def write_runways(self, grid, temp):
'''
Write the files for airports with runways
'''
inv_cols = [
'facility_id', 'facility_name', 'fac_col', 'fac_row', 'state'
]
df = pd.merge(self.locs,
self.inv[inv_cols].drop_duplicates(),
on='facility_id',
how='left')
if not df.empty:
print 'Airports facilities with runways: %s' % len(
df['facility_id'].drop_duplicates())
df['length'] = df.apply(lambda row: grid.calc_dist(\
row['start_x'],row['start_y'],row['end_x'],row['end_y']), axis=1)
df['area'] = df['length'] * df['width']
df['lnemis'] = 1000. / df['area']
df['relhgt'] = 3.
df['szinit'] = 3.
df['type'] = 'LINE'
df['centroid_lon'] = grid.colrow_to_ll(
df['fac_col'].astype('f') + 0.5,
df['fac_row'].astype('f') + 0.5)['lon'].astype('f')
utm = UTM()
df['utm_zone'] = df['centroid_lon'].apply(utm.get_zone)
df[['xs1','ys1']] = df[['start_x','start_y','utm_zone']].apply(lambda x: pd.Series(\
utm.get_coords(x['start_x'],x['start_y'],x['utm_zone'])), axis=1)
df[['xs2','ys2']] = df[['end_x','end_y','utm_zone']].apply(lambda x: pd.Series(\
utm.get_coords(x['end_x'],x['end_y'],x['utm_zone'])), axis=1)
self._write_runway_locs(df)
self._write_runway_params(df)
self._write_temp_prof(df, temp, 'line')
def write_no_runways(self, grid, temp):
'''
Write the files for airports without runways
'''
inv_cols = [
'facility_id', 'facility_name', 'fac_col', 'fac_row', 'state',
'latitude', 'longitude'
]
df = self.inv.ix[~self.inv['facility_id'].isin(self.runways),
inv_cols].copy()
df.drop_duplicates(inplace=True)
if not df.empty:
print 'Airports facilities without runways: %s' % len(
df['facility_id'].drop_duplicates())
df['len_x'] = 10.
df['len_y'] = 10.
df['src_id'] = 'AP1'
df['relhgt'] = 3.
df['szinit'] = 3.
df['angle'] = 0
df['centroid_lon'] = grid.colrow_to_ll(
df['fac_col'].astype('f') + 0.5,
df['fac_row'].astype('f') + 0.5)['lon'].astype('f')
utm = UTM()
df['utm_zone'] = df['centroid_lon'].apply(utm.get_zone)
df[['utm_x','utm_y']] = df[['longitude','latitude','utm_zone']].apply(lambda x: pd.Series(\
utm.get_coords(x['longitude'],x['latitude'],x['utm_zone'])), axis=1)
df[['grid_x','grid_y']] = df[['longitude','latitude']].apply(lambda x: pd.Series(\
grid.get_coords(x['longitude'],x['latitude'])), axis=1)
self._write_nonrunway_locs(df)
self._write_nonrunway_params(df)
self._write_temp_prof(df, temp, 'nonrunway')
def main():
var_list = ('GRIDDESC', 'REGION_IOAPI_GRIDNAME', 'EMISINV_A', 'HAPS_LIST',
'BASE_YEAR', 'SECTOR', 'WORK_PATH', 'POLY_FILE', 'STATE_FIPS')
check_env_vars(var_list)
inv_list = get_inv_list()
state_fips = os.environ['STATE_FIPS']
emis = read_emissions(inv_list, os.environ['HAPS_LIST'], state_fips)
shapes = read_shapes(os.environ['POLY_FILE'], state_fips)
# Drop shapes that have no emissions
shapes = pd.merge(shapes,
emis[['facid', 'src_id',
'scc']].drop_duplicates(['facid', 'src_id']),
on=['facid', 'src_id'],
how='left')
shapes = shapes[shapes['scc'].notnull()].copy()
shapes.drop('scc', axis=1, inplace=True)
met_grid = Grid(os.environ['REGION_IOAPI_GRIDNAME'],
os.environ['GRIDDESC'])
cell_poly_df = get_met_cell(shapes, met_grid)
shapes.drop(['col', 'row', 'cell'], axis=1, inplace=True)
shapes = pd.merge(shapes, cell_poly_df, on=['facid', 'src_id'], how='left')
shapes['centroid_lon'] = met_grid.colrow_to_ll(
shapes['col'].astype('f') + 0.5,
shapes['row'].astype('f') + 0.5)['lon'].astype('f')
utm = UTM()
shapes['utm_zone'] = shapes['centroid_lon'].apply(utm.get_zone)
shapes[['utmx','utmy']] = shapes[['lon','lat','utm_zone']].apply(lambda x: \
pd.Series(utm.get_coords(x['lon'],x['lat'],x['utm_zone'])), axis=1)
shapes['type'] = 'AREAPOLY'
state_rh = calc_state_rh(emis[['region_cd', 'scc', 'ann_value',
'ure']].copy())
'''
Ports and underway get separate output files except for the emissions files
'''
shapes['state'] = shapes['region_cd'].str[:2]
for source_type in ('ports', 'underway'):
print 'Running for %s' % source_type
shapes_type = shapes[shapes['facid'].str.startswith(
source_type[0].upper())].copy()
if shapes_type.empty:
print 'WARNING: No %s sources found' % source_type
else:
emis_type = emis[emis['facid'].str.startswith(
source_type[0].upper())].copy()
write_emis(emis_type.copy(), source_type)
write_locations(
shapes_type.copy().drop_duplicates([
'region_cd', 'facid', 'src_id', 'type', 'col', 'row',
'utm_zone'
]), source_type)
write_parameters(shapes_type, state_rh, source_type)
write_temporal(emis_type, shapes_type, source_type)
def get_utm_location(df, grid_info):
'''
Select UTM zone based on centroid of met grid cell. Calc UTM X and Y.
'''
utm = UTM()
df['centroid_lon'] = grid_info.colrow_to_ll(df['col'].astype('f') + 0.5,
df['row'].astype('f') +
0.5)['lon'].astype('f')
utm = UTM()
df['utm_zone'] = df['centroid_lon'].apply(utm.get_zone)
df[['utm_x','utm_y']] = df[['longitude','latitude','utm_zone']].apply(lambda x: \
pd.Series(utm.get_coords(x['longitude'],x['latitude'],x['utm_zone'])), axis=1)
return df
def main():
var_list = ('EMISINV_A','HAPS_LIST','BASE_YEAR','WORK_PATH','POLY_FILE','STATE_FIPS',
'HEM_GROUPS','SRG_XREF','SRG_PATH','SRG_PREFIX','RUN_GROUPS')
check_env_vars(var_list)
inv_list = get_inv_list()
state_fips = os.environ['STATE_FIPS']
inv = AnnualFF10(inv_list, os.environ['HAPS_LIST'], state_fips)
hem = HEMGroups(os.environ['HEM_GROUPS'])
inv.emis = pd.merge(inv.emis, hem.xref, on='scc', how='left')
# Narrow down to just the selected run groups.
if os.environ['RUN_GROUPS']:
run_groups = [rg.strip() for rg in os.environ['RUN_GROUPS'].split(',')]
inv.emis = inv.emis[inv.emis['run_group'].isin(run_groups)].copy()
inv.update_lists()
else:
run_groups = list(inv.emis['run_group'].drop_duplicates())
if inv.emis.empty:
raise ValueError, 'No emissions found for given states and run groups.'
surgs = nonconus_surgs(os.environ['SRG_PATH'], os.environ['SRG_PREFIX'])
surgs.read_xref(os.environ['SRG_XREF'])
inv.emis = surgs.match_emis(inv.emis)
if not inv.emis[inv.emis['tract'].isnull()].empty:
print inv.emis[inv.emis['tract'].isnull()].drop_duplicates()
raise ValueError, 'Missing tract cross-reference'
shapes = read_shapes(os.environ['POLY_FILE'], inv.fips_list)
utm_zones = shapes[['facid','lon']].copy().drop_duplicates('facid')
utm = UTM()
utm_zones['utm_zone'] = utm_zones['lon'].apply(utm.get_zone)
shapes = pd.merge(shapes, utm_zones[['facid','utm_zone']], on='facid', how='left')
shapes[['utmx','utmy']] = shapes[['lon','lat','utm_zone']].apply(lambda x: \
pd.Series(utm.get_coords(x['lon'],x['lat'],x['utm_zone'])), axis=1)
temp = Temporal(os.environ['ATREF'],os.environ['ATPRO_HOURLY'],os.environ['ATPRO_WEEKLY'],
os.environ['ATPRO_MONTHLY'], inv.scc_list, inv.fips_list)
for run_group in list(inv.emis['run_group'].drop_duplicates()):
for state in list(inv.emis['region_cd'].str[1:3].drop_duplicates()):
write_aermod_emis(inv, surgs, shapes, hem.xref, run_group, state)
emis_group = inv.emis.ix[(inv.emis['run_group'] == run_group) & \
(inv.emis['region_cd'].str[1:3] == state), ['region_cd','run_group','tract']].copy()
emis_group.drop_duplicates(inplace=True)
write_locations(emis_group, shapes.copy().drop_duplicates('facid'), run_group, state)
write_parameters(emis_group, shapes, run_group, state)
temp_group = pd.merge(inv.emis[(inv.emis['run_group'] == run_group) & \
(inv.emis['region_cd'].str[1:3] == state)], shapes[['region_cd','facid','tract',
'polynumber','src_id']].drop_duplicates(), on=['region_cd','tract'], how='left')
if temp.use_daily:
write_daily_prof(temp_group, temp, run_group, state)
else:
write_no_daily_prof(temp_group, temp, run_group, state)
def proc_shapes(shapes, met_grid):
'''
Process the shapes into the correct cells and reproject to UTM
'''
cell_poly_df = get_met_cell(shapes, met_grid)
shapes.drop(['col', 'row', 'cell'], axis=1, inplace=True)
shapes = pd.merge(shapes, cell_poly_df, on=['facid', 'src_id'], how='left')
shapes['centroid_lon'] = met_grid.colrow_to_ll(
shapes['col'].astype('f'),
shapes['row'].astype('f'))['lon'].astype('f')
utm = UTM()
shapes['utm_zone'] = shapes['centroid_lon'].apply(utm.get_zone)
shapes[['utmx','utmy']] = shapes[['lon','lat','utm_zone']].apply(lambda x: \
pd.Series(utm.get_coords(x['lon'],x['lat'],x['utm_zone'])), axis=1)
shapes['type'] = 'AREAPOLY'
shapes['state'] = shapes['region_cd'].str[:2]
return shapes