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.
299 lines
11 KiB
Python
299 lines
11 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 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 plot_profiles(profile_info, profile, output_loc, LL_limit):
|
|
#plot the profile. expects output from CC_split_profile
|
|
|
|
YminorLocator=MultipleLocator(0.5)
|
|
XminorLocator=MultipleLocator(5)
|
|
|
|
|
|
fig,ax=plt.subplots(figsize=(8, 3))
|
|
|
|
num_plots=len(profile_info.keys())-1
|
|
colormap = plt.cm.jet
|
|
ax.set_prop_cycle(cycler('color', [colormap(i) for i in np.linspace(0, 0.9, num_plots)]))
|
|
|
|
max_y=0
|
|
for date in profile_info.keys():
|
|
if date!='info':
|
|
|
|
plt.plot(profile_info[date]['Chainage'], profile_info[date]['Elevation'], label=date)
|
|
try:
|
|
if max([i for i in profile_info[date]['Elevation'] if pd.isnull(i)==False])>max_y:
|
|
max_y=max([i for i in profile_info[date]['Elevation'] if pd.isnull(i)==False])
|
|
except:
|
|
print("empty elevation section for %s" % date)
|
|
|
|
|
|
plt.plot([LL_limit,LL_limit], [-1,max_y], 'r--', alpha=0.5, label="Landward Limit")
|
|
|
|
|
|
plt.xlabel('Chainage (m)',weight='bold')
|
|
plt.ylabel('Elevation (m AHD)',weight='bold')
|
|
plt.legend(loc='upper right', bbox_to_anchor=(1.3,1))
|
|
plt.title(profile)
|
|
plt.rcParams['font.size']=8
|
|
|
|
ax.set_ylim([-1,math.ceil(max_y)])
|
|
|
|
ax.xaxis.set_minor_locator(XminorLocator)
|
|
ax.yaxis.set_minor_locator(YminorLocator)
|
|
ax.xaxis.grid(True, which='minor', color='k', linestyle='-', alpha=0.3)
|
|
ax.yaxis.grid(True,which='minor',color='k', linestyle='-', alpha=0.3)
|
|
|
|
plt.grid(which='major', color='k', linestyle='-')
|
|
today=datetime.datetime.now().date().strftime('%Y%m%d')
|
|
plt.savefig(os.path.join(output_loc, '%s_%s.png' % (today, profile)),bbox_inches='tight',dpi=900)
|
|
plt.clf()
|
|
|
|
return None
|
|
|
|
|
|
def CC_split_profile(file2read):
|
|
# this reads the profile files and splits it into dates
|
|
|
|
file_master=pd.read_csv(file2read)
|
|
|
|
beach_original=file_master['Beach'].tolist()
|
|
profile_original=file_master['Profile'].tolist()
|
|
date_original=file_master['Date'].tolist()
|
|
chainage_original=file_master['Chainage'].tolist()
|
|
elevation_original=file_master['Elevation'].tolist()
|
|
easting_original=file_master['Easting'].tolist()
|
|
northing_original=file_master['Northing'].tolist()
|
|
|
|
data={}
|
|
i=0
|
|
|
|
#add info on the beach and profile number
|
|
data['info']={'Profile':profile_original[0], 'Beach':beach_original[0]}
|
|
date_now=date_original[0]
|
|
|
|
while i<len(file_master):
|
|
chainage_tmp=[]
|
|
elevation_tmp=[]
|
|
easting_tmp=[]
|
|
northing_tmp=[]
|
|
while i<len(file_master) and date_now==date_original[i]:
|
|
chainage_tmp.append(chainage_original[i])
|
|
elevation_tmp.append(elevation_original[i])
|
|
easting_tmp.append(easting_original[i])
|
|
northing_tmp.append(northing_original[i])
|
|
i=i+1
|
|
|
|
data[date_now]={'Beach': beach_original[i-1], 'Profile':profile_original[i-1],'Easting': easting_tmp, 'Northing':northing_tmp, 'Elevation':elevation_tmp, 'Chainage':chainage_tmp}
|
|
|
|
if i<len(file_master):
|
|
date_now=date_original[i]
|
|
|
|
return data
|
|
|
|
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
|
|
|
|
input_file = 'Parameter Files/las-manipulation-survey-2.xlsx'
|
|
params_file=pd.read_excel(input_file, sheet_name="PARAMS")
|
|
|
|
for i in range(0, len(params_file)): #0, len(params_file)
|
|
print("Starting to process %s" % params_file['Beach'][i])
|
|
beach=params_file['Beach'][i]
|
|
survey_date=params_file['SURVEY DATE'][i]
|
|
original_las=params_file['INPUT LAS'][i]
|
|
classified_las_dir=params_file['LAS CLASSIFIED FOLDER'][i]
|
|
shp_swash_dir=params_file['SHP SWASH FOLDER'][i]
|
|
crop_heatmap_poly=params_file['HEATMAP CROP POLY'][i]
|
|
output_las_dir=params_file['LAS OUTPUT FOLDER'][i]
|
|
zone_MGA=params_file['ZONE MGA'][i]
|
|
output_poly_dir=params_file['SHP RASTER FOLDER'][i]
|
|
output_tif_dir=params_file['TIF OUTPUT FOLDER'][i]
|
|
cp_csv=params_file['INPUT CSV'][i]
|
|
profile_limit_file=params_file['PROFILE LIMIT FILE'][i]
|
|
csv_output_dir=params_file['CSV OUTPUT FOLDER'][i]
|
|
graph_loc = params_file['PNG OUTPUT FOLDER'][i]
|
|
volume_output=params_file['CSV VOLUMES FOLDER'][i]
|
|
tmp_dir=params_file['TMP FOLDER'][i]
|
|
|
|
# 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)
|
|
# crop_las(input_las5, crop_swash_poly, final_las, path_2_lastools)
|
|
|
|
# Apply sea-side clipping polygon
|
|
las_data = call_lastools('lasclip', input=las_data, output='-stdout',
|
|
args=['-poly', crop_heatmap_poly], verbose=False)
|
|
# crop_las(final_las, heatmap_crop_poly, heatmap_las, path_2_lastools)
|
|
|
|
# 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)
|
|
# las_boundary(heatmap_las, output_poly_name, output_poly_dir, path_2_lastools, zone_MGA)
|
|
|
|
#make a raster
|
|
# make_raster(heatmap_las, output_raster, path_2_lastools, keep_only_ground=True)
|
|
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 the points and get volumes
|
|
df = extract_pts(
|
|
las_data,
|
|
cp_csv,
|
|
survey_date,
|
|
beach,
|
|
args=['-parse', 'sxyz', '-keep_class', '2'],
|
|
verbose=False)
|
|
update_survey_output(df, csv_output_dir)
|
|
|
|
|
|
|
|
#colourise the point cloud
|
|
|
|
|
|
#delete the temp files from the tmp_dir and the interim_dir
|
|
remove_temp_files(tmp_dir)
|
|
#remove_temp_files(int_dir)
|
|
|
|
csv_names = [f for f in os.listdir(csv_output_dir) if f.endswith('.csv')]
|
|
ch_limits = pd.read_excel(profile_limit_file, index_col='Profile')
|
|
|
|
for csv_name in csv_names:
|
|
profile_name = os.path.splitext(csv_name)[0]
|
|
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')
|
|
|
|
# Remove landward section of profiles (behind beach)
|
|
ch_min = ch_limits.loc[profile_name, 'Landward Limit']
|
|
# profiles = profiles.loc[ch_min:]
|
|
|
|
profiles.plot(title=profile_name)
|
|
plt.show()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
print("doing the volume analysis")
|
|
# test=profile_plots_volume(csv_output_dir, profile_limit_file, volume_output, graph_loc)
|