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203 lines
7.3 KiB
Python
203 lines
7.3 KiB
Python
# python 3.5
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#requires LAStools to be installed (with the appropriate license). Note that LAStools requires no spaces in file names
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#should have previously run 2017088_las_manipulation to have a las that has the buildings and veg removed
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#note that the neilson volumes script must be in the same folder
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# this script will:
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#crop to a given polygon (crop away the swash zone)
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# extract values along a predefined profile,
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# do the volume analysis
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#export pngs of the surveys
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########################### IMPORTS ###########################################
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import os
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import io
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import re
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import sys
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import math
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import argparse
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import datetime
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import subprocess
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import numpy as np
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import pandas as pd
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from cycler import cycler
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import matplotlib.pyplot as plt
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from matplotlib.ticker import MultipleLocator
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import nielsen_volumes
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from survey_tools import call_lastools, extract_pts, update_survey_output
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def remove_temp_files(directory):
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for f in os.listdir(directory):
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os.unlink(os.path.join(directory, f))
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return None
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def plot_profiles(profile_name, survey_date, csv_output_dir, graph_loc, ch_limits):
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csv_name = profile_name + '.csv'
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profiles = pd.read_csv(os.path.join(csv_output_dir, csv_name))
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# Remove metadata, and extract profile coordinates
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profiles = profiles.loc[:, 'Chainage':].set_index('Chainage')
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# Find landward limit of profile (behind beach)
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ch_min = ch_limits.loc[profile_name, 'Landward Limit']
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ax = plt.axes()
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for col in profiles.columns:
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profile = profiles.loc[ch_min:, col]
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date_str = str(survey_date)
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date = '{}-{}-{}'.format(date_str[:4], date_str[4:6], date_str[6:])
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ax.plot(profile.index, profile, label=date)
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ax.set_xlabel('Chainage (m)', labelpad=10)
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ax.set_ylabel('Elevation (m AHD)', labelpad=10)
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ax.legend(frameon=False, fontsize=9)
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ax.xaxis.set_minor_locator(MultipleLocator(5))
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ax.yaxis.set_minor_locator(MultipleLocator(0.5))
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ax.xaxis.grid(True, which='minor', color='k', linewidth=0.5, alpha=0.3)
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ax.yaxis.grid(True,which='minor',color='k', linewidth=0.5, alpha=0.3)
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png_name = os.path.join(graph_loc, profile_name + '.png')
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plt.savefig(png_name, bbox_inches='tight', dpi=300)
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plt.close()
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def calculate_volumes(profile_name, survey_date, csv_output_dir, ch_limits, volume_output_dir):
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csv_prof = profile_name + '.csv'
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beach = re.search('.*(?=_\d)', profile_name).group()
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profiles = pd.read_csv(os.path.join(csv_output_dir, csv_prof))
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# Remove metadata, and extract profile coordinates
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profiles = profiles.loc[:, 'Chainage':].set_index('Chainage')
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# Find landward limit of profile (behind beach)
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ch_min = ch_limits.loc[profile_name, 'Landward Limit']
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# Open volume spreadsheet
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csv_vol = os.path.join(volume_output_dir, 'volumes.csv')
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try:
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volumes = pd.read_csv(csv_vol, index_col=0)
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except FileNotFoundError:
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volumes = pd.DataFrame()
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# Format dates
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date_str = str(survey_date)
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date = '{}-{}-{}'.format(date_str[:4], date_str[4:6], date_str[6:])
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for current_date in profiles.columns:
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# Get Nielsen erosion volumes
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chainage = profiles.loc[:, current_date].dropna().index
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elevation = profiles.loc[:, current_date].dropna().values
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volume = nielsen_volumes.volume_available(chainage, elevation, ch_min)
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# Update spreadsheet
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volumes.loc[profile_name, date] = volume
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# Save updated volumes spreadsheet
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volumes = volumes.sort_index()
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volumes.to_csv(csv_vol)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'input_file',
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metavar='PARAMS_FILE',
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help='name of parameter file',
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default=None)
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# Print usage if no arguments are provided
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if len(sys.argv) == 1:
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parser.print_help(sys.stderr)
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sys.exit(1)
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args = parser.parse_args()
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# read the parameters file and scroll through it
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input_file = args.input_file
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params_file=pd.read_excel(input_file, sheet_name="PARAMS")
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for i, row in params_file.iterrows():
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print("Starting to process %s" % row['Beach'])
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beach=row['Beach']
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survey_date = row['SURVEY DATE']
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original_las = row['INPUT LAS']
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classified_las_dir = row['LAS CLASSIFIED FOLDER']
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shp_swash_dir = row['SHP SWASH FOLDER']
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crop_heatmap_poly = row['HEATMAP CROP POLY']
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output_las_dir = row['LAS OUTPUT FOLDER']
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zone_MGA = row['ZONE MGA']
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output_poly_dir = row['SHP RASTER FOLDER']
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output_tif_dir = row['TIF OUTPUT FOLDER']
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cp_csv = row['INPUT CSV']
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profile_limit_file = row['PROFILE LIMIT FILE']
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csv_output_dir = row['CSV OUTPUT FOLDER']
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graph_loc = row['PNG OUTPUT FOLDER']
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volume_output_dir = row['CSV VOLUMES FOLDER']
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tmp_dir = row['TMP FOLDER']
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# Get base name of input las
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las_basename = os.path.splitext(os.path.basename(original_las))[0]
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# Get name of input point cloud
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input_las = os.path.join(classified_las_dir, las_basename + '.las')
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# Get name of swash cropping polygon
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crop_swash_poly = os.path.join(shp_swash_dir, las_basename + '.shp')
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# Crop point cloud to swash boundary
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print('Cropping swash...')
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las_data = call_lastools('lasclip', input=input_las, output='-stdout',
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args=['-poly', crop_swash_poly], verbose=False)
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# Apply sea-side clipping polygon
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print('Cropping back of beach...')
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las_data = call_lastools('lasclip', input=las_data, output='-stdout',
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args=['-poly', crop_heatmap_poly], verbose=False)
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# Create clipping polygon for heatmap raster
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print('Creating heat map cropping polygon...')
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shp_name = os.path.join(output_poly_dir, las_basename + '.shp')
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call_lastools('lasboundary', input=las_data, output=shp_name, verbose=False)
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# Make a raster from point cloud
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print('Creating heat map raster...')
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tif_name = os.path.join(output_tif_dir, las_basename + '.tif')
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call_lastools('blast2dem', input=las_data, output=tif_name,
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args=['-step', 0.2], verbose=False)
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# Extract elevations along profiles from triangulated surface
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print('Extracting profile elevations...')
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df = extract_pts(
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las_data,
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cp_csv,
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survey_date,
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beach,
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args=['-parse', 'sxyz', '-keep_class', '2'],
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verbose=False)
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# Update survey profiles
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update_survey_output(df, csv_output_dir)
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# Get landward limit of surveys
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ch_limits = pd.read_excel(profile_limit_file, index_col='Profile')
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# Plot profiles, and save sand volumes for current beach
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print('Updating figures...')
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profile_names = df['Profile'].unique()
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for profile_name in profile_names:
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plot_profiles(profile_name, survey_date, csv_output_dir, graph_loc, ch_limits)
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calculate_volumes(profile_name, survey_date, csv_output_dir, ch_limits, volume_output_dir)
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# Remove temprary files
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remove_temp_files(tmp_dir)
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if __name__ == '__main__':
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main()
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