Fix: combining structural and alt_structural tags

README.md

@@ -68,10 +68,10 @@ python -m atc138.cli ./examples/ICSB ./examples/ICSB/output --seed 12345 --force
 
 ### Imported via Python script
 
-Ensure that the `src/` directory is on the path of the main script. Then:
+Once installed, the package can also be imported in scripts:
 
 ```python
-from src.atc138 import driver
+from atc138 import driver
 
 example_dir = './examples/ICSB'
 output_dir = './examples/ICSB/output'
@@ -216,7 +216,7 @@ To use Pelicun outputs, ensure `simulated_inputs.json` does not exist in your mo
 The assessment will automatically detect Pelicun files and convert them to the standard ATC-138 format. Use the same CLI or Python API as normal:
 
 ```python
-from src.atc138 import driver
+from atc138 import driver
 
 example_dir = './examples/RCSW_4story_pelicun'
 output_dir = './examples/RCSW_4story_pelicun/output'

src/atc138/functionality/other_functionality_functions.py

@@ -1540,7 +1540,8 @@ def fn_extract_recovery_metrics( tenant_unit_recovery_day,
 
 
     # Determine replacement cases
-    replace_cases = np.logical_not(np.isnan(simulated_replacement_time))
+    sim_rt = np.array(simulated_replacement_time, dtype=float)
+    replace_cases = ~np.isnan(sim_rt)
     ''' Post process tenant-level recovery times
       Overwrite NaNs in tenant_unit_day_functional
       Only NaN where never had functional loss, therefore set to zero'''

src/atc138/input_builder.py

@@ -740,6 +740,7 @@ def convert_pelicun(model_dir):
     accepted_first_tag = ['cmp', 'dmg', 'loss']
     assert any(item in accepted_first_tag for item in dv_tag_meta), "Missing meta-tag in index column (i.e. 'cmp/dmg/loss-loc-dir-ds' must be provided at minimum)"
     assert set(reqd_tags) <= set(dv_tag_meta), "Missing meta-tag in index column (i.e. 'cmp/dmg/loss-loc-dir-ds' must be provided at minimum)"
+    assert 'uid' not in dv_tag_meta, "uid found in DV input meta-tags. Duplicate cmp/dmg/loss-loc-dir-ds is not supported via uid; Condense duplicate cmp/dmg/loss-loc-dir-ds"
     DV_time = dvs.loc[:, dvs.columns.str.upper().str.startswith("TIME")]
     DV_cost = dvs.loc[:, dvs.columns.str.upper().str.startswith("COST")]
 

src/atc138/red_tag.py

@@ -54,7 +54,9 @@ def simulate_tagging(damage, comps, sc_ids, sc_thresholds, red_tag_options ):
                     num_comps = np.array(comps['story'][s]['qty_dir_' + str(direc)])
 
                     # For each structural system
-                    structural_systems = np.unique(np.array(damage['comp_ds_table']['structural_system'] + damage['comp_ds_table']['structural_system_alt']))
+                    structural_systems = np.unique(np.concatenate(
+                        (np.array(damage['comp_ds_table']['structural_system']), np.array(damage['comp_ds_table']['structural_system_alt']))
+                    ))
                     structural_systems = np.delete(structural_systems, 0) # do not include components not assigned to a structural system
 
                     sys_tag = np.zeros([num_reals, len(structural_systems)]).astype(bool)
@@ -69,30 +71,43 @@ def simulate_tagging(damage, comps, sc_ids, sc_thresholds, red_tag_options ):
 
                         # Check damage among each series within this structural system
                         series = np.unique(np.array(damage['comp_ds_table']['structural_series_id'])[ss_filt_ds])
-                        ser_dmg = np.zeros([num_reals,len(series)])
-                        ser_qty = np.zeros([1,len(series)])
+
+                        # Allocate ONCE (outside the ser loop)
+                        ser_dmg = np.zeros((num_reals, len(series)))   # [num_reals x num_series]
+                        ser_qty = np.zeros(len(series))               # [num_series]
+
                         for ser in range(len(series)):
-                            ser_filt_ds = np.array(damage['comp_ds_table']['structural_series_id']) == series[ser] 
-                            ser_filt_comp = np.array(comps['comp_table']['structural_series_id']) == series[ser] 
-        
-                            # Total damage within this series and system
-                            ser_dmg[:,ser] = np.sum(sc_dmg[:,ser_filt_ds & ss_filt_ds], axis=1)
-        
-                            # Total number of components within this series and system
-                            ser_qty[:,ser] = np.sum(num_comps[ser_filt_comp & ss_filt_comp])
+                            ser_filt_ds = np.array(damage['comp_ds_table']['structural_series_id']) == series[ser]
+                            ser_filt_comp = np.array(comps['comp_table']['structural_series_id']) == series[ser]
+
+                            # Total damage within this series and system (per realization)
+                            ser_dmg[:, ser] = np.sum(sc_dmg[:, (ser_filt_ds & ss_filt_ds)], axis=1)
+
+                            # Total number of components within this series and system (constant across realizations)
+                            ser_qty[ser] = np.sum(num_comps[ser_filt_comp & ss_filt_comp])
 
                         # Check if this system is causing a red tag
                         if structural_systems[sys] == 12 and bool(red_tag_options.get('tag_coupling_beams_over_height', False)):
                             # HARED CODED CHECK FOR COUPLING BEAMS
                             # just do counts for now instead of adding to red tag per story check
                             coupling_beam_dmg[:, direc-1] = coupling_beam_dmg[:,direc-1] + np.nanmax(ser_dmg, axis = 1)
-                            coupling_beam_qty[0, direc-1] = coupling_beam_qty[0,direc-1] + np.nanmax(ser_qty, axis = 1)
+                            coupling_beam_qty[0, direc-1] = coupling_beam_qty[0, direc-1] + np.nanmax(ser_qty)
                             red_tag_impact_cb_sc = np.fmax(red_tag_impact_cb_sc, ss_filt_ds.reshape(1,-1) * sc_filt.reshape(1,-1) * (sc_dmg>0))
                         else:
-                            sys_dmg = np.nanmax(ser_dmg, axis = 1)
-                            sys_qty = np.nanmax(ser_qty, axis = 1)
-                            sys_ratio = sys_dmg / sys_qty
-                            sys_tag[:,sys] = sys_ratio > sc_thresholds[sc]
+                            empty_bin = (ser_dmg.size == 0 or ser_dmg.shape[1] == 0 or ser_qty.size == 0)
+
+                            if empty_bin:
+                                sys_ratio = np.zeros((num_reals,), dtype=float)   # implies "not tagged" for this bin
+                            else:
+                                sys_dmg = np.nanmax(ser_dmg, axis=1)   # (num_reals,)
+                                sys_qty = float(np.nanmax(ser_qty))    # scalar
+
+                                # avoid division by 0 / NaN issues
+                                sys_ratio = np.zeros((num_reals,), dtype=float)
+                                if np.isfinite(sys_qty) and sys_qty > 0:
+                                    ok = np.isfinite(sys_dmg)
+                                    sys_ratio[ok] = sys_dmg[ok] / sys_qty
+                            sys_tag[:, sys] = sys_ratio > sc_thresholds[sc]
 
                             '''Calculate the impact that each component has on red tag
                             (boolean, 1 = affects red tag, 0 = does not affect)
@@ -160,7 +175,8 @@ def simulate_tagging(damage, comps, sc_ids, sc_thresholds, red_tag_options ):
 
 
     # Account for global red tag cases
-    replace_case = np.logical_not(np.isnan(np.array(simulated_replacement_time)))
+    sim_rt = np.array(simulated_replacement_time, dtype=float)
+    replace_case = ~np.isnan(sim_rt)
     red_tag[replace_case] = 1
 
     return red_tag, red_tag_impact, inspection_tag

src/atc138/repair_schedule/other_repair_schedule_functions.py

@@ -813,7 +813,8 @@ def fn_format_gantt_chart_data( damage, systems, simulated_replacement_time):
     comps = np.unique(damage['comp_ds_table']['comp_id'])
 
     # Determine replacement cases
-    replace_cases = np.logical_not(np.isnan(simulated_replacement_time))
+    sim_rt = np.array(simulated_replacement_time, dtype=float)
+    replace_cases = ~np.isnan(sim_rt)
 
     ## Reformat repair schedule data into various breakdowns
     # Per component