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.
269 lines
9.4 KiB
Python
269 lines
9.4 KiB
Python
# python 3.5
|
|
#requires LAStools to be installed (with the appropriate license). Note that LAStools requires no spaces in file names
|
|
|
|
#should have previously run 2017088_las_manipulation to have a las that has the buildings and veg removed
|
|
#note that the neilson volumes script must be in the same folder
|
|
|
|
# this script will:
|
|
#crop to a given polygon (crop away the swash zone)
|
|
# extract values along a predefined profile,
|
|
# do the volume analysis
|
|
#export pngs of the surveys
|
|
########################### IMPORTS ###########################################
|
|
import os
|
|
import io
|
|
import re
|
|
import sys
|
|
import math
|
|
import argparse
|
|
import datetime
|
|
import subprocess
|
|
import numpy as np
|
|
import pandas as pd
|
|
from cycler import cycler
|
|
import matplotlib.pyplot as plt
|
|
from matplotlib.ticker import MultipleLocator
|
|
|
|
import nielsen_volumes
|
|
from survey_tools import call_lastools, extract_pts, update_survey_output
|
|
|
|
|
|
def get_datestring(x):
|
|
"""Format a date integer into an ISO-8601 date string
|
|
|
|
Args:
|
|
x: unformatted date
|
|
|
|
Returns:
|
|
formatted date string
|
|
|
|
Examples:
|
|
|
|
>> get_datestring(19700101)
|
|
'1970-01-01'
|
|
"""
|
|
datestr = pd.datetime.strptime(str(x), '%Y%m%d').strftime('%Y-%m-%d')
|
|
|
|
return datestr
|
|
|
|
|
|
def remove_temp_files(directory):
|
|
for f in os.listdir(directory):
|
|
os.unlink(os.path.join(directory, f))
|
|
|
|
return None
|
|
|
|
|
|
def plot_profiles(profile_name, csv_output_dir, graph_loc, ch_limits, delta_vol, survey_date):
|
|
csv_name = profile_name + '.csv'
|
|
profiles = pd.read_csv(os.path.join(csv_output_dir, csv_name))
|
|
|
|
# Remove metadata, and extract profile coordinates
|
|
profiles = profiles.loc[:, 'Chainage':].set_index('Chainage')
|
|
|
|
# Determine if current survey is the latest
|
|
current_survey_col = 'Elevation_' + survey_date
|
|
is_latest = profiles.columns[-1] == current_survey_col
|
|
|
|
# Only plot profiles up to current survey date
|
|
profiles = profiles.loc[:, :current_survey_col]
|
|
|
|
# Find landward limit of profile (behind beach)
|
|
ch_min = ch_limits.loc[profile_name, 'Landward Limit']
|
|
|
|
# Set figure dimensions based on beach size
|
|
vertical_exag = 8
|
|
m_per_inch = 8
|
|
fig_h = profiles.dropna().values.max() / m_per_inch * vertical_exag
|
|
fig_w = (profiles.index.max() - ch_min) / m_per_inch
|
|
|
|
fig, ax = plt.subplots(figsize=(fig_w, fig_h))
|
|
for col in profiles.columns:
|
|
profile = profiles.loc[ch_min:, col]
|
|
date_str = col.split('_')[-1]
|
|
date = get_datestring(date_str)
|
|
|
|
ax.plot(profile.index, profile, label=date)
|
|
|
|
ax.set_aspect(vertical_exag)
|
|
|
|
ax.set_xlabel('Chainage (m)', labelpad=10)
|
|
ax.set_ylabel('Elevation (m AHD)', labelpad=10)
|
|
|
|
# Show most recent volume change
|
|
if delta_vol is not None:
|
|
ax.annotate('Most recent\nvolume change:\n{:0.1f} m$^3$/m'.format(delta_vol),
|
|
(0.05, 0.15), xycoords='axes fraction', fontsize=9,
|
|
backgroundcolor='#ffffff', linespacing=1.5)
|
|
|
|
ax.legend(edgecolor='none', facecolor='#ffffff', fontsize=9)
|
|
|
|
ax.xaxis.set_minor_locator(MultipleLocator(5))
|
|
ax.yaxis.set_minor_locator(MultipleLocator(0.5))
|
|
ax.xaxis.grid(True, which='minor', color='k', linewidth=0.5, alpha=0.3)
|
|
ax.yaxis.grid(True,which='minor',color='k', linewidth=0.5, alpha=0.3)
|
|
|
|
# Save in folder with current date
|
|
png_dirs = [os.path.join(graph_loc, get_datestring(survey_date))]
|
|
if is_latest:
|
|
# Save copy in'latest' if survey is most recent
|
|
png_dirs += [os.path.join(graph_loc, 'latest')]
|
|
|
|
for png_dir in png_dirs:
|
|
# Prepare output directory
|
|
try:
|
|
os.makedirs(png_dir)
|
|
except FileExistsError:
|
|
pass
|
|
|
|
png_name = os.path.join(png_dir, profile_name + '.png')
|
|
plt.savefig(png_name, bbox_inches='tight', dpi=300)
|
|
plt.close()
|
|
|
|
|
|
def calculate_volumes(profile_name, survey_date, csv_output_dir, ch_limits, volume_output_dir):
|
|
csv_prof = profile_name + '.csv'
|
|
beach = re.search('.*(?=_\d)', profile_name).group()
|
|
profiles = pd.read_csv(os.path.join(csv_output_dir, csv_prof))
|
|
|
|
# Remove metadata, and extract profile coordinates
|
|
profiles = profiles.loc[:, 'Chainage':].set_index('Chainage')
|
|
|
|
# Find landward limit of profile (behind beach)
|
|
ch_min = ch_limits.loc[profile_name, 'Landward Limit']
|
|
|
|
# Open volume spreadsheet
|
|
csv_vol = os.path.join(volume_output_dir, 'volumes.csv')
|
|
try:
|
|
volumes = pd.read_csv(csv_vol, index_col=0)
|
|
except FileNotFoundError:
|
|
# Create new dataframe if csv does not exist
|
|
volumes = pd.DataFrame()
|
|
|
|
for current_date in profiles.columns:
|
|
# Get Nielsen erosion volumes
|
|
chainage = profiles.loc[:, current_date].dropna().index
|
|
elevation = profiles.loc[:, current_date].dropna().values
|
|
volume = nielsen_volumes.volume_available(chainage, elevation, ch_min)
|
|
|
|
# Update spreadsheet
|
|
volumes.loc[profile_name, 'Volume_' + survey_date] = volume
|
|
|
|
# Save updated volumes spreadsheet
|
|
volumes = volumes[volumes.columns.sort_values()]
|
|
volumes = volumes.sort_index()
|
|
volumes.to_csv(csv_vol)
|
|
|
|
# Get most recent volume difference for current profile
|
|
try:
|
|
previous_vol = volumes.loc[profile_name].values[-2]
|
|
current_vol = volumes.loc[profile_name].values[-1]
|
|
delta_vol = current_vol - previous_vol
|
|
except IndexError:
|
|
# Return None if there is only one survey
|
|
delta_vol = None
|
|
|
|
return delta_vol
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
'input_file',
|
|
metavar='PARAMS_FILE',
|
|
help='name of parameter file',
|
|
default=None)
|
|
|
|
# Print usage if no arguments are provided
|
|
if len(sys.argv) == 1:
|
|
parser.print_help(sys.stderr)
|
|
sys.exit(1)
|
|
|
|
args = parser.parse_args()
|
|
|
|
# read the parameters file and scroll through it
|
|
input_file = args.input_file
|
|
params_file=pd.read_excel(input_file, sheet_name="PARAMS")
|
|
|
|
for i, row in params_file.iterrows():
|
|
print("Starting to process %s" % row['BEACH'])
|
|
beach=row['BEACH']
|
|
survey_date = str(row['SURVEY DATE'])
|
|
original_las = row['INPUT LAS']
|
|
classified_las_dir = row['LAS CLASSIFIED FOLDER']
|
|
shp_swash_dir = row['SHP SWASH FOLDER']
|
|
crop_heatmap_poly = row['HEATMAP CROP POLY']
|
|
output_las_dir = row['LAS OUTPUT FOLDER']
|
|
zone_MGA = row['ZONE MGA']
|
|
output_poly_dir = row['SHP RASTER FOLDER']
|
|
output_tif_dir = row['TIF OUTPUT FOLDER']
|
|
cp_csv = row['INPUT CSV']
|
|
profile_limit_file = row['PROFILE LIMIT FILE']
|
|
csv_output_dir = row['CSV OUTPUT FOLDER']
|
|
graph_loc = row['PNG OUTPUT FOLDER']
|
|
volume_output_dir = row['CSV VOLUMES FOLDER']
|
|
tmp_dir = row['TMP FOLDER']
|
|
|
|
# Get base name of input las
|
|
las_basename = os.path.splitext(os.path.basename(original_las))[0]
|
|
|
|
# Get name of input point cloud
|
|
input_las = os.path.join(classified_las_dir, las_basename + '.las')
|
|
|
|
# Get name of swash cropping polygon
|
|
crop_swash_poly = os.path.join(shp_swash_dir, las_basename + '.shp')
|
|
|
|
# Crop point cloud to swash boundary
|
|
print('Cropping swash...')
|
|
las_data = call_lastools('lasclip', input=input_las, output='-stdout',
|
|
args=['-poly', crop_swash_poly], verbose=False)
|
|
|
|
# Apply sea-side clipping polygon
|
|
print('Cropping back of beach...')
|
|
las_data = call_lastools('lasclip', input=las_data, output='-stdout',
|
|
args=['-poly', crop_heatmap_poly], verbose=False)
|
|
|
|
# Create clipping polygon for heatmap raster
|
|
print('Creating heat map cropping polygon...')
|
|
shp_name = os.path.join(output_poly_dir, las_basename + '.shp')
|
|
call_lastools('lasboundary', input=las_data, output=shp_name, verbose=False)
|
|
|
|
# Make a raster from point cloud
|
|
print('Creating heat map raster...')
|
|
tif_name = os.path.join(output_tif_dir, las_basename + '.tif')
|
|
call_lastools('las2dem', input=las_data, output=tif_name,
|
|
args=['-step', 0.2, '-keep_class', 2], verbose=False)
|
|
|
|
# Extract elevations along profiles from triangulated surface
|
|
print('Extracting profile elevations...')
|
|
df = extract_pts(
|
|
las_data,
|
|
cp_csv,
|
|
survey_date,
|
|
beach,
|
|
args=['-parse', 'sxyz', '-keep_class', '2'],
|
|
verbose=False)
|
|
|
|
# Update survey profiles
|
|
update_survey_output(df, csv_output_dir)
|
|
|
|
# Get landward limit of surveys
|
|
ch_limits = pd.read_excel(profile_limit_file, index_col='Profile')
|
|
|
|
# Plot profiles, and save sand volumes for current beach
|
|
print('Updating figures...')
|
|
profile_names = df['Profile'].unique()
|
|
for profile_name in profile_names:
|
|
delta_vol = calculate_volumes(profile_name, survey_date,
|
|
csv_output_dir, ch_limits,
|
|
volume_output_dir)
|
|
plot_profiles(profile_name, csv_output_dir, graph_loc, ch_limits,
|
|
delta_vol, survey_date)
|
|
|
|
# Remove temprary files
|
|
remove_temp_files(tmp_dir)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|