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.
258 lines
9.2 KiB
Python
258 lines
9.2 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 subprocess
|
|
import pandas as pd
|
|
import numpy as np
|
|
import neilson_volumes
|
|
import matplotlib.pyplot as plt
|
|
from matplotlib.ticker import MultipleLocator
|
|
import datetime
|
|
import xlsxwriter
|
|
import math
|
|
from cycler import cycler
|
|
|
|
from survey_tools import call_lastools, extract_pts, update_survey_output
|
|
|
|
|
|
def profile_plots_volume(csv_loc, LL_xlsx, output_xlsx, graph_location):
|
|
#get a list of all csvs which will each be analysed
|
|
file_list=[]
|
|
for file in os.listdir(csv_loc):
|
|
if file.endswith(".csv"):
|
|
file_list.append(os.path.join(csv_loc, file))
|
|
|
|
#now read the LL file
|
|
LL_limit_file=pd.read_excel(LL_xlsx, 'profile_locations')
|
|
LL_info={}
|
|
for i in range(0, len(LL_limit_file)):
|
|
#make a dictionary that alllows you to search the LL
|
|
prof="%s_%s" % (LL_limit_file['Profile'][i].split(" ")[0], LL_limit_file['Profile'][i].split(" ")[-1])
|
|
|
|
LL_info[prof]=LL_limit_file['Landward Limit'][i]
|
|
|
|
all_dates=[]
|
|
results_volume={}
|
|
for file in file_list:
|
|
#read the profile data - this should have all dates
|
|
profile_data=CC_split_profile(file)
|
|
profile=profile_data['info']['Profile']
|
|
|
|
#plot all of the profiles
|
|
print(profile)
|
|
plot_profiles(profile_data, profile, graph_location,LL_info[profile])
|
|
|
|
results_volume[profile]={}
|
|
|
|
#nowgo through each date and do a neilson volume calculations
|
|
for date in profile_data.keys():
|
|
if date!='info':
|
|
if date not in all_dates:
|
|
all_dates.append(date)
|
|
|
|
chainage=profile_data[date]['Chainage']
|
|
elevation=[0 if pd.isnull(profile_data[date]['Elevation'][i]) else profile_data[date]['Elevation'][i] for i in range(0, len(profile_data[date]['Elevation']))]
|
|
LL_limit=LL_info[profile]
|
|
#do a neilson calculation to get the ZSA volume
|
|
if len(elevation)>2:
|
|
#if there aren't enough available points don't do it
|
|
volume=neilson_volumes.volume_available(chainage, elevation, LL_limit)
|
|
if volume<0:
|
|
volume=0
|
|
print('%s %s has a negative volume available' % (profile, date))
|
|
else:
|
|
volume=0
|
|
|
|
results_volume[profile][date]=volume
|
|
|
|
#write an excel sheet which summarises the data
|
|
workbook = xlsxwriter.Workbook(output_xlsx)
|
|
worksheet=workbook.add_worksheet('Volumes')
|
|
|
|
row=0
|
|
col=0
|
|
|
|
worksheet.write(row, col, 'Profile')
|
|
for date in all_dates:
|
|
col=col+1
|
|
worksheet.write(row, col, date)
|
|
|
|
col=0
|
|
row=1
|
|
|
|
for prof in results_volume.keys():
|
|
|
|
worksheet.write(row, col, prof)
|
|
|
|
for date in all_dates:
|
|
col=col+1
|
|
try:
|
|
vol=results_volume[prof][date]
|
|
except KeyError:
|
|
print("error with profile %s on %s" % (prof, date))
|
|
vol=None
|
|
worksheet.write(row, col, vol)
|
|
col=0
|
|
row=row+1
|
|
return results_volume
|
|
|
|
|
|
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, survey_date, csv_output_dir, graph_loc, ch_limits):
|
|
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')
|
|
|
|
# Find landward limit of profile (behind beach)
|
|
ch_min = ch_limits.loc[profile_name, 'Landward Limit']
|
|
|
|
ax = plt.axes()
|
|
for col in profiles.columns:
|
|
profile = profiles.loc[ch_min:, col]
|
|
date_str = str(survey_date)
|
|
date = '{}-{}-{}'.format(date_str[:4], date_str[4:6], date_str[6:])
|
|
|
|
ax.plot(profile.index, profile, label=date)
|
|
|
|
ax.set_xlabel('Chainage (m)', labelpad=10)
|
|
ax.set_ylabel('Elevation (m AHD)', labelpad=10)
|
|
ax.legend(frameon=False, 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)
|
|
|
|
png_name = os.path.join(graph_loc, 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:
|
|
volumes = pd.DataFrame()
|
|
|
|
# Format dates
|
|
date_str = str(survey_date)
|
|
date = '{}-{}-{}'.format(date_str[:4], date_str[4:6], date_str[6:])
|
|
|
|
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 = neilson_volumes.volume_available(chainage, elevation, ch_min)
|
|
|
|
# Update spreadsheet
|
|
volumes.loc[profile_name, date] = volume
|
|
|
|
# Save updated volumes spreadsheet
|
|
volumes = volumes.sort_index()
|
|
volumes.to_csv(csv_vol)
|
|
|
|
|
|
input_file = 'Parameter Files/las-manipulation-survey-2.xlsx'
|
|
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 = 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
|
|
las_data = call_lastools('lasclip', input=input_las, output='-stdout',
|
|
args=['-poly', crop_swash_poly], verbose=False)
|
|
|
|
# Apply sea-side clipping polygon
|
|
las_data = call_lastools('lasclip', input=las_data, output='-stdout',
|
|
args=['-poly', crop_heatmap_poly], verbose=False)
|
|
|
|
# Create clipping polygon for heatmap raster
|
|
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
|
|
tif_name = os.path.join(output_tif_dir, las_basename + '.tif')
|
|
call_lastools('blast2dem', input=las_data, output=tif_name,
|
|
args=['-step', 0.2], verbose=False)
|
|
|
|
# Extract elevations along profiles from triangulated surface
|
|
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
|
|
profile_names = df['Profile'].unique()
|
|
for profile_name in profile_names:
|
|
plot_profiles(profile_name, survey_date, csv_output_dir, graph_loc, ch_limits)
|
|
calculate_volumes(profile_name, survey_date, csv_output_dir, ch_limits, volume_output_dir)
|
|
|
|
# Remove temprary files
|
|
remove_temp_files(tmp_dir)
|