|
|
|
@ -452,14 +452,19 @@ def process(beach_name, beach_scenario, n_runs, start_year, end_year,
|
|
|
|
|
|
|
|
|
|
col_names = [c for c in df_in.columns if c.isdigit()]
|
|
|
|
|
|
|
|
|
|
# Loop through profiles
|
|
|
|
|
dff = df_in[df_in['beach'] == beach_name]
|
|
|
|
|
# Make sure blocks are always strings
|
|
|
|
|
df_omit = pd.DataFrame(omit)
|
|
|
|
|
df_omit['block'] = df_omit['block'].astype(str)
|
|
|
|
|
df_in['block'] = df_in['block'].astype(str)
|
|
|
|
|
|
|
|
|
|
# Remove omitted profiles
|
|
|
|
|
dff = pd.merge(
|
|
|
|
|
pd.DataFrame(omit), dff, how='outer',
|
|
|
|
|
df_in = pd.merge(
|
|
|
|
|
df_omit, df_in, how='outer',
|
|
|
|
|
indicator='source').query('source!="both"').drop(columns='source')
|
|
|
|
|
|
|
|
|
|
# Loop through profiles
|
|
|
|
|
dff = df_in[df_in['beach'] == beach_name]
|
|
|
|
|
|
|
|
|
|
pbar_profile = tqdm(dff.iterrows(), total=dff.shape[0], leave=False)
|
|
|
|
|
for i, prof in pbar_profile:
|
|
|
|
|
|
|
|
|
|