lidar/generate_recession_tables.py

@@ -1,88 +0,0 @@
-"""generate_recession_tables.py
-
-Generate ZSA and ZRFC recession tables (storm demand vs. chainage) based on
-Nielsen et al. (1992) for lidar-derived beach profiles.
-
-Reads:
-    Profiles 1 to 12 2019 DEM.xlsx
-
-Writes:
-    recession_results_zsa.csv
-    recession_results_zrfc.csv
-
-D Howe
-d.howe@wrl.unsw.edu.au
-2022-05-06
-"""
-
-import os
-import re
-import json
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from nielsen import Gridder, BeachProfile
-
-BEACH = 'Roches Beach'
-COLUMNS = ['Chainage', 'Easting', 'Northing', 'Elevation']  # Workbook headers
-VOLUME = 200  # Storm demand volume for plotting (m^3/m)
-FIGURE_DIR = 'png'  # Save profile plots here
-
-# Define block and profile IDs
-with open('settings.json', 'r') as f:
-    DATA = json.loads(f.read())
-
-print('.', end='', flush=True)  # Show progress
-
-# Load data
-xlsx_path = 'Profiles 1 to 12 2019 DEM.xlsx'
-workbook = pd.ExcelFile(xlsx_path)
-sheets = [s for s in workbook.sheet_names if s[0].isdigit()][::-1]
-
-# Create output dataframs
-zsa = pd.DataFrame(index=[[], [], []])
-zrfc = pd.DataFrame(index=[[], [], []])
-zsa.index = zsa.index.set_names(['beach', 'block', 'profile'])
-zrfc.index = zrfc.index.set_names(['beach', 'block', 'profile'])
-
-for s in sheets:
-    print('.', end='', flush=True)  # Show progress
-
-    # Get block and profile IDs
-    block = DATA[s].pop('block')
-    profile = DATA[s].pop('profile')
-
-    # Get additional keyword arguments for Nielsen calculations
-    kwargs = DATA[s]
-
-    # Read current sheet in workbook
-    df = workbook.parse(s, names=COLUMNS)
-
-    # Extract profile coordinates
-    g = Gridder(chainage=df['Chainage'],
-                elevation=df['Elevation'],
-                easting=df['Easting'],
-                northing=df['Northing'],
-                **kwargs)
-
-    # Update recession tables
-    zsa.loc[(BEACH, block, profile), g.volume] = g.chainage['zsa']
-    zrfc.loc[(BEACH, block, profile), g.volume] = g.chainage['zrfc']
-
-    # Plot profiles
-    fig, ax = plt.subplots(1, 1, figsize=(12, 4))
-
-    p = BeachProfile(df['Chainage'], df['Elevation'])
-    p.plot(v=VOLUME, ax=ax, title=f'Roches Beach, P{s}')
-
-    ax.spines['top'].set_visible(False)
-    ax.spines['right'].set_visible(False)
-
-    png_path = os.path.join(FIGURE_DIR, f'P{s}')
-    plt.savefig(png_path, bbox_inches='tight', dpi=300)
-
-# Write tables
-zsa.to_csv('recession_results_zsa.csv', float_format='%0.3f')
-zrfc.to_csv('recession_results_zrfc.csv', float_format='%0.3f')
-
-print('\nFinished.')

lidar/settings.json

@@ -1,72 +0,0 @@
-{
-  "1": {
-    "block": 1,
-    "profile": 1,
-    "z_swash": 2
-  },
-  "2": {
-    "block": 2,
-    "profile": 1,
-    "z_swash": 2
-  },
-  "3": {
-    "block": 3,
-    "profile": 1,
-    "z_swash": 2
-  },
-  "4s": {
-    "block": 4,
-    "profile": 1,
-    "z_swash": 2
-  },
-  "4": {
-    "block": 4,
-    "profile": 2,
-    "z_swash": 2
-  },
-  "4n": {
-    "block": 4,
-    "profile": 3,
-    "z_swash": 2
-  },
-  "5": {
-    "block": 5,
-    "profile": 1,
-    "z_swash": 2
-  },
-  "6": {
-    "block": 6,
-    "profile": 1,
-    "z_swash": 2
-  },
-  "7": {
-    "block": 7,
-    "profile": 1,
-    "z_swash": 2
-  },
-  "8": {
-    "block": 8,
-    "profile": 1,
-    "z_swash": 2
-  },
-  "9": {
-    "block": 9,
-    "profile": 1,
-    "z_swash": 2
-  },
-  "10": {
-    "block": 10,
-    "profile": 1,
-    "z_swash": 2
-  },
-  "11": {
-    "block": 11,
-    "profile": 1,
-    "z_swash": 2
-  },
-  "12": {
-    "block": 12,
-    "profile": 1,
-    "z_swash": 2
-  }
-}

probabilistic-analysis/probabilistic_assessment.py

@@ -488,7 +488,7 @@ def process(beach_name, beach_scenario, n_runs, start_year, end_year,
                 for b, p, in zip(segment_gaps['block'],
                                  segment_gaps['profile']):
                     idx = (df_out['block'] == str(b)) & (df_out['profile']
-                                                         == p)
+                                                              == p)
                     df_out.loc[idx, 'segment_gaps'] = True
 
                 # Specify which profiles to plot