Add 'csv_to_shp.py'

README.md

@@ -9,6 +9,9 @@ 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

@@ -0,0 +1,95 @@
+# 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')