Smk2aermod updates

scripts/aermod/smk2ae/scripts/aermod.py

@@ -159,7 +159,7 @@ def get_sw_corner(df, met_grid):
     df['met_swcorner_lon'] = met_grid.colrow_to_ll(df['met_col'].astype('f'), 
       df['met_row'].astype('f'))['lon'].astype('f')
     df['met_cell'] = 'G' + df['met_col'].astype(str).str.zfill(3) + 'R' + df['met_row'].astype(str).str.zfill(3)
-    return df
+    return df.drop_duplicates('met_cell')
 
 def proc_area_sector(inv_list, grid_name, grid_desc, state_fips):
     '''
@@ -197,7 +197,7 @@ def proc_area_sector(inv_list, grid_name, grid_desc, state_fips):
         run_emis = match_surrogate(inv.emis[inv.emis['run_group'] == run_group], surg.xref)
         if grid_name.startswith('12'):
             # Grid at 4 km for HDON LDON HDOFF LDOFF HOTEL and OILGAS
-            if run_group[:3] in ('HDO','LDO','HOT','OIL'):
+            if run_group[:4] in ('HDON','LDON','HOTE','OILG'):
                 cell_grid = Grid('4%s' %grid_name[2:], grid_desc)
             else:
                 cell_grid = met_grid
@@ -269,6 +269,9 @@ def write_aermod_emis(inv, cell_size, run_emis, xref):
     key_cols = ['run_group','region_cd','met_cell','src_id','source_group','smoke_name']
     out_cols = key_cols + ['ann_value',]
     group_sccs = list(run_emis['scc'].drop_duplicates())
+    run_frac = run_emis[['region_cd','scc','frac']].groupby(['region_cd','scc'], as_index=False).sum()
+    run_emis = pd.merge(run_emis, run_frac, on=['region_cd','scc'], how='left', suffixes=['','_sum'])
+    run_emis['frac'] = run_emis['frac'] / run_emis['frac_sum']
     # Merge in the pollutants
     group_df = pd.merge(run_emis, inv.emis, on=['region_cd','scc'], how='left')
     group_df['ann_value'] = group_df['ann_value'] * group_df['frac']

scripts/aermod/smk2ae/smk2ae/area_io.py

@@ -18,6 +18,11 @@ def proc_area(df, grid_info, temp, params):
             write_daily_prof(df, temp)
         else:
             write_no_daily_prof(df, temp)
+    else:
+        run_group = df['run_group'].values[0]
+        fips_map = get_fips_map(df[['run_group','region_cd','met_cell','src_id',
+          'ann_value']])
+        write_county_xwalk(fips_map, run_group)
 
 def calc_cell_corner(df, grid_info, utm):
     '''
@@ -67,9 +72,12 @@ def write_parameters(df, grid_info, utm, params):
     # The polygons are grid cells
     df = calc_polygons(df, grid_info, utm)
     df = pd.merge(df, params, on='run_group', how='left')
-    if len(df[df['rh'].isnull()]) > 0:
+    for col in ['rh','release_height']:
+        if col in list(df.columns) and 'rel_ht' not in list(df.columns):
+            df.rename(columns={col: 'rel_ht'}, inplace=True)
+    if len(df[df['rel_ht'].isnull()]) > 0:
         print('Unmatched run groups in GROUP PARAMS file')
-        print(df.loc[df['rh'].isnull(), 'run_group'].drop_duplicates())
+        print(df.loc[df['rel_ht'].isnull(), 'run_group'].drop_duplicates())
         raise ValueError('Unmatched run group parameters')
     df['verts'] = 4
     out_cols = ['run_group','met_cell','src_id','rel_ht','verts','sz','utmx','utmy','x2','y2',
@@ -87,7 +95,7 @@ def write_daily_prof(df, temp):
     cols = ['region_cd','scc','run_group','met_cell','ann_value','src_id']
     hierarchy = [['region_cd','scc'],['region_cd',]]
     df = df[cols].copy().sort_values('ann_value').drop_duplicates(['run_group','region_cd','met_cell'], 
-      take_last=True)
+      keep='last')
     temp.profs.fillna(0, inplace=True)
     value_cols = ['month','day','hour','factor']
     run_group = df['run_group'].values[0]
@@ -150,6 +158,7 @@ def write_no_daily_prof(df, temp):
     run_group = df['run_group'].values[0]
     df.sort_values('src_id', inplace=True)
     states = list(df['region_cd'].str[:2].drop_duplicates())
+    print(run_group)
     if run_group[:3] in ('LDO','HDO','HOT'):
         fips_map = get_fips_map(df[['run_group','region_cd','met_cell','src_id',
           'ann_value']].copy())
@@ -175,7 +184,7 @@ def get_fips_map(df):
     '''
     Map county and cell by sum of emissions
     '''
-    df.sort_values('ann_value', inplace=True)
+    df = df.copy().sort_values('ann_value')
     df.drop_duplicates(['run_group','met_cell','src_id'], keep='last', inplace=True)
     df.drop('ann_value', axis=1, inplace=True)
     return df

scripts/aermod/smk2ae/smk2ae/grid_surg.py

@@ -50,6 +50,7 @@ def _get_gref(self, gref, scc_list):
         df['region_cd'] = df['region_cd'].apply(self._fix_fips) 
         df.ix[df['scc'].str.startswith('00'), 'scc'] = df.ix[df['scc'].str.startswith('00'), 
           'scc'].str[2:]
+        df.loc[df['scc'] != '', 'scc'] = df.loc[df['scc'] != '', 'scc'].astype(int).astype(str)
         df['code'] = df['code'].str.split('!').str[0].str.strip()
         return df[df['scc'].isin(scc_list)].copy()
 
@@ -164,6 +165,7 @@ def grid_sources(emis, surg, grid_info):
         except KeyError:
             raise KeyError('Missing surrogate for code %s' %code)
         if src_surg.cell != grid_info.XCELL:
+            print(src_surg.cell, grid_info.XCELL)
             raise ValueError('Gridding surrogate cell size does not match grid cell size')
         code_emis = pd.merge(code_emis, src_surg.table, on='region_cd', how='left')
         # Identify sources the cross reference successfully to a surrogate but are either missing a fraction