Add handling of limited linked seg data prep

To limit data output, technical potential contributing mseg data preparation is restricted to only those data needed for competition in run.py when the user is executing a max adoption potential scenario only. This caused a problem with the new code to link costs across multiple segments in a measure, as the code was trying to pull from linked data fields that did not exist/were restricted. This issue has been fixed.

scout/ecm_prep.py

@@ -6504,6 +6504,18 @@ def fill_mkts(self, msegs, msegs_cpl, convert_data, tsv_data_init, opts,
                             # Shorthand for mseg data to add costs to
                             add_to_dict = self.markets[adopt_scheme]["mseg_adjust"][
                                 "contributing mseg keys and values"][add_to_mseg]
+                            # If full detailed data were not prepared for current scenario (see var
+                            # 'add_dict_limited' below, account for this by only linking to the
+                            # data available (efficient/measure competed data for stock/energy
+                            # variables only)
+                            if not self.handyvars.full_dat_out[adopt_scheme] and \
+                                    self.name not in ctrb_ms_pkg_prep:
+                                ecarb_cases, stk_cases, outputs, cost_keys = [
+                                    ["efficient"], ["measure"], ["competed"], ["stock", "energy"]]
+                            else:
+                                ecarb_cases, stk_cases, outputs, cost_keys = [
+                                    ["baseline", "efficient"], ["all", "measure"],
+                                    ["total", "competed"], ["stock", "energy", "carbon"]]
                             # Add in all cost data (stock, energy, and carbon)
                             add_to_dict["cost"] = {cost_key: {output: {
                                 # Add to cost data for baseline/efficient cases
@@ -6528,10 +6540,9 @@ def fill_mkts(self, msegs, msegs_cpl, convert_data, tsv_data_init, opts,
                                         or cost_key != "stock" and not opts.no_lnkd_op_costs)) else
                                     add_to_dict["cost"][cost_key][output][case][yr]
                                     for yr in self.handyvars.aeo_years} for case, stk_var, rmv_hp in
-                                zip(["baseline", "efficient"], ["all", "measure"],
+                                zip(ecarb_cases, stk_cases,
                                     [rmv_hp_dblct_base_stkcosts, rmv_hp_dblct_meas_stkcosts])
-                                } for output in ["total", "competed"]
-                            } for cost_key in ["stock", "energy", "carbon"]}
+                                } for output in outputs} for cost_key in cost_keys}
 
                     # Remove minor HVAC equipment stocks in cases where major HVAC tech. is also
                     # covered by the measure definition, as well as double counted stock and stock
@@ -6576,8 +6587,10 @@ def fill_mkts(self, msegs, msegs_cpl, convert_data, tsv_data_init, opts,
                         if opts.no_lnkd_op_costs:
                             for var in ["energy", "carbon"]:
                                 for case in ["baseline", "efficient"]:
-                                    add_dict["cost"][var]["total"][case] = {
-                                        yr: 0 for yr in self.handyvars.aeo_years}
+                                    add_dict["cost"][var]["total"][case], \
+                                        add_dict["cost"][var]["competed"][case] = ({
+                                            yr: 0 for yr in self.handyvars.aeo_years}
+                                            for n in range(2))
 
                     # Append lifetime data multiplied by # of stock units (after any adjustments to
                     # remove linked stock totals above), to support later calculation of