You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
89 lines
2.4 KiB
Python
89 lines
2.4 KiB
Python
"""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.')
|