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@ -329,7 +329,7 @@ def get_storm_demand_volume(ref_aep, ref_vol, n, mode='fit'):
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def process(beach_name, beach_scenario, n_runs, start_year, end_year,
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output_years, output_ep, zsa_profile_file, zfc_profile_file,
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output_years, output_ep, zsa_profile_file, zrfc_profile_file,
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output_folder, figure_folder, sea_level_rise, bruun_factor,
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underlying_recession, storm_demand, plot_stats, omit_from_shp,
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min_chainage, segment_gaps, insert_points, append_points):
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@ -345,7 +345,7 @@ def process(beach_name, beach_scenario, n_runs, start_year, end_year,
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output_years (list): years to save profiles
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output_ep (list): EP values for saved profiles
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zsa_profile_file (str): path to storm demand vs chainge data (ZSA)
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zfc_profile_file (str): path to storm demand vs chainge data (ZFC)
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zrfc_profile_file (str): path to storm demand vs chainge data (ZRFC)
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output_folder (str): where to save profiles
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sea_level_rise (dict):
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'year' (list): years
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@ -420,14 +420,14 @@ def process(beach_name, beach_scenario, n_runs, start_year, end_year,
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np.array(ref_vol)[sort_idx])
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# Load profile data for current beach
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pbar_profiles = tqdm(['ZSA', 'ZFC'], leave=False)
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pbar_profiles = tqdm(['ZSA', 'ZRFC'], leave=False)
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for profile_type in pbar_profiles:
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pbar_profiles.set_description('{}'.format(profile_type))
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if profile_type == 'ZSA':
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df_in = pd.read_csv(zsa_profile_file)
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if profile_type == 'ZFC':
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df_in = pd.read_csv(zfc_profile_file)
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if profile_type == 'ZRFC':
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df_in = pd.read_csv(zrfc_profile_file)
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col_names = [c for c in df_in.columns if c.isdigit()]
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