README.md

@@ -9,9 +9,6 @@ Double-click `anaconda-prompt.bat`
 
 #### 2. Generate ZSA and ZRFC recession tables.
 
-The package required to calculate setbacks based on Nielsen et al. (1992) can be found here:  
-http://git.wrl.unsw.edu.au:3000/coastal/nielsen
-
 ```shell
   > cd lidar  
   > python generate_recession_tables.py

probabilistic-analysis/csv_to_shp.py

@@ -1,95 +0,0 @@
-# Converts shoreline chainages to MGA coordinates
-
-import os
-import re
-import sys
-import ast
-import json
-import yaml
-import argparse
-import numpy as np
-from scipy import interpolate
-import pandas as pd
-import matplotlib.pyplot as plt
-from shapely.geometry import LineString
-import geopandas as gpd
-from tqdm import tqdm
-
-sys.path.insert(0, '../lidar/')
-from nielsen import Gridder  # noqa
-
-MGA55 = 28355  # GDA94, MGA Zone 55
-BEACH = 'Roches Beach'
-
-# Load profile data
-xlsx_path = '../lidar/Profiles 1 to 12 2019 DEM.xlsx'
-workbook = pd.ExcelFile(xlsx_path)
-
-with open('../lidar/settings.json', 'r') as f:
-    sheets = json.loads(f.read())
-
-# Create grids to convert chainages to eastings and northings
-grids = {}
-for s in sheets:
-    names = ['Chainage', 'Easting', 'Northing', 'Elevation']
-    p = workbook.parse(s, names=names)
-    k = (sheets[s]['block'], sheets[s]['profile'])
-    grids[k] = g = Gridder(chainage=p['Chainage'],
-                           elevation=p['Elevation'],
-                           easting=p['Easting'],
-                           northing=p['Northing'])
-
-input_dir = 'output_csv'
-output_dir = 'output_shp'
-
-master = gpd.GeoDataFrame(columns=['name', 'year', 'ep', 'type'])
-
-# Load probabilistic recession chainages
-file_list = [f for f in os.listdir(input_dir) if f.startswith(BEACH)]
-
-df = pd.DataFrame()
-for f in file_list:
-    info = re.search(r'(\d{4}) (Z\w+)', f).groups()
-    data = pd.read_csv(os.path.join(input_dir, f))
-
-    ep_cols = [c for c in data.columns if c.startswith('ep_')]
-    for i, d in data.iterrows():
-        row = {}
-        # row['beach'] = BEACH
-        row['block'] = d['block']
-        row['profile'] = d['profile']
-        row['year'] = int(info[0])
-        row['type'] = info[1]
-        for e in ep_cols:
-            row['ep'] = e[3:]
-            ch = d[e]
-            g = grids[d['block'], d['profile']]  # Get correct gridder
-            east, north = g.from_chainage(ch)
-            row['easting'] = east.round(3)
-            row['northing'] = north.round(3)
-            df = df.append(row, ignore_index=True)
-
-# Convert floats to int
-for col in ['block', 'profile', 'year']:
-    df[col] = df[col].astype(int)
-
-df = df.set_index(['year', 'type', 'ep', 'block', 'profile']).sort_index()
-years = df.index.unique(level='year')
-types = df.index.unique(level='type')
-eps = df.index.unique(level='ep')
-
-lines = []
-for y in years:
-    for t in types:
-        for e in eps:
-            line = {}
-            line['year'] = y
-            line['type'] = t
-            line['ep'] = str(e)
-            line['geometry'] = LineString(df.loc[y, t, e].to_numpy())
-            lines.append(line)
-
-gdf = gpd.GeoDataFrame(lines).set_geometry('geometry')
-gdf = gdf.set_crs(MGA55)
-gdf.to_file(os.path.join(output_dir, 'hazard-lines.shp'),
-            driver='ESRI Shapefile')