb_proto['hr23'] = sim_hourly_wb_proto[23]/3600000
+sim_hourly_wb_proto['hr24'] = sim_hourly_wb_proto[24]/3600000
+
+#rearrange columns
+sim_hourly_wb_v1 = sim_hourly_wb_proto[['TechID','file','BldgLoc','BldgType','ID','daynum','hr01','hr02','hr03','hr04','hr05','hr06',
+           'hr07',     'hr08',     'hr09',     'hr10',     'hr11',     'hr12',
+           'hr13',     'hr14',     'hr15',     'hr16',     'hr17',     'hr18',
+           'hr19',     'hr20',     'hr21',     'hr22',     'hr23',     'hr24']]
+# %%
+#separate into 1s and 2s for hourly
+sim_hourly_wb_1s_v1 = sim_hourly_wb_v1[sim_hourly_wb_v1['BldgType'].str.contains('&1&')].copy()
+sim_hourly_wb_2s_v1 = sim_hourly_wb_v1[sim_hourly_wb_v1['BldgType'].str.contains('&2&')].copy()
+# %%
+##STEP 3: 1-S, 2-S combination, and Normalizing Units
+# annual data
+rename_1s_fields = {'kwh_tot':'kwh_tot1', 
+                    'kwh_ltg':'kwh_ltg1',
+                    'kwh_task':'kwh_task1', 
+                    'kwh_equip':'kwh_equip1',
+                    'kwh_htg':'kwh_htg1',
+                    'kwh_clg':'kwh_clg1',                    
+                    'kwh_twr':'kwh_twr1',
+                    'kwh_aux':'kwh_aux1', 
+                    'kwh_vent':'kwh_vent1',
+                    'kwh_venthtga':'kwh_venthtg1a', 
+                    'kwh_ventclga':'kwh_ventclg1a', 
+                    'kwh_venthtgb':'kwh_venthtg1b', 
+                    'kwh_ventclgb':'kwh_ventclg1b',
+                    'kwh_refg':'kwh_refg1', 
+                    'kwh_hpsup':'kwh_hpsup1',
+                    'kwh_shw':'kwh_shw1', 
+                    'kwh_ext':'kwh_ext1', 
+                    'thm_tot':'thm_tot1',
+                    'thm_htg':'thm_htg1',
+                    'thm_equip':'thm_equip1',
+                    'thm_shw':'thm_shw1'}
+
+rename_2s_fields = {'kwh_tot':'kwh_tot2', 
+                    'kwh_ltg':'kwh_ltg2',
+                    'kwh_task':'kwh_task2', 
+                    'kwh_equip':'kwh_equip2',
+                    'kwh_htg':'kwh_htg2',
+                    'kwh_clg':'kwh_clg2',                    
+                    'kwh_twr':'kwh_twr2',
+                    'kwh_aux':'kwh_aux2', 
+                    'kwh_vent':'kwh_vent2',
+                    'kwh_venthtga':'kwh_venthtg2a', 
+                    'kwh_ventclga':'kwh_ventclg2a', 
+                    'kwh_venthtgb':'kwh_venthtg2b', 
+                    'kwh_ventclgb':'kwh_ventclg2b',
+                    'kwh_refg':'kwh_refg2', 
+                    'kwh_hpsup':'kwh_hpsup2',
+                    'kwh_shw':'kwh_shw2', 
+                    'kwh_ext':'kwh_ext2', 
+                    'thm_tot':'thm_tot2',
+                    'thm_htg':'thm_htg2',
+                    'thm_equip':'thm_equip2',
+                    'thm_shw':'thm_shw2'}
+
+#rename columns
+sim_annual_1s = sim_annual_1s_v1.rename(columns=rename_1s_fields)
+sim_annual_2s = sim_annual_2s_v1.rename(columns=rename_2s_fields)
+
+# %%
+####Finalize Norm units
+#Read from normunit table
+bldgtype = 'SFm'
+os.chdir(os.path.dirname(__file__)) #resets to current script directory
+print(os.path.abspath(os.curdir))
+df_normunits = pd.read_excel('Normunits.xlsx', sheet_name=bldgtype)
+numunits_vals = df_normunits[df_normunits['Normunit'] == df_measure['Normunit'].unique()[0]][['CZ','Value', 'Msr','BldgVint']]
+
+#%%
+
+#create numunits object based on what normunit it uses. 
+#numunits can be a single value, or a dictionary
+if len(numunits_vals) == 1:
+    numunits = numunits_vals[0]
+elif df_measure['Normunit'].unique()[0] == 'Area-ft2-BA':
+    cz = list(numunits_vals['CZ'])
+    nvals = list(numunits_vals['Value'])
+    #create dictionary of {cz:values}
+    numunits = {cz[i]:nvals[i] for i in range(len(cz))}
+elif (measure_name == 'Wall Insulation') or (measure_name == 'Ceiling Insulation'):
+    #filter to the corresponding measure
+    numunits_vals = numunits_vals[numunits_vals['Msr'] == measure_name]
+    #create aligned lists for numunit dictionary
+    cz = list(numunits_vals['CZ'])
+    nvals = list(numunits_vals['Value'])
+    #create dictionary of {cz:values}
+    numunits = {cz[i]:nvals[i] for i in range(len(cz))}
+
+elif measure_name == 'PTAC / PTHP':
+    #create aligned lists for numunit dictionary
+    cz = list(numunits_vals['CZ'])
+    vint = list(numunits_vals['BldgVint'])
+    nvals = list(numunits_vals['Value'])
+    #create dictionary of {(cz,vintage):numunits}
+    numunits = {(cz[i],vint[i]):nvals[i] for i in range(len(cz))}
+else:
+    pass
+
+# %%
+#note HVAC type of this dataset
+print(sim_annual_1s['BldgHVAC'].unique())
+
+#note HVAC type and its corresponding normalizing unit
+#num unit will be per dwelling, so use normunit / num of dwellings (2 for SFM & DMo, 24 for MFm)
+sim_annual_1s.reset_index(inplace=True)
+sim_annual_1s['SizingID'] = 'None'
+sim_annual_1s['tstat'] = 0
+sim_annual_1s['normunit'] = df_measure['Normunit'].unique()[0]
+
+#apply numunits appropriately
+if (measure_name == 'Wall Insulation') or (measure_name == 'Ceiling Insulation'):
+    sim_annual_1s['numunits'] = (sim_annual_1s['BldgLoc'].map(numunits))/2
+elif df_measure['Normunit'].unique()[0] == 'Area-ft2-BA':
+    sim_annual_1s['numunits'] = (sim_annual_1s['BldgLoc'].map(numunits))/2
+elif measure_name == 'PTAC / PTHP':
+    sim_annual_1s['numunits'] = (pd.Series(list(zip(sim_annual_1s['BldgLoc'],sim_annual_1s['BldgVint']))).map(numunits))/2
+else:
+    sim_annual_1s['numunits'] = numunits/2
+
+#%%
+sim_annual_2s.reset_index(inplace=True)
+sim_annual_2s['SizingID'] = 'None'
+sim_annual_2s['tstat'] = 0
+sim_annual_2s['normunit'] = df_measure['Normunit'].unique()[0]
+
+#apply numunits appropriately
+if (measure_name == 'Wall Insulation') or (measure_name == 'Ceiling Insulation'):
+    sim_annual_2s['numunits'] = (sim_annual_2s['BldgLoc'].map(numunits))/2
+elif df_measure['Normunit'].unique()[0] == 'Area-ft2-BA':
+    sim_annual_2s['numunits'] = (sim_annual_2s['BldgLoc'].map(numunits))/2
+elif measure_name == 'PTAC / PTHP':
+    sim_annual_2s['numunits'] = (pd.Series(list(zip(sim_annual_2s['BldgLoc'],sim_annual_2s['BldgVint']))).map(numunits))/2
+else:
+    sim_annual_2s['numunits'] = numunits/2
+
+#%%
+
+#merge 1-s and 2-s results back together side by side
+sim_annual_final = pd.merge(sim_annual_1s, sim_annual_2s, on=['TechID', 'SizingID', 'BldgType', 'BldgLoc','BldgHVAC','BldgVint','tstat','normunit','numunits'])
+sim_annual_final['lastmod']=dt.datetime.now()
+
+#%%
+#rearrange columns
+sim_annual_f = sim_annual_final[['TechID', 'SizingID', 'BldgType','BldgVint','BldgLoc','BldgHVAC','tstat',
+       'normunit', 'numunits', 'kwh_tot1', 'kwh_ltg1', 'kwh_task1',
+       'kwh_equip1', 'kwh_htg1', 'kwh_clg1', 'kwh_twr1', 'kwh_aux1',
+       'kwh_vent1', 'kwh_venthtg1a', 'kwh_ventclg1a', 'kwh_venthtg1b',
+       'kwh_ventclg1b', 'kwh_refg1', 'kwh_hpsup1', 'kwh_shw1', 'kwh_ext1',
+       'thm_tot1', 'thm_equip1', 'thm_htg1', 'thm_shw1','kwh_tot2', 'kwh_ltg2',
+       'kwh_task2', 'kwh_equip2', 'kwh_htg2', 'kwh_clg2', 'kwh_twr2',
+       'kwh_aux2', 'kwh_vent2', 'kwh_venthtg2a', 'kwh_ventclg2a',
+       'kwh_venthtg2b', 'kwh_ventclg2b', 'kwh_refg2', 'kwh_hpsup2', 'kwh_shw2',
+       'kwh_ext2', 'thm_tot2', 'thm_equip2', 'thm_htg2', 'thm_shw2', 'lastmod']]
+# %%
+###Finalize Hourly data
+
+#hourly data
+#1story
+#rename some fields to fit MC3 output
+#add some placeholder fields to fit MC3 output
+df_tmp = parse_measure_name2(sim_hourly_wb_1s_v1['ID'],verify=True)
+sim_hourly_wb_1s_v1['BldgVint'] = df_tmp['BldgVint']
+sim_hourly_wb_1s_v1['BldgHVAC'] = df_tmp['BldgHVAC']
+sim_hourly_wb_1s_v1['BldgType'] = df_tmp['BldgType']
+del df_tmp
+sim_hourly_wb_1s_v1['SizingID'] = 'None'
+sim_hourly_wb_1s_v1['tstat'] = 0
+sim_hourly_wb_1s_v1['enduse'] = 0
+
+#2story
+#rename some fields to fit MC3 output
+#add some placeholder fields to fit MC3 output
+df_tmp = parse_measure_name2(sim_hourly_wb_2s_v1['ID'],verify=True)
+sim_hourly_wb_2s_v1['BldgVint'] = df_tmp['BldgVint']
+sim_hourly_wb_2s_v1['BldgHVAC'] = df_tmp['BldgHVAC']
+sim_hourly_wb_2s_v1['BldgType'] = df_tmp['BldgType']
+del df_tmp
+sim_hourly_wb_2s_v1['SizingID'] = 'None'
+sim_hourly_wb_2s_v1['tstat'] = 0
+sim_hourly_wb_2s_v1['enduse'] = 0
+# %%
+rename_1s_hrly = {'hr01':'hr01a', 
+                  'hr02':'hr02a',
+                  'hr03':'hr03a', 
+                  'hr04':'hr04a',
+                  'hr05':'hr05a', 
+                  'hr06':'hr06a', 
+                  'hr07':'hr07a', 
+                  'hr08':'hr08a', 
+                  'hr09':'hr09a', 
+                  'hr10':'hr10a', 
+                  'hr11':'hr11a',
+                  'hr12':'hr12a', 
+                  'hr13':'hr13a', 
+                  'hr14':'hr14a', 
+                  'hr15':'hr15a', 
+                  'hr16':'hr16a', 
+                  'hr17':'hr17a', 
+                  'hr18':'hr18a', 
+                  'hr19':'hr19a', 
+                  'hr20':'hr20a',
+                  'hr21':'hr21a', 
+                  'hr22':'hr22a', 
+                  'hr23':'hr23a', 
+                  'hr24':'hr24a'}
+rename_2s_hrly = {'hr01':'hr01b', 
+                  'hr02':'hr02b',
+                  'hr03':'hr03b', 
+                  'hr04':'hr04b',
+                  'hr05':'hr05b', 
+                  'hr06':'hr06b', 
+                  'hr07':'hr07b', 
+                  'hr08':'hr08b', 
+                  'hr09':'hr09b', 
+                  'hr10':'hr10b', 
+                  'hr11':'hr11b',
+                  'hr12':'hr12b', 
+                  'hr13':'hr13b', 
+                  'hr14':'hr14b', 
+                  'hr15':'hr15b', 
+                  'hr16':'hr16b', 
+                  'hr17':'hr17b', 
+                  'hr18':'hr18b', 
+                  'hr19':'hr19b', 
+                  'hr20':'hr20b',
+                  'hr21':'hr21b', 
+                  'hr22':'hr22b', 
+                  'hr23':'hr23b', 
+                  'hr24':'hr24b'}
+
+#rename hourly columns
+sim_hourly_1s = sim_hourly_wb_1s_v1.rename(columns=rename_1s_hrly)
+sim_hourly_2s = sim_hourly_wb_2s_v1.rename(columns=rename_2s_hrly)
+
+#rearrange cols
+sim_hourly_1s_re = sim_hourly_1s[['TechID','SizingID','BldgType','BldgVint','BldgLoc','BldgHVAC','tstat','enduse','daynum','hr01a',
+       'hr02a', 'hr03a', 'hr04a', 'hr05a', 'hr06a', 'hr07a', 'hr08a', 'hr09a',
+       'hr10a', 'hr11a', 'hr12a', 'hr13a', 'hr14a', 'hr15a', 'hr16a', 'hr17a',
+       'hr18a', 'hr19a', 'hr20a', 'hr21a', 'hr22a', 'hr23a', 'hr24a']]
+
+sim_hourly_2s_re = sim_hourly_2s[['TechID','SizingID','BldgType','BldgVint','BldgLoc','BldgHVAC','tstat','enduse','daynum','hr01b',
+       'hr02b', 'hr03b', 'hr04b', 'hr05b', 'hr06b', 'hr07b', 'hr08b', 'hr09b',
+       'hr10b', 'hr11b', 'hr12b', 'hr13b', 'hr14b', 'hr15b', 'hr16b', 'hr17b',
+       'hr18b', 'hr19b', 'hr20b', 'hr21b', 'hr22b', 'hr23b', 'hr24b']]
+
+#merge columns of 1s and 2s
+sim_hourly_final = pd.merge(sim_hourly_1s_re, sim_hourly_2s_re, on=['TechID', 'SizingID','BldgType','BldgVint','BldgLoc','BldgHVAC','tstat','enduse','daynum'])
+sim_hourly_final['lastmod']=dt.datetime.now()
+
+#%%
+#Create CZ:VintYear dictionary based on prototype definitions
+cz_list1 = ['CZ01','CZ02','CZ03','CZ04','CZ05','CZ06','CZ07','CZ08','CZ09']
+cz_list2 = ['CZ10','CZ11','CZ12','CZ13','CZ14','CZ15','CZ16']
+
+cz_vint_dict1 = {i:'1975' for i in cz_list1}
+cz_vint_dict2 = {i:'1985' for i in cz_list2}
+
+cz_vint_dict = cz_vint_dict1 | cz_vint_dict2
+
+#%%
+if MODE_NEW_VINTAGE:
+    pass
+else:
+    ##BldgVint label correction for NumStor weights
+    sim_annual_f['BldgVint'] = sim_annual_f['BldgLoc'].map(cz_vint_dict)
+    sim_hourly_final['BldgVint'] = sim_hourly_final['BldgLoc'].map(cz_vint_dict)
+
+# %%
+##STEP 4: Measure setup file (current_msr_mat.csv)
+
+# Creating current_msr_mat and finalzing TechID's
+
+metadata_cols = sim_annual_f[['TechID', 'BldgLoc', 'BldgType', 'BldgVint', 'BldgHVAC', 'SizingID',
+       'tstat', 'normunit']]
+
+#check unique TechID cases
+metadata_cols['TechID'].unique()
+# %%
+#TechID identification from Master table
+#if looping over all HVAC types, ignore BldgHVAC filter
+PreTechIDs = df_measure[['PreTechID','Common_PreTechID']].drop_duplicates()
+StdTechIDs = df_measure[['StdTechID','Common_StdTechID']].drop_duplicates()
+MeasTechIDs = df_measure[['MeasTechID','Common_MeasTechID']].drop_duplicates()
+# %%
+#filter out each pre, std, msr using the Common TechIDs from master table
+metadata_pre = metadata_cols[metadata_cols['TechID'].isin(PreTechIDs['Common_PreTechID'].unique())]
+metadata_std = metadata_cols[metadata_cols['TechID'].isin(StdTechIDs['Common_StdTechID'].unique())]
+metadata_msr = metadata_cols[metadata_cols['TechID'].isin(MeasTechIDs['Common_MeasTechID'].unique())]
+
+# %%
+#rename to Pre, Std or Msr 
+#both Std and Pre are baseline for SEER rated AC measures
+metadata_pre = metadata_pre.rename(columns={'TechID':'PreTechID'})
+metadata_std = metadata_std.rename(columns={'TechID':'StdTechID'})
+metadata_msr = metadata_msr.rename(columns={'TechID':'MeasTechID'})
+# %%
+#Changing common TechID to actual TechIDs if needed.
+#might only apply to SEER AC/HP
+
+#create full pre_metadata sets for different names but the same TechID
+# commom_preTechID = PreTechIDs['Common_PreTechID'].unique()[0]
+if False in list(PreTechIDs['PreTechID']==PreTechIDs['Common_PreTechID']):
+    metadata_pre_full = pd.DataFrame()
+    for _, (common_id, new_id) in PreTechIDs[['Common_PreTechID', 'PreTechID']].iterrows():
+        print(f'changing to specific PreTechID {new_id}')
+        metadata_pre_mod = metadata_pre[metadata_pre['PreTechID']==common_id].copy()
+        metadata_pre_mod['PreTechID'] = new_id
+        #merge to final df
+        metadata_pre_full = pd.concat([metadata_pre_full, metadata_pre_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    metadata_pre_full = metadata_pre.copy()
+
+
+# %%
+#create std_metadata sets, assigning appropriate final TechIDs
+if False in list(StdTechIDs['StdTechID']==StdTechIDs['Common_StdTechID']):
+    metadata_std_full = pd.DataFrame()
+    for common_id, new_id in zip(StdTechIDs['Common_StdTechID'], StdTechIDs['StdTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Isolate specific common id (old)
+        metadata_std_mod = metadata_std[metadata_std['StdTechID']==common_id].copy()
+        #Change into final techID name (new)
+        metadata_std_mod['StdTechID'] = new_id
+        #merge to final df
+        metadata_std_full = pd.concat([metadata_std_full, metadata_std_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    metadata_std_full = metadata_std.copy()
+# %%
+#create msr_metadata sets, assigning appropriate final TechIDs
+if False in list(MeasTechIDs['MeasTechID']==MeasTechIDs['Common_MeasTechID']):
+    metadata_msr_full = pd.DataFrame()
+    for common_id, new_id in zip(MeasTechIDs['Common_MeasTechID'], MeasTechIDs['MeasTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Identify corresponding common TechID (the last 9 characters indicating SEER levels)
+        metadata_msr_mod = metadata_msr[metadata_msr['MeasTechID']==common_id].copy()
+        #Change into final TechID name
+        metadata_msr_mod['MeasTechID'] = new_id
+        #merge to final df
+        metadata_msr_full = pd.concat([metadata_msr_full, metadata_msr_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    metadata_msr_full = metadata_msr.copy()
+# %%
+#create raw merged current_msr_mat
+#need to delete/drop incorrect sets
+if np.NaN in list(StdTechIDs['StdTechID'].unique()):
+    df_measure_set_full = pd.merge(metadata_pre_full, metadata_msr_full, on=['BldgLoc','BldgType','BldgVint','BldgHVAC','SizingID','tstat','normunit'])
+elif np.NaN in list(PreTechIDs['PreTechID'].unique()):
+    df_measure_set_full = pd.merge(metadata_std_full, metadata_msr_full, on=['BldgLoc','BldgType','BldgVint','BldgHVAC','SizingID','tstat','normunit'])
+else:
+    df_measure_baseline_full = pd.merge(metadata_pre_full, metadata_std_full, on=['BldgLoc','BldgType','BldgVint','BldgHVAC','SizingID','tstat','normunit'])
+    df_measure_set_full = pd.merge(df_measure_baseline_full, metadata_msr_full, on=['BldgLoc','BldgType','BldgVint','BldgHVAC','SizingID','tstat','normunit'])
+# %%
+#Unique sets of each MeasureID with their TechID triplets
+TechID_triplets = df_measure[['EnergyImpactID','MeasureID', 'PreTechID', 'StdTechID','MeasTechID']].drop_duplicates()
+# %%
+#to match TechID triplets, merge on these 3 fields, keeping only valid TechID Triplets
+if np.NaN in list(StdTechIDs['StdTechID'].unique()):
+    current_msr_mat_proto = pd.merge(df_measure_set_full, TechID_triplets, on=['PreTechID','MeasTechID'])
+elif np.NaN in list(PreTechIDs['PreTechID'].unique()):
+    current_msr_mat_proto = pd.merge(df_measure_set_full, TechID_triplets, on=['StdTechID','MeasTechID'])
+else:
+    current_msr_mat_proto = pd.merge(df_measure_set_full, TechID_triplets, on=['PreTechID','StdTechID','MeasTechID'])
+
+# %%
+#add placeholders, rearrange fields
+current_msr_mat_proto['PreSizingID']='None'
+current_msr_mat_proto['StdSizingID']='None'
+current_msr_mat_proto['MsrSizingID']='None'
+current_msr_mat_proto['SizingSrc']=np.nan
+
+#to be worked on: need to add corresponding indicator for what enduse it is for end use loadshape connections
+current_msr_mat_proto['EU_HrRepVar']=np.nan
+
+current_msr_mat = current_msr_mat_proto[['MeasureID', 'BldgType', 'BldgVint','BldgLoc','BldgHVAC','tstat','PreTechID','PreSizingID',
+                             'StdTechID', 'StdSizingID','MeasTechID','MsrSizingID','SizingSrc','EU_HrRepVar','normunit']]
+current_msr_mat = current_msr_mat.rename(columns={'normunit':'NormUnit'})
+
+#%%
+#check length of current_msr_mat
+len(current_msr_mat)
+
+# %%
+##STEP 5: Clean Up Sequence
+# Creating updated Sim_annual and Sim_hourly data with distinguished TechID names
+sim_annual_pre_common = sim_annual_f[sim_annual_f['TechID'].isin(PreTechIDs['Common_PreTechID'].unique())]
+sim_annual_std_common = sim_annual_f[sim_annual_f['TechID'].isin(StdTechIDs['Common_StdTechID'].unique())]
+sim_annual_msr_common = sim_annual_f[sim_annual_f['TechID'].isin(MeasTechIDs['Common_MeasTechID'].unique())]
+# %%
+#Add a TechID col renaming the common TechID to the specific TechID using PreTechIDs, StdTechIDs, MeasTechIDs
+
+#create full pre sim_annual sets for different names but the same TechID
+# commom_preTechID = PreTechIDs['Common_PreTechID'].unique()[0]
+if False in list(PreTechIDs['PreTechID']==PreTechIDs['Common_PreTechID']):
+    sim_annual_pre = pd.DataFrame()
+    for _, (common_id, new_id) in PreTechIDs[['Common_PreTechID', 'PreTechID']].iterrows():
+        print(f'changing to specific PreTechID {new_id}')
+        sim_annual_pre_mod = sim_annual_pre_common[sim_annual_pre_common['TechID']==common_id].copy()
+        sim_annual_pre_mod['TechID'] = new_id
+        #merge to final df
+        sim_annual_pre = pd.concat([sim_annual_pre, sim_annual_pre_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_annual_pre = sim_annual_pre_common.copy()
+
+# %%
+# create full std sim_annual sets for different names but the same TechID
+if False in list(StdTechIDs['StdTechID']==StdTechIDs['Common_StdTechID']):
+    sim_annual_std = pd.DataFrame()
+    for common_id, new_id in zip(StdTechIDs['Common_StdTechID'], StdTechIDs['StdTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Isolate specific common id (old)
+        sim_annual_std_mod = sim_annual_std_common[sim_annual_std_common['TechID']==common_id].copy()
+        #Change into final techID name (new)
+        sim_annual_std_mod['TechID'] = new_id
+        #merge to final df
+        sim_annual_std = pd.concat([sim_annual_std, sim_annual_std_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_annual_std = sim_annual_std_common.copy()
+# %%
+# create full msr sim_annual sets for different names but the same TechID
+if False in list(MeasTechIDs['MeasTechID']==MeasTechIDs['Common_MeasTechID']):
+    sim_annual_msr = pd.DataFrame()
+    for common_id, new_id in zip(MeasTechIDs['Common_MeasTechID'], MeasTechIDs['MeasTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Isolate specific common id (old)
+        sim_annual_msr_mod = sim_annual_msr_common[sim_annual_msr_common['TechID']==common_id].copy()
+        #Change into final techID name (new)
+        sim_annual_msr_mod['TechID'] = new_id
+        #merge to final df
+        sim_annual_msr = pd.concat([sim_annual_msr, sim_annual_msr_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_annual_msr = sim_annual_msr_common.copy()
+# %%
+#final merge sim_annual
+sim_annual_final = pd.concat([sim_annual_pre, sim_annual_std, sim_annual_msr])
+# %%
+
+###same deal with with hourly data, separate into pre std msr, change into specific TechID
+sim_hourly_pre_common = sim_hourly_final[sim_hourly_final['TechID'].isin(PreTechIDs['Common_PreTechID'].unique())]
+sim_hourly_std_common = sim_hourly_final[sim_hourly_final['TechID'].isin(StdTechIDs['Common_StdTechID'].unique())]
+sim_hourly_msr_common = sim_hourly_final[sim_hourly_final['TechID'].isin(MeasTechIDs['Common_MeasTechID'].unique())]
+
+# %%
+#Pre hourly
+if False in list(PreTechIDs['PreTechID']==PreTechIDs['Common_PreTechID']):
+    sim_hourly_pre = pd.DataFrame()
+    for _, (common_id, new_id) in PreTechIDs[['Common_PreTechID', 'PreTechID']].iterrows():
+        print(f'changing to specific PreTechID {new_id}')
+        sim_hourly_pre_mod = sim_hourly_pre_common[sim_hourly_pre_common['TechID']==common_id].copy()
+        sim_hourly_pre_mod['TechID'] = new_id
+        #merge to final df
+        sim_hourly_pre = pd.concat([sim_hourly_pre, sim_hourly_pre_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_hourly_pre = sim_hourly_pre_common.copy()
+# %%
+#Std hourly
+if False in list(StdTechIDs['StdTechID']==StdTechIDs['Common_StdTechID']):
+    sim_hourly_std = pd.DataFrame()
+    for common_id, new_id in zip(StdTechIDs['Common_StdTechID'], StdTechIDs['StdTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Isolate specific common id (old)
+        sim_hourly_std_mod = sim_hourly_std_common[sim_hourly_std_common['TechID']==common_id].copy()
+        #Change into final techID name (new)
+        sim_hourly_std_mod['TechID'] = new_id
+        #merge to final df
+        sim_hourly_std = pd.concat([sim_hourly_std, sim_hourly_std_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_hourly_std = sim_hourly_std_common.copy()
+# %%
+#Msr hourly
+if False in list(MeasTechIDs['MeasTechID']==MeasTechIDs['Common_MeasTechID']):
+    sim_hourly_msr = pd.DataFrame()
+    for common_id, new_id in zip(MeasTechIDs['Common_MeasTechID'], MeasTechIDs['MeasTechID']):
+        print(f'common is {common_id}, changing into new id is {new_id}')
+        #Isolate specific common id (old)
+        sim_hourly_msr_mod = sim_hourly_msr_common[sim_hourly_msr_common['TechID']==common_id].copy()
+        #Change into final techID name (new)
+        sim_hourly_msr_mod['TechID'] = new_id
+        #merge to final df
+        sim_hourly_msr = pd.concat([sim_hourly_msr, sim_hourly_msr_mod])
+else:
+    print('same TechID, proceeding without changing names')
+    sim_hourly_msr = sim_hourly_msr_common.copy()
+
+# %%
+#final merge sim_hourly
+sim_hourly_final = pd.concat([sim_hourly_pre, sim_hourly_std, sim_hourly_msr])
+# %%
+##Final export of all processed data pre-SQL process
+#change directory to wherever desired, if needed
+current_msr_mat.to_csv('current_msr_mat.csv', index=False)
+sim_annual_final.to_csv('sfm_annual.csv', index=False)
+sim_hourly_final.to_csv('sfm_hourly_wb.csv', index=False)
+
+# %%
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/post-process/run_residential_processing_SWHC049.psql b/residential measures/SWHC049-08 SEER Rated AC HP/post-process/run_residential_processing_SWHC049.psql
new file mode 100644
index 000000000..20f842e09
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/post-process/run_residential_processing_SWHC049.psql	
@@ -0,0 +1,199 @@
+/*
+run_step5.psql
+Nicholas Fette, 2023-01-05
+
+Import all the results data into PostgreSQL, and run all the post-processing scripts.
+Warning: deletes the results data currently in the database.
+
+Usage on command line:
+  cd "C:/.../DEER-Prototypes-EnergyPlus/scripts/"
+  psql -d "dbname" -U "dbuser" -f "run_step5.psql" -v import_sfm= -v import_sim=
+To trigger additional steps for Duct_Opt or Duct_Seal
+  psql -d "dbname" -U "dbuser" -f "run_step5.psql" -v Duct_Opt=
+  psql -d "dbname" -U "dbuser" -f "run_step5.psql" -v Duct_Seal=
+
+If psql is not recognized, you may need to configure the PATH.
+After installing one of pgAdmin, dbeaver, or postgresql for example:
+  set "path=%PATH%;%LOCALAPPDATA%\Programs\pgAdmin 4\v6\runtime"
+  set "path=%PATH%;C:\Program Files\PostgreSQL\15\bin"
+
+Within an open interactive psql session:
+  \cd 'C:/.../DEER-Prototypes-EnergyPlus/scripts/'
+  \i run_step5.psql
+*/
+
+\set ON_ERROR_STOP on
+
+-- Uncomment these lines to import sfm and sim files respectively.
+\set import_sfm
+\set import_sim
+
+-- Configure the location from which to get CSV files.
+-- Alternatively, specify these on command line.
+-- E.g, psql ... -v file0="./folder/current_msr_mat.csv" -v file1="sim_annual.csv" -v file2="sim_hourly_wb.csv" -v file3="sfm_annual.csv" -v file4="sfm_hourly_wb.csv"
+\if :{?file0}
+  --pass
+\else
+  \set file0 'results/current_msr_mat.csv'
+\endif
+\if :{?file1}
+  --pass
+\else
+  \set file1 'results/sim_annual.csv'
+\endif
+\if :{?file2}
+  --pass
+\else
+  \set file2 'results/sim_hourly_wb.csv'
+\endif
+\if :{?file3}
+  --pass
+\else
+  \set file3 'results/sfm_annual.csv'
+\endif
+\if :{?file4}
+  --pass
+\else
+  \set file4 'results/sfm_hourly_wb.csv'
+\endif
+
+-- Uncomment these lines if need to apply corrections.
+-- Or, use command line arguments: -v Duct_Opt= -v Duct_Seal=
+--\set Duct_Opt
+--\set Duct_Seal
+
+
+CREATE SCHEMA IF NOT EXISTS "MC_results_SWHC049-08";
+SET search_path TO "MC_results_SWHC049-08";
+
+\echo Import current_msr_mat
+\i 'schema/create_current_msr_mat.sql'
+truncate "current_msr_mat";
+
+\copy "current_msr_mat" FROM 'results/DMo_All/current_msr_mat.csv' WITH (FORMAT csv, HEADER MATCH)
+\copy "current_msr_mat" FROM 'results/MFm_Ex/current_msr_mat.csv' WITH (FORMAT csv, HEADER MATCH)
+\copy "current_msr_mat" FROM 'results/MFm_New/current_msr_mat.csv' WITH (FORMAT csv, HEADER MATCH)
+\copy "current_msr_mat" FROM 'results/SFm_Ex/current_msr_mat.csv' WITH (FORMAT csv, HEADER MATCH)
+\copy "current_msr_mat" FROM 'results/SFm_New/current_msr_mat.csv' WITH (FORMAT csv, HEADER MATCH)
+
+\echo Clean up current_msr_mat artifacts created by Python data transformation
+DELETE FROM "current_msr_mat" WHERE "MeasureID" like '%-NC' and "BldgVint" = 'Ex';
+DELETE FROM "current_msr_mat" WHERE "MeasureID" not like '%-NC' and "BldgVint" = 'New';
+
+\echo Import sim_annual
+\i 'schema/create_sim_annual.sql'
+truncate "sim_annual";
+-- If data comes from multiple folders, uncomment lines below.
+\copy "sim_annual" FROM 'results/DMo_All/sim_annual.csv' WITH (FORMAT csv, HEADER MATCH)
+\copy "sim_annual" FROM 'results/MFm_Ex/sim_annual.csv' WITH (FORMAT csv, HEADER MATCH)
+\copy "sim_annual" FROM 'results/MFm_New/sim_annual.csv' WITH (FORMAT csv, HEADER MATCH)
+
+\echo Import sim_hourly_wb
+\i 'schema/create_sim_hourly_wb.sql'
+truncate "sim_hourly_wb";
+-- If data comes from multiple folders, uncomment lines below.
+\copy "sim_hourly_wb" FROM 'results/DMo_All/sim_hourly_wb.csv' WITH (FORMAT csv, HEADER MATCH)
+\copy "sim_hourly_wb" FROM 'results/MFm_Ex/sim_hourly_wb.csv' WITH (FORMAT csv, HEADER MATCH)
+\copy "sim_hourly_wb" FROM 'results/MFm_New/sim_hourly_wb.csv' WITH (FORMAT csv, HEADER MATCH)
+
+\echo Import sfm_annual
+\i 'schema/create_sfm_annual.sql'
+truncate "sfm_annual";
+\copy "sfm_annual" FROM 'results/SFm_Ex/sfm_annual.csv' WITH (FORMAT csv, HEADER MATCH)
+\copy "sfm_annual" FROM 'results/SFm_New/sfm_annual.csv' WITH (FORMAT csv, HEADER MATCH)
+
+\echo Import sfm_hourly_wb
+\i 'schema/create_sfm_hourly_wb.sql'
+truncate "sfm_hourly_wb";
+\copy "sfm_hourly_wb" FROM 'results/SFm_Ex/sfm_hourly_wb.csv' WITH (FORMAT csv, HEADER MATCH)
+\copy "sfm_hourly_wb" FROM 'results/SFm_New/sfm_hourly_wb.csv' WITH (FORMAT csv, HEADER MATCH)
+
+\echo Loading support tables
+\echo FloorArea_2022
+\i 'schema/create_FloorArea_2022.sql'
+truncate "FloorArea_2022";
+\copy "FloorArea_2022" FROM 'energy savings/FloorArea_2022.csv' WITH (FORMAT csv, HEADER MATCH)
+
+\echo NumBldgs
+\i 'schema/create_NumBldgs.sql'
+truncate "NumBldgs";
+\copy "NumBldgs" FROM 'energy savings/NumBldgs.csv' WITH (FORMAT csv, HEADER MATCH)
+
+\echo NumStor
+\i 'schema/create_NumStor.sql'
+truncate "NumStor";
+\copy "NumStor" FROM 'energy savings/NumStor.csv' WITH (FORMAT csv, HEADER MATCH)
+
+\echo ImpactProfiles
+\i 'energy savings/ImpactProfiles.sql'
+
+\echo peakperspec
+\i 'schema/create_peakperspec.sql'
+truncate "peakperspec";
+\copy "peakperspec" FROM 'energy savings/peakperspec.csv' WITH (FORMAT csv, HEADER MATCH)
+
+\echo wts_res_hvac
+\i 'schema/create_wts_res_hvac.sql'
+truncate "wts_res_hvac";
+\copy "wts_res_hvac" FROM 'energy savings/wts_res_hvac.csv' WITH (FORMAT csv, HEADER MATCH)
+
+\echo wts_res_bldg_2022
+\i 'schema/create_wts_res_bldg_2022.sql'
+truncate "wts_res_bldg_2022";
+\copy "wts_res_bldg_2022" FROM 'energy savings/wts_res_bldg.csv' WITH (FORMAT csv, HEADER MATCH)
+
+\echo Run step R1
+\i 'energy savings/R1_Wt_NumStories_Annual.sql'
+\echo Run step R2
+\i 'energy savings/R2_Wt_NumStories_Hourly.sql'
+\echo Run step R3
+\i 'energy savings/R3_Sum_Annual.sql'
+\echo Run step R4
+\i 'energy savings/R4_Sum_Hourly.sql'
+
+\echo Run step P1
+\i 'energy savings/P1-Create-sim_peakper_2022.sql'
+\echo Run step P2
+\i 'energy savings/P2-Calc_MsrImpacts_2022.sql'
+
+-- Only run these for duct optimization.
+\if :{?Duct_Opt}
+  \i 'energy savings/P2.1A-Duct_Opt_correction_factor_2022.sql'
+  \i 'energy savings/P2.1B-Duct_Opt_correction_factor_2022.sql'
+\endif
+
+-- Only run these for duct seal.
+\if :{?Duct_Seal}
+  \i 'energy savings/P2.1A-Duct_Seal_correction_factor_2022.sql'
+  \i 'energy savings/P2.1B-Duct_Seal_correction_factor_2022.sql'
+\endif
+
+\echo Run step P3
+\i 'energy savings/P3-HVAC_wt_Mult.sql'
+\echo Run step P4
+\i 'energy savings/P4-HVAC_wt_sum.sql'
+\echo Run step P5
+\i 'energy savings/P5_VintSub_2022_all.sql'
+\echo Run step P6
+\i 'energy savings/P6_BldgMult_2022.sql'
+\echo Run step P7
+\i 'energy savings/P7_SumBldg_2022.sql'
+
+\echo Run step P8
+\i 'energy savings/P8_RoundDEER_2022.sql'
+
+
+\echo Write table current_msr_mat to CSV file (in the current directory).
+\copy "current_msr_mat" to 'results/combined_current_msr_mat.csv' WITH (FORMAT csv, HEADER)
+
+\echo Write table sim_peakper to CSV file (in the current directory).
+\copy "sim_peakper" to 'results/P1_sim_peakper.csv' WITH (FORMAT csv, HEADER)
+
+\echo Write table meas_impacts_2022 to CSV file (in the current directory).
+\copy "meas_impacts_2022" to 'results/P2_meas_impacts_2022.csv' WITH (FORMAT csv, HEADER)
+
+\echo Write table meas_impacts_wtd_2022 to CSV file (in the current directory).
+\copy "meas_impacts_wtd_2022" to 'results/P7_meas_impacts_wtd_2022.csv' WITH (FORMAT csv, HEADER)
+
+\echo Write table meas_impacts_2022_res to CSV file (in the current directory).
+\copy "meas_impacts_2022_res" to 'results/P8_meas_impacts_2022_res.csv' WITH (FORMAT csv, HEADER)
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/query_swhc049.txt b/residential measures/SWHC049-08 SEER Rated AC HP/query_swhc049.txt
new file mode 100644
index 000000000..6eff252b1
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/query_swhc049.txt	
@@ -0,0 +1,39 @@
+InputVerificationandResultsSummary/Entire Facility/General/Value/Program Version and Build
+InputVerificationandResultsSummary/Entire Facility/General/Value/Weather File
+InputVerificationandResultsSummary/Entire Facility/Zone Summary/Area/Conditioned Total
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Utility Use Per Conditioned Floor Area/Electricity Intensity/HVAC
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Utility Use Per Conditioned Floor Area/Natural Gas Intensity/HVAC
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Heating
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Cooling
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Interior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Exterior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Interior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Exterior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Fans
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Pumps
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Heat Rejection
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Humidification
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Heat Recovery
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Water Systems
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Refrigeration
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Generators
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Total End Uses
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Heating
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Cooling
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Interior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Exterior Lighting
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Interior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Exterior Equipment
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Fans
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Pumps
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Heat Rejection
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Humidification
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Heat Recovery
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Water Systems
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Refrigeration
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Generators
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Total End Uses
+ComponentSizingSummary/Entire Facility/AirLoopHVAC:Unitary:Furnace:HeatCool/Nominal Heating Capacity/*, HeatCapDXGF
+ComponentSizingSummary/Entire Facility/AirLoopHVAC:Unitary:Furnace:HeatCool/Nominal Cooling Capacity/*, CoolCapDXGF
+ComponentSizingSummary/Entire Facility/AirLoopHVAC:UnitarySystem/Design Size Nominal Heating Capacity/*, HeatCapDXHP
+ComponentSizingSummary/Entire Facility/AirLoopHVAC:UnitarySystem/Design Size Nominal Cooling Capacity/*, CoolCapDXHP
diff --git a/residential measures/SWHC049-08 SEER Rated AC HP/rakefile.rb b/residential measures/SWHC049-08 SEER Rated AC HP/rakefile.rb
new file mode 100644
index 000000000..4ed80a718
--- /dev/null
+++ b/residential measures/SWHC049-08 SEER Rated AC HP/rakefile.rb	
@@ -0,0 +1,1010 @@
+# Copyright (c) 2011-2020 Big Ladder Software LLC. All rights reserved.
+# See the file "license.txt" for additional terms and conditions.
+
+if (not defined?(Modelkit))
+  begin
+    require("modelkit")
+  rescue LoadError => exception
+    args = ARGV.join(" ")
+    puts exception
+    puts "\e[1m\e[31mERROR: This rakefile requires the Modelkit library. Make sure that you have the\nModelkit gem installed in your local Rubygems environment, or try running the\nrakefile using your stand-alone installation of Modelkit by typing:\e[0m\n  \e[1mmodelkit rake #{args}\e[0m"
+    exit
+  end
+end
+
+
+require("pathname")
+require("json")
+
+require("modelkit/config")
+require("modelkit/multitable")
+require("modelkit/parametrics")
+require("modelkit/parametrics/worksheet")
+require("modelkit/energyplus")
+
+
+# Add to modelkit-energyplus:
+# other args:
+# - which design days
+# - water mains temp?
+# - daylight saving time?
+def generate_site_pxt(idd, ddy_path, site_path)
+  site_file = File.open(site_path, "w")
+
+  if (File.exists?(ddy_path))
+    input_file = OpenStudio::InputFile.open(idd, ddy_path)
+  else
+    raise("file not found: #{ddy_path.inspect}")
+  end
+
+  site_locations = input_file.find_objects_by_class_name("Site:Location").to_a
+
+  if (site_locations.empty?)
+    raise("could not find Site:Location object in #{ddy_path.inspect}")
+  else
+    site_file.puts(site_locations.first.to_idf)
+  end
+
+  all_design_days = input_file.find_objects_by_class_name("SizingPeriod:DesignDay").to_a
+  selected_design_days = all_design_days.select { |dd| dd.name[/Ann Htg 99.6% Condns DB|Ann Clg 0?.4% Condns DB/i] }
+
+  if (selected_design_days.length < 2)
+    puts "warning: could not find requested design days; including all design days\n"
+    selected_design_days = all_design_days
+  end
+
+  # Write design days to site file.
+  selected_design_days.each { |dd| site_file.puts(dd.to_idf) }
+
+
+# 'CorrelationFromWeatherFile' is available starting in EP 9.0.
+
+# Does this work for design-day only runs?
+# Seems to work for annual.
+  site_file.puts("\n\nSite:WaterMainsTemperature,\n  CorrelationFromWeatherFile;\n")
+
+  daylight_saving_time = input_file.find_objects_by_class_name("RunPeriodControl:DaylightSavingTime").to_a
+  if (not daylight_saving_time.empty?)
+    site_file.puts
+    site_file.puts(daylight_saving_time.first.to_idf)
+  end
+
+  site_file.close
+end
+
+
+# NOPUB Should move into Modelkit somewhere.
+# Support for running simulations in parallel.
+require("open3")
+require("set")
+
+$child_pids = Set.new  # Global tracking of child PIDs
+
+# Return PID?
+def run_process(command, dir)
+  # NOTE: Separate processes are required to make the EnergyPlus runs thread safe!
+  Open3.popen3(command, :chdir => dir) do |stdin, stdout, stderr, thread|
+    $child_pids.add(thread.pid)
+    # This might work with just an instance variable or similar.
+
+    stdin.close  # All input already sent with command
+
+    file_out = File.open("#{dir}/stdout", "w")
+    file_err = File.open("#{dir}/stderr", "w")
+
+    while (line = stdout.gets)
+      file_out.puts(line)
+      #@proc_out.call(line) if (@proc_out)
+    end
+
+    # This is probably not right.
+    while (line = stderr.gets)
+      file_err.puts(line)
+      #@proc_err.call(line) if (@proc_err)
+    end
+
+    stdout.close
+    stderr.close
+
+    file_out.close
+    file_err.close
+
+    #print "Completed: #{File.basename(dir)}\n"
+    $child_pids.delete(thread.pid)
+  end
+end
+
+# Search up through parent directories for one or more possible file names.
+def search_parent_dirs(start_dir, *file_names)
+  path = nil
+  dir_names = start_dir.to_s.split("/")
+  while (not dir_names.empty?) do
+    file_names.each do |file_name|
+      test_path = "#{dir_names.join("/")}/#{file_name}"
+      if (File.exist?(test_path))
+        path = test_path
+        break
+      end
+    end
+    break if (path)
+    dir_names.pop
+  end
+  return(path)
+end
+
+# Search for a file name or partial path in an array of provided directories.
+# Directories are expected to already be absolute paths.
+def resolve_path(path, dirs)
+  resolved_path = nil
+  dirs.each do |dir|
+    expanded_path = File.expand_path(path, dir)
+    if (File.exist?(expanded_path))
+      resolved_path = expanded_path
+      break
+    end
+  end
+  return(resolved_path)
+end
+
+# Clean up any previous output files left behind if 'compose' or 'run' fails.
+# Leftover files can be processed unintentionally by downstream tasks and
+# ultimately generate false results.
+def clean_energyplus_output_files(dir)
+  # Not all of these files might be present. Others might be present and unhandled.
+  paths = [
+    "#{dir}/instance-out.err",
+    "#{dir}/instance-out.rdd",
+    "#{dir}/instance-out.sql",  # Most important for downstream tasks
+    "#{dir}/instance-tbl.htm",
+    "#{dir}/instance-var.csv",
+    "#{dir}/stderr",
+    "#{dir}/stdout"
+  ]
+  FileUtils.rm_f(paths)
+end
+
+
+# Rake stubbornly sets the working directory to wherever the Rakefile is located.
+# The target directory could optionally be set from a CLI option instead.
+study_dir = Rake.application.original_dir
+
+climates_csv_path = "#{study_dir}/climates.csv"
+cohorts_csv_path = "#{study_dir}/cohorts.csv"
+
+query_path = "#{study_dir}/query.txt"
+results_summary_path = "#{study_dir}/results-summary.csv"
+results_profile_elec_path = "#{study_dir}/results-profile-elec.csv"
+results_profile_gas_path = "#{study_dir}/results-profile-gas.csv"
+results_paths = [results_summary_path, results_profile_elec_path, results_profile_gas_path]
+
+cases_dir = "#{study_dir}/cases"
+runs_dir = "#{study_dir}/runs"
+runs_pathname = Pathname.new(runs_dir)
+
+MUTEX = Mutex.new  # Thread lock for when something needs to run in a single thread
+
+config_path = search_parent_dirs(study_dir, ".modelkit-config")
+if (not config_path)
+  raise("modelkit-config file not found in working directory or any parent directory")
+else
+  CONFIG = Modelkit::Config.new(config_path)
+  puts "Using modelkit-config at #{config_path}\n"
+end
+
+config = Hash.new
+[:prototypes_dir, :templates_dir, :weather_dir, :codes_dir].each do |key|
+  config[key] = []
+  field = key.to_s.gsub(/_/, "-")
+  if (not CONFIG[field])
+    raise("#{field} variable missing in modelkit-config")
+  else
+    config_paths = CONFIG[field].split(/\s*;\s*/)  # Split string with semicolons into array of paths
+    config_paths.each do |path|
+      # Resolve path relative to modelkit-config file and normalize the slashes.
+      config[key] << File.expand_path(path.strip.gsub(/\\/, "/"), File.dirname(config_path))
+    end
+  end
+end
+
+max_workers = CONFIG["max-workers"]
+if (max_workers.nil?)
+  max_workers = 1
+end
+
+global_pxv_path = search_parent_dirs(study_dir, "global.pxv")
+if (global_pxv_path)
+  puts "Using global.pxv at #{global_pxv_path}\n"
+end
+
+rake_tasks = Rake.application.top_level_tasks
+rake_task_name = rake_tasks.first  # Multiple tasks are allowed, but assume one
+
+rake_options = Rake.application.options
+rake_options.always_multitask = true  # --multitask, -m
+#rake_options.job_stats = true  # --job-stats   true | :history
+rake_options.thread_pool_size = max_workers - 1  # --jobs, -j (default 12 on Mac)
+
+if (rake_options.dryrun or rake_options.show_all_tasks or
+  rake_options.show_prereqs or rake_options.show_tasks)
+  # These are information-only requests. Rake nonetheless registers as invoked
+  # with the "default" task name but nothing actually gets run.
+  rake_task_name = "none"
+end
+
+
+# Show threads message and info about how to change
+#   Running with 8 threads (edit .modelkit-config to change).
+#   Type Ctrl+C to cancel all tasks.
+
+require "io/console"  # need this anyway for progress bar
+
+#$stdin.echo = false  # turn off echo; in Mac shows a cursor with a key icon
+# This prevents the user from over-typing the output stream.
+
+# also try switching to raw mode--should also block user input; maybe no key icon?
+
+#print "\e[?25l"  # hide the cursor; MUST remember to show it again on exit or else it's permanent for the session!
+
+
+trap("INT") do  # Ctrl+C (polite kill)
+  puts "Canceling all tasks.\n"
+  if ($child_pids)
+    $child_pids.each { |pid| Process.kill("KILL", pid) }
+  end
+  exit
+end
+
+if (Modelkit::Platform.unix?)
+  trap("TSTP") do  # Ctrl+Z (suspend)
+    puts "Suspending all tasks. Type 'fg' to resume.\n"
+    exit
+  end
+end
+
+if (not rake_task_name =~ /^(prune|clean|none)$/)
+
+# If possible, detect if any tasks will be run before showing this message:
+puts "\e[1mType Ctrl+C to cancel all tasks.\e[0m\n"
+
+end
+
+# modelkit rake -A crashes for some reason
+
+# NOPUB Some of above could be included here too.
+# Don't evaluate worksheets and generate file tasks if not necessary!
+# NOTE: prune does need to evaluate worksheets.
+if (not rake_task_name =~ /^(clean|none)$/)
+
+  pxv_paths = []
+  site_paths = []
+
+  compose_idf_paths = []
+
+
+  #rename size_ to sizerun_  size_run_  sizing_run_
+  size_ref_paths = []
+  size_idf_paths = []
+  size_sql_paths = []
+  size_json_paths = []
+
+  hardsize_idf_paths = []
+
+  run_sql_paths = []
+  run_csv_paths = []
+
+
+
+  old_site_paths = Dir.glob("#{runs_dir}/*/site.pxt")
+  old_pxv_paths = Dir.glob("#{runs_dir}/**/instance.pxv")
+
+  climates = Modelkit::Worksheet.open(climates_csv_path)
+  cohorts = Modelkit::Worksheet.open(cohorts_csv_path)
+
+  climate_pattern = ENV["CLIMATE"] || ""
+
+  new_case_csv = "skip,case_name\n,defaults\n"  # Could be read from config instead
+
+  cohorts_first_pass = true
+
+  puts "Evaluating worksheets...\n"
+
+  csv_table = climates.each_row do |row1, index1, variables1, parameters1|
+    #puts "climate_index = #{index1}"
+
+    if (not variables1.key?(:climate))
+      raise("required column \"climate\" is missing in #{File.basename(climates_csv_path)}")
+    end
+
+    climate_name = variables1[:climate].to_s.strip  # Could have been converted to non-string by Util.value_from_string
+    if (climate_name.empty?)
+      raise("climate field cannot be blank for row #{index1 + 2} of #{File.basename(climates_csv_path)}")
+    end
+
+    next if (not climate_name =~ Regexp.new(climate_pattern))
+
+    FileUtils.mkdir_p("#{runs_dir}/#{climate_name}")
+
+    site_path = "#{runs_dir}/#{climate_name}/site.pxt"
+    if (site_paths.include?(site_path))
+      puts "warning: duplicate name #{climate_name.inspect} in climate column at row #{index1 + 2} in #{File.basename(climates_csv_path)}; row will be skipped"
+      next
+    end
+
+    site_paths << site_path
+
+    if (not variables1.key?(:weather_file))
+      raise("required column \"weather_file\" is missing in #{File.basename(climates_csv_path)}")
+    end
+
+    weather_name = variables1[:weather_file].to_s.strip  # Could have been converted to non-string by Util.value_from_string
+    if (weather_name.empty?)
+      raise("weather_file field cannot be blank for row #{index1 + 2} of #{File.basename(climates_csv_path)}")
+    end
+
+    epw_path = resolve_path(weather_name, config[:weather_dir])
+    if (not epw_path)
+      puts "Could not resolve path #{weather_name.inspect} from possible paths:\n"
+      config[:weather_dir].each { |dir| puts "  #{File.expand_path(weather_name, dir).inspect}\n" }
+      puts "Check the weather-dir variable in modelkit-config file.\n"
+      raise("weather file #{weather_name.inspect} not found for row #{index1 + 2} of #{File.basename(climates_csv_path)}")
+    end
+
+    if (not File.file?(epw_path))
+      raise("weather file #{epw_path.inspect} is not a file for row #{index1 + 2} of #{File.basename(climates_csv_path)}")
+    end
+
+    ddy_path = "#{File.dirname(epw_path)}/#{File.basename(epw_path, ".*")}.ddy"  # Ensure ddy is from same directory as resolved epw file
+    if (not File.exist?(ddy_path))
+      puts "Weather file path resolved to #{epw_path.inspect}\n"
+      raise("ddy file #{ddy_path.inspect} not found for row #{index1 + 2} of #{File.basename(climates_csv_path)}")
+    end
+
+    # This file only exists to indicate if the weather file changes for dependency purposes.
+    # The weather file is the one input that is separate from instance parameters.
+    weather_path = "#{runs_dir}/#{climate_name}/weather"
+    pathname = Pathname.new(weather_path).relative_path_from(runs_pathname)
+
+    if (File.exist?(weather_path))
+      old_epw_path = File.read(weather_path)
+      if (epw_path != old_epw_path)
+        puts "Updating: #{pathname}\n"
+        File.write(weather_path, epw_path)
+      end
+    else
+      puts "Writing: #{pathname}\n"
+      File.write(weather_path, epw_path)
+    end
+
+    # generate site.pxt from .ddy file
+    file site_path => [weather_path, ddy_path] do
+      idd = open_data_dictionary
+      pathname = Pathname.new(site_path).relative_path_from(runs_pathname)
+      puts "Generating: #{pathname}\n"
+      generate_site_pxt(idd, ddy_path, site_path)
+    end
+
+    if (variables1.key?(:codes_file))  # NOTE: codes_file is an optional column
+      codes_name = variables1[:codes_file].to_s.strip  # Could have been converted to non-string by Util.value_from_string
+      if (codes_name.empty?)
+        raise("codes_file field cannot be blank for row #{index1 + 2} of #{File.basename(climates_csv_path)}")
+      end
+
+      codes_path = resolve_path(codes_name, config[:codes_dir])
+      if (not codes_path)
+        puts "Could not resolve path #{codes_name.inspect} from possible paths:\n"
+        config[:codes_dir].each { |dir| puts "  #{File.expand_path(codes_name, dir).inspect}\n" }
+        puts "Check the codes-dir variable in modelkit-config file.\n"
+        raise("codes file #{codes_name.inspect} not found for row #{index1 + 2} of #{File.basename(climates_csv_path)}")
+      end
+
+      if (not File.file?(codes_path))
+        raise("codes file #{codes_path.inspect} is not a file for row #{index1 + 2} of #{File.basename(climates_csv_path)}")
+      end
+
+      codes_table = Modelkit::MultiTable.new(codes_path)
+
+    else
+      codes_table = nil  # Must set something to pass to next worksheet
+    end
+
+    variables1[:codes] = codes_table  # For backwards compatibility
+
+  # Make sure objects passed in are not mutated by the Worksheet. Make dupes?
+
+    cohort_names = []  # Accumulate names to check for duplicates
+
+    cohorts.each_row(variables1) do |_, index2, variables2, parameters2|
+      #puts "  cohort_index = #{index2}"
+
+      # NOTE: Variables from outer worksheet (variables1) are copied into this worksheet.
+      # Changes to the variables here (variables2) do not propagate back up.
+
+      if (not variables2.key?(:cohort))
+        raise("required column \"cohort\" is missing in #{File.basename(cohorts_csv_path)}")
+      end
+
+      cohort_name = variables2[:cohort].to_s.strip  # Could have been converted to non-string by Util.value_from_string
+      if (cohort_name.empty?)
+        raise("cohort field cannot be blank for row #{index2 + 2} of #{File.basename(cohorts_csv_path)}")
+      end
+
+      if (cohort_names.include?(cohort_name))
+        if (cohorts_first_pass)  # Only warn about this row once
+          puts "warning: duplicate name #{cohort_name.inspect} in cohort column at row #{index2 + 2} in #{File.basename(cohorts_csv_path)}; row will be skipped"
+        end
+        next
+      end
+
+      cohort_names << cohort_name
+
+      cases_csv_path = "#{cases_dir}/#{cohort_name}.csv"
+      cases_csv_short_path = "#{File.basename(cases_dir)}/#{cohort_name}.csv"
+      if (not File.exist?(cases_csv_path))
+        puts("Cases worksheet #{cases_csv_short_path.inspect} not found for row #{index2 + 2} of #{File.basename(cohorts_csv_path)}\n")
+        puts("Creating: #{cases_csv_short_path}\n")
+        FileUtils.mkdir_p(cases_dir)
+        File.write(cases_csv_path, new_case_csv)
+      end
+
+      if (not variables2.key?(:root))
+        raise("required column \"root\" is missing in #{File.basename(cohorts_csv_path)}")
+      end
+
+      root_name = variables2[:root].to_s.strip  # Could have been converted to non-string by Util.value_from_string
+      if (root_name.empty?)
+        raise("root field cannot be blank for row #{index2 + 2} of #{File.basename(cohorts_csv_path)}")
+      end
+
+      root_path = resolve_path(root_name, config[:prototypes_dir])
+      if (not root_path)
+        puts "Could not resolve path #{root_name.inspect} from possible paths:\n"
+        config[:prototypes_dir].each { |dir| puts "  #{File.expand_path(root_name, dir).inspect}\n" }
+        puts "Check the prototypes-dir variable in modelkit-config file.\n"
+        raise("root template #{root_name.inspect} not found for row #{index2 + 2} of #{File.basename(cohorts_csv_path)}")
+      end
+
+      if (not File.file?(root_path))
+        raise("root template #{root_path.inspect} is not a file for row #{index2 + 2} of #{File.basename(cohorts_csv_path)}")
+      end
+
+      case_names = []  # Accumulate names to check for duplicates
+
+      # Better to pre-read and cache this outside the looping?
+      # There are only N worksheets...1 per building type.
+      cases = Modelkit::Worksheet.open(cases_csv_path)
+
+      cases.each_row(variables2) do |_, index3, variables3, parameters3|
+        #puts "    case_index = #{index3}"
+
+        # NOTE: Variables from outer worksheet (variables2) are copied into this worksheet.
+        # Changes to the variables here (variables3) do not propagate back up.
+
+        if (not variables3.key?(:case_name))
+          raise("required column \"case_name\" is missing in #{File.basename(cases_dir)}/#{File.basename(cases_csv_path)}")
+        end
+
+        case_name = variables3[:case_name].to_s.strip  # Could have been converted to non-string by Util.value_from_string
+        if (case_name.empty?)
+          raise("case_name field cannot be blank for row #{index3 + 2} of #{File.basename(cases_dir)}/#{File.basename(cases_csv_path)}")
+        end
+
+        if (case_names.include?(case_name))
+          if (cohorts_first_pass)  # Only warn about this row once
+            puts "warning: duplicate name #{case_name.inspect} in case_name column at row #{index3 + 2} in #{File.basename(cases_dir)}/#{File.basename(cases_csv_path)}; row will be skipped"
+          end
+          next
+        end
+
+        case_names << case_name
+        run_name = "#{climate_name}/#{cohort_name}/#{case_name}"
+        case_dir = "#{runs_dir}/#{run_name}"
+        FileUtils.mkdir_p(case_dir)
+
+  # better to create this dynamically in cases.csv by combining variables from other layers.
+  # all variables need to be propagated first from layer to layer.
+  # for example:
+  #   :run_name
+  #   %= "My Prefix Something: #{climate}/#{cohort}/#{case_name}"
+        pxv_string = ":run_name => #{run_name.inspect},\n"
+
+        parameters = parameters1 | parameters2 | parameters3
+        parameters.each do |key, value|
+          value_inspect = value.inspect
+          if (value.kind_of?(String))
+            # Using `inspect` on strings is useful because it reveals invisible
+            # characters and invalid byte sequences. The downside is that the
+            # string must be unescaped.
+            value_inspect.gsub!(/\\\\/, "\\")
+          end
+          pxv_string << ":#{key} => #{value_inspect},\n"
+        end
+
+        pxv_path = "#{case_dir}/instance.pxv"
+        pxv_paths << pxv_path
+
+        pathname = Pathname.new(pxv_path).relative_path_from(runs_pathname)
+
+        if (File.exist?(pxv_path))
+          old_pxv_string = File.read(pxv_path)
+          if (pxv_string != old_pxv_string)
+            puts "Updating: #{pathname}\n"
+            File.write(pxv_path, pxv_string)
+          end
+        else
+          puts "Writing: #{pathname}\n"
+          File.write(pxv_path, pxv_string)
+        end
+
+        compose_idf_path = "#{case_dir}/instance.idf"
+        compose_idf_paths << compose_idf_path
+
+        if (variables3[:sizing_case])
+          size_name = "#{climate_name}/#{cohort_name}/#{variables3[:sizing_case]}"
+          size_dir = "#{runs_dir}/#{size_name}"
+          size_ref_path = "#{size_dir}/instance.idf"
+
+          size_idf_path = "#{size_dir}/instance-size.idf"
+          size_sql_path = "#{size_dir}/instance-size-out.sql"
+          size_json_path = "#{size_dir}/instance-size-out.json"
+
+          if (size_ref_paths.include?(size_ref_path))
+            # Avoid creating redundant tasks when multiple cases reference same sizing case.
+            create_sizing_tasks = false
+          else
+            create_sizing_tasks = true
+
+            size_ref_paths << size_ref_path
+            size_idf_paths << size_idf_path
+            size_sql_paths << size_sql_path
+            size_json_paths << size_json_path
+          end
+
+          hardsize_idf_path = "#{case_dir}/instance-hardsize.idf"
+          hardsize_idf_paths << hardsize_idf_path
+
+          run_idf_path = hardsize_idf_path  # Which input file to run
+
+          run_sql_path = "#{case_dir}/instance-hardsize-out.sql"
+          run_sql_paths << run_sql_path
+
+          run_csv_path = "#{case_dir}/instance-hardsize-var.csv"
+          run_csv_paths << run_csv_path
+
+        else
+          # Autosize-only run.
+          size_ref_path = nil
+
+          run_idf_path = compose_idf_path  # Which input file to run
+
+          run_sql_path = "#{case_dir}/instance-out.sql"
+          run_sql_paths << run_sql_path
+
+          run_csv_path = "#{case_dir}/instance-var.csv"
+          run_csv_paths << run_csv_path
+        end
+
+        # Compose input file from parameter file.
+        #   need more dependencies here: template files
+        file compose_idf_path => [site_path, root_path, pxv_path, global_pxv_path].compact do  # If no path for global.pxv, remove nil element
+          pathname = Pathname.new(compose_idf_path).relative_path_from(runs_pathname)
+          puts "Composing: #{pathname}\n"
+
+          clean_energyplus_output_files(File.dirname(compose_idf_path))
+
+          site_dir = File.dirname(site_path)
+
+          begin
+            Modelkit::Parametrics.template_compose(root_path,
+              :annotate => CONFIG["template-compose.annotate"],
+              :indent => CONFIG["template-compose.indent"],
+              :esc_line => CONFIG["template-compose.esc-line"],
+              :dirs => [site_dir, *config[:templates_dir]],
+              :files => [global_pxv_path, pxv_path].compact,  # If no path for global.pxv, remove nil element
+              :output => compose_idf_path)
+          rescue Exception => exception
+            puts "#{exception.class.name}: #{exception.message}\n"
+            puts "#{exception.backtrace.first}\n" if (not SyntaxError === exception)
+            puts "Skipping: #{pathname}\n"
+          end
+        end
+
+        if (create_sizing_tasks)
+          # Generate a modified input file in order to run a design-day-only simulation.
+          # NOTE: This can be eliminated if a design-day option is added to energyplus-run.
+          file size_idf_path => size_ref_path do
+            pathname = Pathname.new(size_idf_path).relative_path_from(runs_pathname)
+            puts "Generating size run: #{pathname}\n"
+
+            FileUtils.cp(size_ref_path, size_idf_path)
+
+            idd = open_data_dictionary
+            input_file = OpenStudio::InputFile.open(idd, size_idf_path)
+
+            sc_objs = input_file.find_objects_by_class_name("SimulationControl")
+            if sc_objs.length != 1
+              raise "More than one SimulationControl object found"
+            end
+            sc = sc_objs[0]
+            sc.fields[1] = "Yes"
+            sc.fields[2] = "Yes"
+            sc.fields[3] = "Yes"
+            sc.fields[4] = "Yes"
+            sc.fields[5] = "No"
+
+            input_file.write(size_idf_path)
+          end
+
+          # Run sizing input files for design days only.
+          file size_sql_path => size_idf_path do
+            pathname = Pathname.new(size_idf_path).relative_path_from(runs_pathname)
+            puts "Running size run: #{pathname}\n"
+
+            command = "modelkit-energyplus energyplus-run --weather=\"#{epw_path}\" \"#{size_idf_path}\""
+            run_process(command, size_dir)
+          end
+
+          # Generate size data file (instance-size-out.json).
+          file size_json_path => size_sql_path do
+            pathname = Pathname.new(size_json_path).relative_path_from(runs_pathname)
+            puts "Extracting size data: #{pathname}\n"
+
+            # Make a copy to work on because the original gets overwritten by EnergyPlus.size.
+            temp_path = "#{File.dirname(size_idf_path)}/instance-temp.idf"
+            FileUtils.cp(size_idf_path, temp_path)
+
+            idd = open_data_dictionary
+            input_file = OpenStudio::InputFile.open(idd, temp_path)
+            sql = Modelkit::EnergyPlus::SQLOutput.new(size_sql_path)
+
+            # This will be fixed to only generate JSON and not modify the input file.
+            _, count, output_file = Modelkit::EnergyPlus.size(
+              sql, input_file, {json: size_json_path, version: "9-2"})
+            #puts("#{count} modifications made")
+
+            FileUtils.rm_f(temp_path)
+          end
+        end
+
+        if (hardsize_idf_path)
+          # This would be a reasonable place to use `multitask` because compose_idf_path and
+          # size_json_path are independent and can be run concurrently. However, it seems
+          # like `multitask` doesn't compare timestamps like `file` does. Instead it
+          # always runs like a regular `task`.
+          file hardsize_idf_path => [compose_idf_path, size_json_path] do
+            pathname = Pathname.new(hardsize_idf_path).relative_path_from(runs_pathname)
+            puts "Applying hard sizes: #{pathname}\n"
+
+            idd = open_data_dictionary
+            input_file = OpenStudio::InputFile.open(idd, compose_idf_path)
+
+            json_string = File.read(size_json_path)
+            value_map = JSON.parse(json_string, {:symbolize_names=>true})
+            output_file, count = Modelkit::EnergyPlus.modify_objects(input_file, value_map)
+            #puts("#{count} modifications made")
+
+            # Set SimulationControl fields 1, 2, and 3 to "No", "No", and "No"
+            sc_objs = output_file.find_objects_by_class_name("SimulationControl")
+            if sc_objs.length != 1
+              raise "More than one SimulationControl object found"
+            end
+            sc = sc_objs[0]
+            sc.fields[1] = "No"
+            sc.fields[2] = "No"
+            sc.fields[3] = "No"
+            # Remove Sizing:Zone and Sizing:System objects
+            sizing_zones = output_file.find_objects_by_class_name("Sizing:Zone")
+            sizing_systems = output_file.find_objects_by_class_name("Sizing:System")
+            sizing_plants = output_file.find_objects_by_class_name("Sizing:Plant")
+            (sizing_zones + sizing_systems + sizing_plants).each {|x| output_file.delete_object(x)}
+
+            output_file.write(hardsize_idf_path)
+          end
+        end
+
+        # Run input file in a separate process.
+        file run_sql_path => [epw_path, run_idf_path] do
+          # Not sure why this check is needed; seems to try to run if even IDF does not exist.
+          # May need in other places, like sizing run.
+          next if (not File.exist?(run_idf_path))
+
+          pathname = Pathname.new(run_idf_path).relative_path_from(runs_pathname)
+          puts "Running: #{pathname}\n"
+
+          # Because this is spawned to the shell, .modelkit-config options will be
+          # automatically applied.
+          # NOTE: If modelkit-energyplus was thread safe, would not have to run this
+          #   as a separate process.
+          command = "modelkit-energyplus energyplus-run --weather=\"#{epw_path}\" \"#{run_idf_path}\""
+          run_process(command, case_dir)
+          #$bar.inc
+        end
+
+
+  # see discussion with Michael
+        file run_csv_path => run_sql_path
+
+  # deleting instance-out.csv breaks it; doesn't know how to recover
+
+      end
+    end
+
+    cohorts_first_pass = false
+  end
+
+
+  prune_paths = (old_site_paths - site_paths) + (old_pxv_paths - pxv_paths)
+
+  if (not prune_paths.empty? and not rake_task_name =~ /^(prune|clean|none)$/)
+    puts "\e[1m\e[33mNote: There are cases in the runs directory that are not referenced by any\n" \
+      "worksheet. You may want to delete them by typing:\e[0m\n  \e[1mmodelkit rake prune\e[0m\n"
+  end
+
+end
+
+
+desc "Generate case files"
+task :cases do
+  # No operation; cases are generated when worksheets are evaluated.
+end
+
+
+desc "Delete unreferenced files"
+task :prune do
+  if (prune_paths.empty?)
+    puts "Prune has nothing to delete."
+  else
+    prune_dirs = []
+    puts "\e[1m\e[31mPrune will delete the following files:\e[0m"
+
+# show each as:  dirname/* (106 files)
+
+    prune_paths.each do |path|
+      dir = File.dirname(path)
+      prune_dirs << dir
+      pathname = Pathname.new(dir).relative_path_from(runs_pathname)
+      puts "  \e[31m#{pathname}\e[0m"
+    end
+    print "\e[1m\e[31mConfirm (y/n)?\e[0m "
+
+    input = ENV["CONFIRM"] || $stdin.gets || ""
+    if (ENV["CONFIRM"] or not $stdin.tty?)
+      puts input  # Echo when not already written to STDOUT
+    end
+
+    $start_time = Time.now  # Reset to cut out wait time on the user prompt
+    if (input.strip =~ /^y/i)
+      puts "Pruning files..."
+
+      # maybe don't have to repeat this--already said what was to be deleted
+      prune_dirs.each do |dir|
+        pathname = Pathname.new(dir).relative_path_from(runs_pathname)
+        puts "Deleting: #{pathname}"
+        FileUtils.rm_rf(dir)
+      end
+    else
+      puts "Task canceled."
+    end
+  end
+end
+
+
+desc "Delete all files and results"
+task :clean do
+  paths = []; names = []
+  [runs_dir, *results_paths].each do |path|
+    if (File.directory?(path))
+      count = Dir.glob("#{path}/**/*").count { |f| File.file?(f) }
+      if (count.nonzero?)
+        paths << Dir.glob("#{path}/*")
+        names << "#{File.basename(path)}/* (#{count} files)"
+      end
+    elsif (File.file?(path))
+      paths << path
+      names << File.basename(path)
+    end
+  end
+
+  if (paths.empty?)
+    puts "Clean has nothing to delete."
+  else
+    puts "\e[1m\e[31mClean will delete the following files:\e[0m"
+    names.each { |name| puts "  \e[31m#{name}\e[0m"}
+    print "\e[1m\e[31mConfirm (y/n)?\e[0m "
+
+    input = ENV["CONFIRM"] || $stdin.gets || ""
+    if (ENV["CONFIRM"] or not $stdin.tty?)
+      puts input  # Echo when not already written to STDOUT
+    end
+
+    $start_time = Time.now  # Reset to cut out wait time on the user prompt
+    if (input.strip =~ /^y/i)
+      puts "Cleaning files..."
+      FileUtils.rm_rf(paths)
+    else
+      puts "Task canceled."
+    end
+  end
+end
+
+
+desc "Generate site files (site.pxt)"
+multitask :sites => site_paths
+
+
+desc "Compose input files"
+multitask :compose => compose_idf_paths
+
+
+# Generate size input files
+multitask :"size-idf" => size_idf_paths
+
+
+# Run size input files
+multitask :"size-sql" => size_sql_paths
+
+
+# Extract size data
+multitask :"size-json" => size_json_paths
+
+
+desc "Apply hard sizes to input files"
+multitask :hardsize => hardsize_idf_paths
+
+
+desc "Run input files"
+multitask :run => run_sql_paths
+
+
+file query_path do
+  puts "Query file not found.\n"
+  query =
+"AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Energy Per Total Building Area/Net Site Energy, Net Site EUI
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/Site and Source Energy/Total Energy/Net Site Energy, Net Site Energy
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Electricity/Total End Uses, Electricity
+AnnualBuildingUtilityPerformanceSummary/Entire Facility/End Uses/Natural Gas/Total End Uses, Natural Gas\n"
+  File.write(query_path, query)
+  puts "Writing default query file: #{query_path}\n"
+end
+
+
+file results_summary_path => [*run_sql_paths, query_path] do
+  pathname = Pathname.new(results_summary_path).relative_path_from(Pathname.new(study_dir))
+  puts "Processing: #{pathname}\n"
+
+  short_paths = run_sql_paths.map { |path| Pathname.new(path).relative_path_from(runs_pathname) }
+  Modelkit::EnergyPlus.sql(short_paths, query_path, :dir => runs_dir, :output => results_summary_path)
+end
+
+
+file results_profile_elec_path => run_csv_paths do
+  aggregate_profiles("Electricity:Facility", results_profile_elec_path, run_csv_paths, runs_pathname, study_dir)
+end
+
+
+file results_profile_gas_path => run_csv_paths do
+  aggregate_profiles("Gas:Facility", results_profile_gas_path, run_csv_paths, runs_pathname, study_dir)
+end
+
+
+desc "Aggregate the simulation results"
+task :results => results_paths
+
+
+task :default => :results
+
+
+def aggregate_profiles(column_name, output_path, run_csv_paths, runs_pathname, study_dir)
+  pathname = Pathname.new(output_path).relative_path_from(Pathname.new(study_dir))
+  puts "Processing: #{pathname}\n"
+
+  short_paths = run_csv_paths.map { |path| Pathname.new(path).relative_path_from(runs_pathname) }
+
+  columns = []
+  column_header = nil
+  date_time = true
+  short_paths.each do |short_path|
+    csv_path = "#{runs_pathname}/#{short_path}"
+    if (File.exist?(csv_path))
+      csv = CSV.read(csv_path, :headers=>true)
+      if (date_time)
+        column = csv["Date/Time"]
+        column.unshift("Date/Time")  # Add header
+        columns << column
+        date_time = false
+      end
+      if (not column_header)
+        # Match column name to the header while ignoring units/interval, i.e., [J](Hourly).
+        column_header = csv.headers.find { |header| header.match(column_name) }
+      end
+      column = csv[column_header]
+      column.unshift(short_path)  # Add header
+      columns << column
+    else
+      puts "warning: file not found: #{csv_path}\n"
+    end
+  end
+
+  File.open(output_path, "w") do |file|
+    columns.transpose.each { |row| file.puts(row.join(",")) }
+  end
+end
+
+
+# NOPUB consider building this into modelkit-energyplus.
+#   basically caches IDD path and avoids concurrent openings.
+
+# Open the EnergyPlus IDD if needed, but only do it once.
+def open_data_dictionary
+  MUTEX.synchronize do  # Lock to prevent opening multiple times concurrently
+    if (@idd.nil?)
+      puts "Opening Energy+.idd...\n"
+
+      if (path = CONFIG["energyplus-run.engine"])
+        path = File.expand_path(path.gsub(/\\/, "/"))  # Resolve path and normalize
+        if (File.exist?(path))
+          idd_path = "#{path}/Energy+.idd"
+          if (not File.exist?(idd_path))
+            raise("Energy+.idd not found in specified EnergyPlus directory: #{path}")
+          end
+        else
+          raise("EnergyPlus directory not found: #{path}")
+        end
+      else
+        raise("energyplus-run.engine field missing in .modelkit-config")
+      end
+
+      @idd = OpenStudio::DataDictionary.open(idd_path)
+    end
+  end
+  return(@idd)
+end
+
+
+# NICE, works
+#require "rake/cpu_counter"
+#puts "cpu=#{Rake::CpuCounter.count}"
+
+# Almost works but not quite:
+
+# shows what command was invoked from CLI
+# if blank (even with -T), it's "default".
+#puts "top level:"
+# cli_tasks = Rake.application.top_level_tasks
+# cli_task_name = cli_tasks.first  # could be more than one; just grab first for now
+# puts "cli_task_name=#{cli_task_name}"  # returns String
+
+# cli_task = Rake.application.tasks.find { |t| t.name == cli_task_name }  # returns Rake::Task
+# puts "cli_task=#{cli_task}"
+#
+
+#
+## Rake has this builtin:
+#  Rake.application.lookup(task_name) => task
+
+# work_to_do = false
+# if (cli_task.needed?)
+#   # Just because needed doesn't mean there is any work to do--check prereqs!
+#   cli_task.prerequisite_tasks.each do |prereq|
+#     if (prereq.needed?)
+#       work_to_do = true
+#       break
+#     end
+#   end
+# end
+#
+# if (work_to_do)
+#   puts "Work to do!"
+# else
+#   puts "Up to date; nothing to do."
+# end
+
+
+$start_time = Time.now
+#$bar = RakeProgressbar.new(run_sql_paths.length)
+
+at_exit do
+  #$bar.finished
+  if (not rake_task_name =~ /^none$/)
+    puts "Elapsed task duration: #{Time.now - $start_time} sec"
+  end
+end