Revert changes to health report rendering and offshore wind recf inputs

input_processing/recf.py

@@ -369,9 +369,6 @@ def main(reeds_path, inputs_case):
             resource_adequacy_years
         )
         df_windofs.columns = ['wind-ofs_' + col for col in df_windofs]
-    else:
-        # Keep concat inputs consistent when offshore wind is disabled.
-        df_windofs = pd.DataFrame(index=df_windons.index)
 
     ### UPV
     df_upv = calculate_class_region_cf_hourly(

postprocessing/bokehpivot/core.py

@@ -27,7 +27,7 @@
 import bokeh.resources as br
 import bokeh.embed as be
 import datetime
-import urllib.parse as urlp
+import six.moves.urllib.parse as urlp
 import subprocess as sp
 import jinja2 as ji
 import reeds_bokeh as rb
@@ -612,36 +612,15 @@ def vizit_report(data_type, data_source, vizit_data, output_dir, auto_open):
     vizit_commit = '7011d363e40386264bedb3155629729b225fd22e'
     vizit_url = f'https://raw.githubusercontent.com/mmowers/vizit/{vizit_commit}/index.html'
     f_out_str = requests.get(vizit_url).text
-    # Vizit report data can contain NumPy scalars/arrays from pandas outputs;
-    # normalize them before JSON encoding so report generation does not fail.
-    data_str = json.dumps(data_dict, separators=(',',':'), default=json_scalar)
-    config_str = json.dumps(vizit_config, separators=(',',':'), default=json_scalar)
-    # Use callable replacements so backslashes in generated JSON are treated as
-    # literal text rather than regex replacement escapes.
-    f_out_str = re.sub(
-        'let config_load = .*;\n',
-        lambda _: f'let config_load = {config_str};\n',
-        f_out_str,
-        1,
-    )
-    f_out_str = re.sub(
-        'let rawData = .*;\n',
-        lambda _: f'let rawData = {data_str};\n',
-        f_out_str,
-        1,
-    )
+    data_str = json.dumps(data_dict, separators=(',',':'))
+    config_str = json.dumps(vizit_config, separators=(',',':'))
+    f_out_str = re.sub('let config_load = .*;\n', f'let config_load = {config_str};\n', f_out_str, 1)
+    f_out_str = re.sub('let rawData = .*;\n', f'let rawData = {data_str};\n', f_out_str, 1)
     with open(f'{output_dir}report_vizit.html', 'w') as f_out:
         f_out.write(f_out_str)
     if auto_open == 'Yes':
         sp.Popen(os.path.abspath(f'{output_dir}report_vizit.html'), shell=True)
 
-def json_scalar(obj):
-    if isinstance(obj, np.generic):
-        return obj.item()
-    if isinstance(obj, np.ndarray):
-        return obj.tolist()
-    raise TypeError(f'Object of type {obj.__class__.__name__} is not JSON serializable')
-
 def preset_wdg(preset, download_full_source=False):
     '''
     Reset widgets and then set them to that specified in input preset

postprocessing/bokehpivot/reeds2.py

@@ -1111,49 +1111,8 @@ def net_co2(dfs, **kw):
     co2['Cumulative CO2e (MMton)'] /= 1e6
     return co2
 
-# health_damages_caused_r.csv is now written with the current 9-column schema
-# from postprocessing/air_quality, while these report paths still use the older
-# bokehpivot display names downstream. Keep both lists explicit at the boundary.
-HEALTH_DAMAGE_SOURCE_COLUMNS = [
-    'rb', 'year', 'e', 'Emissions (thousand metric tons)',
-    'model', 'cr', 'Marginal damage ($/metric ton)',
-    'Health damages (billion $)', 'Health damages (lives)'
-]
-HEALTH_DAMAGE_LEGACY_COLUMNS = [
-    'rb', 'st', 'year', 'e', 'Emissions (thousand metric tons)',
-    'model', 'cr', 'Marginal damage ($/metric ton)',
-    'Health damages (billion $)', 'Health damages (lives)'
-]
-
-def normalize_health_damage_schema(df):
-    # Accept either current generator headers or legacy report headers, then
-    # return the legacy names expected by the existing report calculations.
-    df = df.copy()
-    df.rename(columns={
-        'ba': 'rb',
-        'pollutant': 'e',
-        'tons': 'Emissions (thousand metric tons)',
-        'md': 'Marginal damage ($/metric ton)',
-        'damage_$': 'Health damages (billion $)',
-        'mortality': 'Health damages (lives)',
-    }, inplace=True)
-
-    if 'st' not in df.columns:
-        # Current health-damage outputs do not include state. Keep a placeholder
-        # for older bokehpivot paths; derive this from hierarchy if it is used.
-        df['st'] = df['rb'] if 'rb' in df.columns else ''
-
-    required = HEALTH_DAMAGE_SOURCE_COLUMNS + ['st']
-    missing = [col for col in required if col not in df.columns]
-    if missing:
-        raise KeyError('health_damages_caused_r.csv missing columns: {}'.format(missing))
-
-    return df[required]
-
 # function to process health damage estimates
 def process_health_damage(df, **kw):
-    df = normalize_health_damage_schema(df)
-
     # convert to billion $ and inflate series
     df['Health damages (billion $)'] = inflate_series(df['Health damages (billion $)']) * 1e-9
 
@@ -1176,27 +1135,24 @@ def process_health_damage(df, **kw):
     rows = product(*allRows.values())
     df_all = pd.DataFrame.from_records(rows, columns=allRows.keys())
     df_new = df.merge(df_all, how='outer', on=['model', 'cr', 'e', 'rb', 'year'])
-    df_new['st'] = df_new['st'].ffill()
+    df_new['st'] = df_new['st'].interpolate(method="ffill")
 
     # sort by category and year
     df_new = df_new.sort_values(['model', 'cr', 'e', 'rb', 'year'])
 
     # interpolate any missing values 
-    df_new = df_new.groupby(
-        ['model', 'cr', 'e', 'rb'], group_keys=False
-    ).apply(lambda group: group.interpolate(method='ffill'))
+    df_new = df_new.groupby(['model', 'cr', 'e', 'rb']).apply(lambda group: group.interpolate(method='ffill'))
 
     # sum over rb
-    df_out = df_new.groupby(['model', 'cr', 'e', 'year'])[[
-        'Emissions (thousand metric tons)', 'Health damages (billion $)',
-        'Health damages (lives)', 'Discounted health damages (billion $)'
-    ]].sum().reset_index()
+    df_out = df_new.groupby(['model', 'cr', 'e', 'year'])[
+            'Emissions (thousand metric tons)', 'Health damages (billion $)', 
+            'Health damages (lives)', 'Discounted health damages (billion $)'
+        ].sum().reset_index()
 
     # also sum over pollutant   
-    df_poll_agg = df_new.groupby(['model', 'cr', 'year'])[[
-        'Health damages (billion $)', 'Health damages (lives)',
-        'Discounted health damages (billion $)'
-    ]].sum().reset_index()
+    df_poll_agg = df_new.groupby(['model', 'cr', 'year'])[
+            'Health damages (billion $)', 'Health damages (lives)', 'Discounted health damages (billion $)'
+        ].sum().reset_index()
 
     df_poll_agg.rename(columns={'Health damages (billion $)' : 'Total health damages (billion $)', 
                                 'Health damages (lives)' : 'Total health damages (lives)',
@@ -1218,9 +1174,7 @@ def process_social_costs(dfs, **kw):
     system_costs_agg.rename(columns={'Cost (Bil $)' : 'Cost (Bil $)-system', 
                                     'Discounted Cost (Bil $)' : 'Discounted Cost (Bil $)-system'}, inplace=True)
 
-    health_costs_agg = health_costs.groupby(['year', 'model', 'cr'])[[
-        'Health damages (billion $)', 'Discounted health damages (billion $)'
-    ]].sum().reset_index()
+    health_costs_agg = health_costs.groupby(['year', 'model', 'cr'])['Health damages (billion $)', 'Discounted health damages (billion $)'].sum().reset_index()
     health_costs_agg.rename(columns={'Health damages (billion $)' : 'Cost (Bil $)-health', 
                                     'Discounted health damages (billion $)' : 'Discounted Cost (Bil $)-health'}, inplace=True)
 
@@ -2113,10 +2067,7 @@ def pre_spur(dfs, **kw):
 
     ('Health Damages from Emissions',
         {'file':'health_damages_caused_r.csv',
-        'columns': HEALTH_DAMAGE_LEGACY_COLUMNS,
-        # Preserve the CSV's real headers when the current 9-column output is
-        # present; process_health_damage normalizes the schema before plotting.
-        'allow_mismatched_columns': True,
+        'columns': ['rb', 'st', 'year', 'e', 'Emissions (thousand metric tons)', 'model', 'cr', 'Marginal damage ($/metric ton)', 'Health damages (billion $)', 'Health damages (lives)'],
         'preprocess': [
             {'func': process_health_damage, 'args':{}},
         ],
@@ -2139,9 +2090,7 @@ def pre_spur(dfs, **kw):
             {'name': 'df_capex_init', 'file': '../inputs_case/df_capex_init.csv'},
             {'name': 'sw', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
             {'name': 'scalars', 'file': '../inputs_case/scalars.csv', 'header':None, 'columns': ['scalar', 'value', 'comment']},
-            # See the Health Damages source above: keep current headers intact
-            # and normalize them inside process_social_costs/process_health_damage.
-            {'name': 'health_damages', 'file': 'health_damages_caused_r.csv', 'columns': HEALTH_DAMAGE_LEGACY_COLUMNS, 'allow_mismatched_columns': True},
+            {'name': 'health_damages', 'file': 'health_damages_caused_r.csv', 'columns': ['rb', 'st', 'year', 'e', 'Emissions (thousand metric tons)', 'model', 'cr', 'Marginal damage ($/metric ton)', 'Health damages (billion $)', 'Health damages (lives)']},
         ],  
         'preprocess': [
             {'func': process_social_costs, 'args': {}},

postprocessing/bokehpivot/reeds_bokeh.py

@@ -359,15 +359,7 @@ def get_src(scen, src):
     if 'transpose' in src and src['transpose'] is True:
         df_src = df_src.T
     if 'columns' in src:
-        if len(df_src.columns) == len(src['columns']):
-            df_src.columns = src['columns']
-        elif not src.get('allow_mismatched_columns', False):
-            df_src.columns = src['columns']
-        else:
-            # Some newer outputs are normalized in their preprocess functions.
-            # For those opt-in sources, preserve CSV headers when legacy report
-            # columns no longer match the generated file width.
-            pass
+        df_src.columns = src['columns']
     df_src.replace('Eps',0, inplace=True)
     df_src.replace('Undf',0, inplace=True)
     df_src = df_src.apply(pd.to_numeric, errors='ignore')
@@ -627,4 +619,4 @@ def df_to_lowercase(df):
     for col in df:
         if df[col].dtype == object:
             df[col] = df[col].str.lower()
-    return df
+    return df