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