Add 'get_adopted_input_values.py'

inputs/get_adopted_input_values.py

@@ -0,0 +1,148 @@
+import os
+import json
+import yaml
+import numpy as np
+import pandas as pd
+from scipy.optimize import curve_fit
+
+fname = 'adopted-input-values.xlsx'
+output_path = '../probabilistic-analysis'
+
+# Load input file
+df = pd.read_excel(fname)
+
+# Remove calculation columns (headers that begin with #)
+df = df.drop([c for c in df.columns if c.startswith('#')], axis=1)
+
+# Parse lists and other objects in dataframe
+for col_name in [c for c in df.columns if df[c].dtype == 'O']:
+    parsed_vals = []
+    for i, cell_contents in enumerate(df[col_name]):
+        if type(cell_contents) is not str:
+            parsed_vals.append([])
+        else:
+            try:
+                parsed_vals.append(json.loads(cell_contents))
+            except json.decoder.JSONDecodeError as e:
+                raise Exception(
+                    ('\n\nInvalid JSON string in column "{}" on row {}: {}'
+                     '\nEnsure brackets are matched, and strings are '
+                     'double-quoted, e.g. ["A", "B", "C"]\n').format(
+                         col_name, i + 2,
+                         cell_contents)).with_traceback(e.__traceback__)
+
+    df[col_name] = parsed_vals
+
+    df = df.rename(columns={col_name: col_name.replace('#list', '')})
+
+parameters = {
+    'beach_scenario':
+    '# Beach scenrio name including north/south or other special conditions',
+    'beach_name': '# Beach name as it appears in photgrammetry database',
+    'n_runs': '# Number of runs for Monte Carlo simulation',
+    'start_year': '# Beginning of period for simulation',
+    'end_year': '# End of period for simulation',
+    'output_years': '# Years used for reporting',
+    'output_ep': '# Encounter probability values for reporting',
+    'output_folder': '# Path to output folder',
+    'figure_folder': '# Path to figure folder',
+    'zsa_profile_file':
+    '# Path to storm demand file (zone of slope adjustment)',
+    'zrfc_profile_file':
+    '# Path to storm demand file (zone of reduced foundation capacity)',
+    'sea_level_rise': '# Projected sea levels (m)',
+    'storm_demand':
+    '# Storm demand annual recurrance intervals (years) and volumes (m3/m)',
+    'bruun_factor': '# Bruun factor (-)',
+    'underlying_recession': '# Underlying shoreline recession rate (m/year)',
+    'diagnostics': '# Choose profiles to output probabilistic diagnostics',
+    'omit': '# Choose profiles to omit from analysis',
+    'min_chainage':
+    '# Set minimum chainages for non-erodable sections of profiles',
+    'segment_gaps':
+    '# Break shoreline into multiple segments (with a gap at this profile)',
+    'insert_points': '# Insert points before specific profile',
+    'append_points': '# Add points after specific profile',
+}
+
+
+def gordon_log_fit(ari, p1, p2):
+    """
+    Fit log curve based on ARI values. Gordon (1987) provides two cases:
+        1. Low demand, open beaches:
+            p1 = 5
+            p2 = 30
+        2. High demand, rip heads
+            p1 = 40
+            p2 = 40
+
+    Args:
+        ari (array_like): input array of ARI values
+        p1 (float): parameter 1
+        p2 (float): parameter 2
+    Returns:
+        the fitted values for the given ARIs
+    """
+
+    return p1 + p2 * np.log(ari)
+
+
+def get_storm_demand(x):
+    """
+    Get storm demand based on a 100 year ARI storm demand volume.
+
+    Args:
+        x (float): storm demand volume for 100 year ARI event
+    Returns:
+        storm demand volumes for 1, 10, 100, 1000 year ARI events
+    """
+    if x > 140:
+        # Repeat values to supress scipy covariance warning
+        ref_ari = np.array([0.03, 0.03, 100, 100])
+        ref_vol = np.array([-100, -100, x, x])
+    else:
+        # Repeat values to supress scipy covariance warning
+        ref_ari = np.array([1, 1, 100, 100])
+        ref_vol = np.array([1, 1, x, x])
+
+    ari = np.array([1, 10, 100, 1000])
+    try:
+        p, _ = curve_fit(gordon_log_fit, ref_ari, ref_vol, p0=(5., 30.))
+        vol = gordon_log_fit(ari, *p)
+    except ValueError:
+        vol = ari * np.nan
+
+    return ari, vol
+
+
+# Calculate storm demand volumes
+df = df.assign(storm_demand_ari=0).astype('object')
+df = df.assign(storm_demand_vol=0).astype('object')
+
+for i in range(len(df)):
+    vol_ari_100 = df['storm_demand'][i]
+    ari, vol = get_storm_demand(vol_ari_100)
+
+    df.at[i, 'storm_demand_ari'] = ari.tolist()
+    df.at[i, 'storm_demand_vol'] = vol.round(1).tolist()
+
+# Remove original storm demand column
+df = df.drop('storm_demand', axis=1)
+
+for i, row in df.sort_values(by='beach_scenario').iterrows():
+    output_file = os.path.join(output_path, row['beach_scenario'] + '.yaml')
+    with open(output_file, 'w') as f:
+        f.write('# {}\n'.format(row['beach_scenario']))
+
+        for parameter, description in parameters.items():
+            col_names = [c for c in df.columns if parameter in c]
+
+            # Write parameter description
+            f.write('\n{}\n'.format(description))
+
+            # Collect and write parameter values
+            values = {c.split('_')[-1]: row[c] for c in col_names}
+            if len(values) == 1:
+                # Remove from dictionary if parameter just has one value
+                values = list(values.values())[0]
+            yaml.dump({parameter: values}, f, default_flow_style=False)