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Author SHA1 Message Date
chrisd e9b95d3a85 Update 'las_outputs.py' 
Move legend in profile plots to RHS
 5 years ago
chrisd d22acb6e09 Update 'las_manipulation.py' 5 years ago
chrisd 5edc0b0aa6 Update 'README.md' 5 years ago
chrisd 3e484f9f2f Update 'README.md' 5 years ago
Dan Howe 7f046d84a5 Update readme 5 years ago
Dan Howe 00791e5317 Update docstring 5 years ago
Dan Howe f6b2f8f38e Update readme 5 years ago
Dan Howe e334ed888e Add polyline_to_points.py 5 years ago
Dan Howe cfba2b500a Merge branch 'master' of http://git.wrl.unsw.edu.au:3000/danh/central-coast-aerial-survey 5 years ago
Dan Howe 793aad14c3 Change figure sizes 5 years ago
Dan Howe 4a31ae3f34 Add 'heatmap' or 'DEM' suffix to output tif files 5 years ago
chrisd e59f3aedd1 Update 'README.md' 
Added extract_contours to readme
 5 years ago
chrisd 3220cf60ad Update 'README.md' 5 years ago
chrisd 993296cef8 Update 'README.md' 5 years ago
chrisd 82f22df890 Update 'README.md' 5 years ago
7 changed files with 922 additions and 786 deletions
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28
README.md
Unescape Escape View File

@ -63,7 +63,7 @@ python las_outputs.py param-files/survey-1*.yaml param-files/survey-2*.yaml
Example usage: Example usage:
``` ```
C:/Python27/ArcGIS10.4/python generate_heatmaps.py param-files/survey-2*.yaml C:\Python27\ArcGIS10.5\python generate_heatmaps.py param-files\survey-2*.yaml
``` ```
@ -76,5 +76,29 @@ C:/Python27/ArcGIS10.4/python generate_heatmaps.py param-files/survey-2*.yaml
Example usage: Example usage:
``` ```
C:/Python27/ArcGIS10.4/python plot_heatmaps.py param-files/survey-2-avoca.yaml C:\Python27\ArcGIS10.5\python plot_heatmaps.py param-files\survey-2-avoca.yaml
```
### 7. Export 0.7 m contour for DSAS trend analysis
`extract_contours.py` extracts a predefined contour (say 0.7 m AHD) and outputs it in a format suitable for DSAS shoreline trend analysis.
Example usage:
```
python extract_contours.py
```
## polyline_to_points.py
This script interpolates points along transects in a shapefile.
Example usage:
```
# Extract points at default spacing (1m)
$ python polyline_to_points.py path/to/shp
# Extract points at 5m increments
$ python polyline_to_points.py -s 5 path/to/shp
# Use profile names from field "ProfileID" in the attribute table at 1 m spacing
$ python polyline_to_points.py -f ProfileID -s 1 path/to/shp
``` ```

6
generate_heatmaps.py
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@ -72,10 +72,10 @@ def process(yaml_name):
raise ValueError('No previous survey date provided') raise ValueError('No previous survey date provided')
# Set paths for raster files # Set paths for raster files
current_raster = os.path.join(input_tif_dir, base_name + '.tif') current_raster = os.path.join(input_tif_dir, base_name + '_DEM.tif')
previous_base_name = re.sub('\d+', previous_date, base_name) previous_base_name = re.sub('\d+', previous_date, base_name)
previous_raster = os.path.join(input_tif_dir, previous_base_name + '.tif') previous_raster = os.path.join(input_tif_dir, previous_base_name + '_DEM.tif')
heatmap_raster = os.path.join(output_tif_dir, base_name + '.tif') heatmap_raster = os.path.join(output_tif_dir, base_name + '_heatmap.tif')
print('processing {}'.format(beach)) print('processing {}'.format(beach))

918
las_manipulation.py
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@ -1,459 +1,459 @@
"""las_manipulation.py """las_manipulation.py
Clip, classify, and detect swash zone for an input las file. Clip, classify, and detect swash zone for an input las file.
Example usage: Example usage:
# Process single survey at specific beach # Process single survey at specific beach
python las_manipulation.py survey-1-avoca.yaml python las_manipulation.py survey-1-avoca.yaml
# Process single survey at multiple beaches # Process single survey at multiple beaches
python las_manipulation.py survey-1-avoca.yaml survey-1-pearl.yaml python las_manipulation.py survey-1-avoca.yaml survey-1-pearl.yaml
# Process all surveys at specific beach # Process all surveys at specific beach
python las_manipulation.py *avoca.yaml python las_manipulation.py *avoca.yaml
# Process all beaches for specific survey date # Process all beaches for specific survey date
python las_manipulation.py survey-1*.yaml python las_manipulation.py survey-1*.yaml
""" """
import os import os
import sys import sys
import yaml import yaml
import argparse import argparse
import subprocess import subprocess
from glob import glob from glob import glob
from tqdm import tqdm from tqdm import tqdm
import numpy as np import numpy as np
from scipy import interpolate from scipy import interpolate
import pandas as pd import pandas as pd
import geopandas as gpd import geopandas as gpd
from shapely.geometry import Point, Polygon from shapely.geometry import Point, Polygon
from survey_tools import call_lastools from survey_tools import call_lastools
def remove_problems(x_list, y_list, z_list, x_now, y_now, check_value): def remove_problems(x_list, y_list, z_list, x_now, y_now, check_value):
z_ave=nine_pt_moving_average(z_list) z_ave=nine_pt_moving_average(z_list)
deriv_ave, chainage=forward_derivative(x_list, y_list, z_ave) deriv_ave, chainage=forward_derivative(x_list, y_list, z_ave)
deriv_real, chainage=two_point_derivative(x_list, y_list, z_list) deriv_real, chainage=two_point_derivative(x_list, y_list, z_list)
#first find the reference contour on the beach #first find the reference contour on the beach
#index_contour, x_now, y_now, distance=find_beach_reference_contour_choose_closest(chainage, z_ave, x_list, y_list, x_now, y_now,deriv_ave, check_value) #index_contour, x_now, y_now, distance=find_beach_reference_contour_choose_closest(chainage, z_ave, x_list, y_list, x_now, y_now,deriv_ave, check_value)
index_contour, x_now, y_now, distance=find_beach_reference_contour(chainage, z_ave, x_list, y_list, x_now, y_now,deriv_ave,deriv_real,check_value) index_contour, x_now, y_now, distance=find_beach_reference_contour(chainage, z_ave, x_list, y_list, x_now, y_now,deriv_ave,deriv_real,check_value)
if index_contour<len(chainage): #other wise keep everthing if index_contour<len(chainage): #other wise keep everthing
#find the beach slope, get the interpolated line (beach line) and the index of the reference contour +1 #find the beach slope, get the interpolated line (beach line) and the index of the reference contour +1
beach_slope, beach_line, index_high=find_beach_slope(chainage, z_ave,index_contour, check_value) beach_slope, beach_line, index_high=find_beach_slope(chainage, z_ave,index_contour, check_value)
#find the natural deviation of the lower beach #find the natural deviation of the lower beach
nat_dev=get_natural_deviation(chainage, z_list, index_contour, index_high, beach_line) nat_dev=get_natural_deviation(chainage, z_list, index_contour, index_high, beach_line)
for i in range(index_contour, len(z_list)): for i in range(index_contour, len(z_list)):
if abs(z_list[i]-float(beach_line(chainage[i])))>nat_dev: if abs(z_list[i]-float(beach_line(chainage[i])))>nat_dev:
break break
else: else:
i=index_contour i=index_contour
z_return=z_list[0:i] z_return=z_list[0:i]
chainage_return=chainage[0:i] chainage_return=chainage[0:i]
return z_return, chainage_return, x_now, y_now, distance return z_return, chainage_return, x_now, y_now, distance
def two_point_derivative(x_list, y_list, z_list): def two_point_derivative(x_list, y_list, z_list):
chain=[((x_list[0]-x_list[i])**2+(y_list[0]-y_list[i])**2)**0.5 for i in range(0,len(x_list))] chain=[((x_list[0]-x_list[i])**2+(y_list[0]-y_list[i])**2)**0.5 for i in range(0,len(x_list))]
deriv=[(z_list[i+1]-z_list[i-1])/(chain[i+1]-chain[i-1]) for i in range(1, len(z_list)-1)] deriv=[(z_list[i+1]-z_list[i-1])/(chain[i+1]-chain[i-1]) for i in range(1, len(z_list)-1)]
deriv.insert(0,0) deriv.insert(0,0)
deriv.append(0) deriv.append(0)
return deriv, chain return deriv, chain
def forward_derivative(x_list, y_list, z_list): def forward_derivative(x_list, y_list, z_list):
chain=[((x_list[0]-x_list[i])**2+(y_list[0]-y_list[i])**2)**0.5 for i in range(0,len(x_list))] chain=[((x_list[0]-x_list[i])**2+(y_list[0]-y_list[i])**2)**0.5 for i in range(0,len(x_list))]
deriv=[(z_list[i]-z_list[i-1])/(chain[i]-chain[i-1]) for i in range(0, len(z_list)-1)] deriv=[(z_list[i]-z_list[i-1])/(chain[i]-chain[i-1]) for i in range(0, len(z_list)-1)]
deriv.insert(0,0) deriv.insert(0,0)
return deriv, chain return deriv, chain
def find_first_over_reference(z_list, value): def find_first_over_reference(z_list, value):
i=len(z_list)-1 i=len(z_list)-1
while i>0 and z_list[i]<value: while i>0 and z_list[i]<value:
i=i-1 i=i-1
return i return i
def nine_pt_moving_average(z_list): def nine_pt_moving_average(z_list):
i=0 i=0
move_ave=[] move_ave=[]
while i<len(z_list): while i<len(z_list):
if i<5: if i<5:
ave=np.mean([z_list[j] for j in range(0,i+5)]) ave=np.mean([z_list[j] for j in range(0,i+5)])
elif i>len(z_list)-5: elif i>len(z_list)-5:
ave=np.mean([z_list[j] for j in range(i-4,len(z_list))]) ave=np.mean([z_list[j] for j in range(i-4,len(z_list))])
else: else:
ave=np.mean([z_list[j] for j in range(i-4,i+5)]) ave=np.mean([z_list[j] for j in range(i-4,i+5)])
move_ave.append(ave) move_ave.append(ave)
i=i+1 i=i+1
return move_ave return move_ave
def find_neg_derivative(z_list, deriv_list): def find_neg_derivative(z_list, deriv_list):
i=len(z_list)-5 i=len(z_list)-5
while z_list[i]>=0 and z_list[i+1]>=0 and z_list[i+2]>=0 and z_list[i+3]>=0 and z_list[i+4]>=0: while z_list[i]>=0 and z_list[i+1]>=0 and z_list[i+2]>=0 and z_list[i+3]>=0 and z_list[i+4]>=0:
i=i-1 i=i-1
return i return i
def find_beach_reference_contour_choose_closest(chain_list, z_ave_list, x_list, y_list, x_last, y_last, deriv_ave_list, check_value): def find_beach_reference_contour_choose_closest(chain_list, z_ave_list, x_list, y_list, x_last, y_last, deriv_ave_list, check_value):
#note that z_list should be the 9 point moving average #note that z_list should be the 9 point moving average
#assumes that beaches are shallow (|derivative|<0.3), sloping and between 0 - 4 m AHD #assumes that beaches are shallow (|derivative|<0.3), sloping and between 0 - 4 m AHD
i=0 i=0
choice_list=[] choice_list=[]
distance_list=[] distance_list=[]
if z_ave_list[i]>check_value: if z_ave_list[i]>check_value:
state_now='over' state_now='over'
else: else:
state_now='under' state_now='under'
while i<len(z_ave_list): while i<len(z_ave_list):
if state_now=='under' and z_ave_list[i]>check_value: #only keep if it is downward sloping if state_now=='under' and z_ave_list[i]>check_value: #only keep if it is downward sloping
state_now='over' state_now='over'
elif state_now=='over' and z_ave_list[i]<check_value: elif state_now=='over' and z_ave_list[i]<check_value:
choice_list.append(i) choice_list.append(i)
state_now='under' state_now='under'
if x_last!=None: if x_last!=None:
distance_list.append(((x_last - x_list[i])**2+(y_last - y_list[i])**2)**0.5) distance_list.append(((x_last - x_list[i])**2+(y_last - y_list[i])**2)**0.5)
i=i+1 i=i+1
if len(choice_list)>0 and x_last==None: #choose the first time for the first point if len(choice_list)>0 and x_last==None: #choose the first time for the first point
i=choice_list[0] i=choice_list[0]
distance=0 distance=0
elif len(choice_list)>0 and x_last!=None: elif len(choice_list)>0 and x_last!=None:
assert(len(choice_list)==len(distance_list)) assert(len(choice_list)==len(distance_list))
i=choice_list[distance_list.index(min(distance_list))] i=choice_list[distance_list.index(min(distance_list))]
distance=min(distance_list) distance=min(distance_list)
if i>=len(x_list): if i>=len(x_list):
i=len(x_list)-1 i=len(x_list)-1
if x_last!=None: if x_last!=None:
distance=((x_last - x_list[i])**2+(y_last - y_list[i])**2)**0.5 distance=((x_last - x_list[i])**2+(y_last - y_list[i])**2)**0.5
else: else:
distance=0 distance=0
x=x_list[i] x=x_list[i]
y=y_list[i] y=y_list[i]
return i, x, y, distance return i, x, y, distance
def find_beach_reference_contour(chain_list, z_ave_list, x_list, y_list, x_last, y_last, deriv_ave_list,deriv_real_list, check_value): def find_beach_reference_contour(chain_list, z_ave_list, x_list, y_list, x_last, y_last, deriv_ave_list,deriv_real_list, check_value):
#note that z_list should be the 9 point moving average #note that z_list should be the 9 point moving average
#assumes that beaches are shallow (|derivative|<0.3), sloping and between 0 - 4 m AHD #assumes that beaches are shallow (|derivative|<0.3), sloping and between 0 - 4 m AHD
i=len(z_ave_list)-1 i=len(z_ave_list)-1
while i>=0 and (z_ave_list[i]>check_value+2 or z_ave_list[i]<check_value-2 or deriv_ave_list[i]>0 or max([abs(i) for i in deriv_real_list[max(0,i-7):i]]+[0])>0.3):#beaches are shallow sloping, low while i>=0 and (z_ave_list[i]>check_value+2 or z_ave_list[i]<check_value-2 or deriv_ave_list[i]>0 or max([abs(i) for i in deriv_real_list[max(0,i-7):i]]+[0])>0.3):#beaches are shallow sloping, low
i=i-1 i=i-1
#find the first time it gets to check_value after this #find the first time it gets to check_value after this
while i>=0 and z_ave_list[i]<check_value: while i>=0 and z_ave_list[i]<check_value:
i=i-1 i=i-1
if i==0: if i==0:
i=len(z_ave_list)-1 # the whole this is above the beach i=len(z_ave_list)-1 # the whole this is above the beach
if x_last!=None: if x_last!=None:
distance=((x_last - x_list[i])**2+(y_last - y_list[i])**2)**0.5 distance=((x_last - x_list[i])**2+(y_last - y_list[i])**2)**0.5
else: else:
distance=0 distance=0
x=x_list[i] x=x_list[i]
y=y_list[i] y=y_list[i]
return i, x, y, distance return i, x, y, distance
def find_beach_slope(chain_list, z_ave_list, ref_index, check_value): def find_beach_slope(chain_list, z_ave_list, ref_index, check_value):
#ref index is the index of the check value #ref index is the index of the check value
#find the beach slope between this point and 1 m above this point #find the beach slope between this point and 1 m above this point
i=ref_index i=ref_index
while i>0 and z_ave_list[i]<check_value+1: while i>0 and z_ave_list[i]<check_value+1:
i=i-1 i=i-1
# Hide numpy floating point arithmetic warnings # Hide numpy floating point arithmetic warnings
np.seterr(all='ignore') np.seterr(all='ignore')
slope=(z_ave_list[i]-z_ave_list[ref_index])/(chain_list[i]-chain_list[ref_index]) slope=(z_ave_list[i]-z_ave_list[ref_index])/(chain_list[i]-chain_list[ref_index])
# Show numpy floating point arithmetic warnings # Show numpy floating point arithmetic warnings
np.seterr(all=None) np.seterr(all=None)
beach_ave=interpolate.interp1d([min(chain_list),max(chain_list)], [(min(chain_list)-chain_list[ref_index])*slope+z_ave_list[ref_index], (z_ave_list[ref_index]-(chain_list[ref_index]-max(chain_list))*slope)]) beach_ave=interpolate.interp1d([min(chain_list),max(chain_list)], [(min(chain_list)-chain_list[ref_index])*slope+z_ave_list[ref_index], (z_ave_list[ref_index]-(chain_list[ref_index]-max(chain_list))*slope)])
return slope, beach_ave, i return slope, beach_ave, i
def get_natural_deviation(chain_list, z_list, ref_index, ref_high, beach_ave): def get_natural_deviation(chain_list, z_list, ref_index, ref_high, beach_ave):
#for the points considered to be on the beach (reference contour to reference contour +1), find the average natural deviation #for the points considered to be on the beach (reference contour to reference contour +1), find the average natural deviation
deviation=[] deviation=[]
for i in range(ref_high, ref_index+1): for i in range(ref_high, ref_index+1):
dev_tmp=abs(z_list[i] - float(beach_ave(chain_list[i]))) dev_tmp=abs(z_list[i] - float(beach_ave(chain_list[i])))
deviation.append(dev_tmp) deviation.append(dev_tmp)
natural_deviation=min(np.max(deviation),0.4) #THIS MAY BE TOO CONSERVATIVE natural_deviation=min(np.max(deviation),0.4) #THIS MAY BE TOO CONSERVATIVE
return natural_deviation return natural_deviation
def distance_point_to_poly(x_list, y_list, x_now, y_now): def distance_point_to_poly(x_list, y_list, x_now, y_now):
#make a line from the mid of x_list, y_list #make a line from the mid of x_list, y_list
end=Point(x_list[-1], y_list[-1]) end=Point(x_list[-1], y_list[-1])
point=Point(x_now, y_now) point=Point(x_now, y_now)
dist=point.distance(end) dist=point.distance(end)
return dist return dist
def polygon_wave_runup(xyz_1m, direction, shp_name, set_check_value, distance_check, zone): def polygon_wave_runup(xyz_1m, direction, shp_name, set_check_value, distance_check, zone):
#print('starting processing of wave runup') #print('starting processing of wave runup')
all_data=pd.read_csv(xyz_1m, header=None, names=['X','Y','Z']) all_data=pd.read_csv(xyz_1m, header=None, names=['X','Y','Z'])
if direction=='north_south': if direction=='north_south':
all_data_sorted=all_data.sort_values(by=['X', 'Y'], ascending=[1,0]) all_data_sorted=all_data.sort_values(by=['X', 'Y'], ascending=[1,0])
elif direction=='west_east': elif direction=='west_east':
all_data_sorted=all_data.sort_values(by=['Y', 'X'], ascending=[0,1]) all_data_sorted=all_data.sort_values(by=['Y', 'X'], ascending=[0,1])
fixed_now=0 fixed_now=0
a=0 a=0
X_tmp=[] X_tmp=[]
processed_data = pd.DataFrame(columns=['X','Y','Z']) processed_data = pd.DataFrame(columns=['X','Y','Z'])
list_to_print=[10,20,30,40,50,60,70,80,90] list_to_print=[10,20,30,40,50,60,70,80,90]
crop_line=[] crop_line=[]
top_line=[] top_line=[]
tmp_x_last=None tmp_x_last=None
tmp_y_last=None tmp_y_last=None
exceed_list=[] exceed_list=[]
# Create progress bar # Create progress bar
pbar = tqdm(all_data_sorted.iterrows(), total=all_data_sorted.shape[0]) pbar = tqdm(all_data_sorted.iterrows(), total=all_data_sorted.shape[0])
for index, line in pbar: for index, line in pbar:
a=a+1 a=a+1
percent_done=round(a/len(all_data_sorted)*100,1) percent_done=round(a/len(all_data_sorted)*100,1)
if percent_done in list_to_print: if percent_done in list_to_print:
#print("Finished %s%% of the processing" % percent_done) #print("Finished %s%% of the processing" % percent_done)
list_to_print=list_to_print[1:len(list_to_print)] list_to_print=list_to_print[1:len(list_to_print)]
if direction=='north_south': if direction=='north_south':
check_this=line['X'] check_this=line['X']
elif direction=='west_east': elif direction=='west_east':
check_this=line['Y'] check_this=line['Y']
if check_this==fixed_now: if check_this==fixed_now:
X_tmp.append(line['X']) X_tmp.append(line['X'])
Y_tmp.append(line['Y']) Y_tmp.append(line['Y'])
Z_tmp.append(line['Z']) Z_tmp.append(line['Z'])
else: else:
if len(X_tmp)!=0: if len(X_tmp)!=0:
#try: ########may need to change!~! #try: ########may need to change!~!
if len(X_tmp)>10: if len(X_tmp)>10:
Z_set, chainage_tmp, temp_x, temp_y, distance=remove_problems(X_tmp, Y_tmp, Z_tmp,tmp_x_last, tmp_y_last, set_check_value) Z_set, chainage_tmp, temp_x, temp_y, distance=remove_problems(X_tmp, Y_tmp, Z_tmp,tmp_x_last, tmp_y_last, set_check_value)
#except: #except:
else: else:
Z_set=Z_tmp Z_set=Z_tmp
temp_x=X_tmp[len(Z_set)-1] temp_x=X_tmp[len(Z_set)-1]
temp_y=Y_tmp[len(Z_set)-1] temp_y=Y_tmp[len(Z_set)-1]
distance=0 distance=0
distance_2_old=distance_point_to_poly(X_tmp, Y_tmp, temp_x, temp_y) distance_2_old=distance_point_to_poly(X_tmp, Y_tmp, temp_x, temp_y)
if distance_2_old<distance_check: # find a way to change so it is checking the distance from the first crop polyogn, concave_now.buffer(buffer) if distance_2_old<distance_check: # find a way to change so it is checking the distance from the first crop polyogn, concave_now.buffer(buffer)
tmp_x_last=temp_x tmp_x_last=temp_x
tmp_y_last=temp_y tmp_y_last=temp_y
crop_line.append([X_tmp[len(Z_set)-1], Y_tmp[len(Z_set)-1]]) crop_line.append([X_tmp[len(Z_set)-1], Y_tmp[len(Z_set)-1]])
top_line.append([X_tmp[0], Y_tmp[0]]) top_line.append([X_tmp[0], Y_tmp[0]])
#otherwise crop by the distance_check #otherwise crop by the distance_check
else: else:
exceed_list.append(1) exceed_list.append(1)
try: try:
tmp_x_last=X_tmp[len(X_tmp)-distance_check] #beacuse this is a 1m DSM, this works tmp_x_last=X_tmp[len(X_tmp)-distance_check] #beacuse this is a 1m DSM, this works
tmp_y_last=Y_tmp[len(Y_tmp)-distance_check] tmp_y_last=Y_tmp[len(Y_tmp)-distance_check]
crop_line.append([tmp_x_last, tmp_y_last]) crop_line.append([tmp_x_last, tmp_y_last])
top_line.append([X_tmp[0], Y_tmp[0]]) top_line.append([X_tmp[0], Y_tmp[0]])
except: except:
print('problem with the last crop point, keeping whole line') print('problem with the last crop point, keeping whole line')
crop_line.append([X_tmp[-1], Y_tmp[-1]]) crop_line.append([X_tmp[-1], Y_tmp[-1]])
top_line.append([X_tmp[0], Y_tmp[0]]) top_line.append([X_tmp[0], Y_tmp[0]])
if direction=='north_south': if direction=='north_south':
fixed_now=line['X'] fixed_now=line['X']
elif direction=='west_east': elif direction=='west_east':
fixed_now=line['Y'] fixed_now=line['Y']
X_tmp=[line['X']] X_tmp=[line['X']]
Y_tmp=[line['Y']] Y_tmp=[line['Y']]
Z_tmp=[line['Z']] Z_tmp=[line['Z']]
else: else:
if direction=='north_south': if direction=='north_south':
fixed_now=line['X'] fixed_now=line['X']
elif direction=='west_east': elif direction=='west_east':
fixed_now=line['Y'] fixed_now=line['Y']
X_tmp=[line['X']] X_tmp=[line['X']]
Y_tmp=[line['Y']] Y_tmp=[line['Y']]
Z_tmp=[line['Z']] Z_tmp=[line['Z']]
#for the last line #for the last line
derivative, chainage=forward_derivative(X_tmp, Y_tmp, Z_tmp) derivative, chainage=forward_derivative(X_tmp, Y_tmp, Z_tmp)
if len(X_tmp)>10: if len(X_tmp)>10:
Z_set, chainage_tmp, temp_x, temp_y, distance=remove_problems(X_tmp, Y_tmp, Z_tmp,tmp_x_last, tmp_y_last, set_check_value) Z_set, chainage_tmp, temp_x, temp_y, distance=remove_problems(X_tmp, Y_tmp, Z_tmp,tmp_x_last, tmp_y_last, set_check_value)
#except: #except:
else: else:
Z_set=Z_tmp Z_set=Z_tmp
temp_x=X_tmp[len(Z_set)-1] temp_x=X_tmp[len(Z_set)-1]
temp_y=Y_tmp[len(Z_set)-1] temp_y=Y_tmp[len(Z_set)-1]
distance=0 distance=0
X_set=X_tmp[0:len(Z_set)] X_set=X_tmp[0:len(Z_set)]
Y_set=Y_tmp[0:len(Z_set)] Y_set=Y_tmp[0:len(Z_set)]
#write to new data frame #write to new data frame
#if len(Z_set)>0: #if len(Z_set)>0:
# for i in range(0, len(Z_set)): # for i in range(0, len(Z_set)):
# processed_data =processed_data.append({'X':X_set[i],'Y':Y_set[i],'Z':Z_set[i],'r':r_set[i],'g':g_set[i],'b':b_set[i]}, ignore_index=True) # processed_data =processed_data.append({'X':X_set[i],'Y':Y_set[i],'Z':Z_set[i],'r':r_set[i],'g':g_set[i],'b':b_set[i]}, ignore_index=True)
#add to crop line #add to crop line
distance_2_old=distance_point_to_poly(X_tmp, Y_tmp, temp_x, temp_y) distance_2_old=distance_point_to_poly(X_tmp, Y_tmp, temp_x, temp_y)
if distance_2_old<distance_check: # find a way to change so it is checking the distance from the first crop polyogn, concave_now.buffer(buffer) if distance_2_old<distance_check: # find a way to change so it is checking the distance from the first crop polyogn, concave_now.buffer(buffer)
tmp_x_last=temp_x tmp_x_last=temp_x
tmp_y_last=temp_y tmp_y_last=temp_y
crop_line.append([X_tmp[len(Z_set)-1], Y_tmp[len(Z_set)-1]]) crop_line.append([X_tmp[len(Z_set)-1], Y_tmp[len(Z_set)-1]])
top_line.append([X_tmp[0], Y_tmp[0]]) top_line.append([X_tmp[0], Y_tmp[0]])
#otherwise crop by the distance_check #otherwise crop by the distance_check
else: else:
exceed_list.append(1) exceed_list.append(1)
tmp_x_last=X_tmp[len(X_tmp)-distance_check] tmp_x_last=X_tmp[len(X_tmp)-distance_check]
tmp_y_last=Y_tmp[len(Y_tmp)-distance_check] tmp_y_last=Y_tmp[len(Y_tmp)-distance_check]
crop_line.append(tmp_x_last, tmp_y_last) crop_line.append(tmp_x_last, tmp_y_last)
top_line.append([X_tmp[0], Y_tmp[0]]) top_line.append([X_tmp[0], Y_tmp[0]])
#otherwise dont add. straight line is better #otherwise dont add. straight line is better
if direction=='north_south': if direction=='north_south':
y_filtered=nine_pt_moving_average([i[1] for i in crop_line]) y_filtered=nine_pt_moving_average([i[1] for i in crop_line])
crop_new=[[crop_line[i][0],y_filtered[i]] for i in range(0, len(crop_line))] crop_new=[[crop_line[i][0],y_filtered[i]] for i in range(0, len(crop_line))]
elif direction=='west_east': elif direction=='west_east':
x_filtered=nine_pt_moving_average([i[0] for i in crop_line]) x_filtered=nine_pt_moving_average([i[0] for i in crop_line])
crop_new=[[x_filtered[i],crop_line[i][1]] for i in range(0, len(crop_line))] crop_new=[[x_filtered[i],crop_line[i][1]] for i in range(0, len(crop_line))]
for_shape=crop_new+top_line[::-1] for_shape=crop_new+top_line[::-1]
for_shape.append(crop_new[0]) for_shape.append(crop_new[0])
#print('exceeded the manual distance_check %s%% of the time. manually cropping undertaken' % (round(len(exceed_list)/a,2)*100)) #print('exceeded the manual distance_check %s%% of the time. manually cropping undertaken' % (round(len(exceed_list)/a,2)*100))
#making the cropping shapefile #making the cropping shapefile
#print('making the crop polygon') #print('making the crop polygon')
# Simplify polygon to remove invalid geometry # Simplify polygon to remove invalid geometry
#geom = Polygon(for_shape).simplify(10) #geom = Polygon(for_shape).simplify(10)
geom = Polygon(for_shape) geom = Polygon(for_shape)
# Export polygon as shapefile # Export polygon as shapefile
df = gpd.GeoDataFrame(geometry=[geom]) df = gpd.GeoDataFrame(geometry=[geom])
df.crs = {'init': 'epsg:283{}'.format(zone), 'no_defs': True} df.crs = {'init': 'epsg:283{}'.format(zone), 'no_defs': True}
df.to_file(shp_name, driver='ESRI Shapefile') df.to_file(shp_name, driver='ESRI Shapefile')
return None return None
def remove_temp_files(directory): def remove_temp_files(directory):
for f in os.listdir(directory): for f in os.listdir(directory):
os.unlink(os.path.join(directory, f)) os.unlink(os.path.join(directory, f))
return None return None
def process(yaml_file): def process(yaml_file):
with open(yaml_file, 'r') as f: with open(yaml_file, 'r') as f:
params = yaml.safe_load(f) params = yaml.safe_load(f)
print("Starting to process %s" % params['BEACH']) print("Starting to process %s" % params['BEACH'])
input_las = params['INPUT LAS'] input_las = params['INPUT LAS']
initial_crop_poly = params['INITIAL CROP POLY'] initial_crop_poly = params['INITIAL CROP POLY']
lasground_step = params['LASGROUND STEP'] lasground_step = params['LASGROUND STEP']
zone_MGA = params['ZONE MGA'] zone_MGA = params['ZONE MGA']
check_value = params['CHECK VALUE'] check_value = params['CHECK VALUE']
direct = params['DIRECTION'] direct = params['DIRECTION']
check_distance = params['CHECK DISTANCE'] check_distance = params['CHECK DISTANCE']
las_dir = params['LAS CLASSIFIED FOLDER'] las_dir = params['LAS CLASSIFIED FOLDER']
shp_dir = params['SHP SWASH FOLDER'] shp_dir = params['SHP SWASH FOLDER']
tmp_dir = params['TMP FOLDER'] tmp_dir = params['TMP FOLDER']
survey_date=params['SURVEY DATE'] survey_date=params['SURVEY DATE']
beach=params['BEACH'] beach=params['BEACH']
# Get base name of input las # Get base name of input las
#las_basename = os.path.splitext(os.path.basename(input_las))[0] #las_basename = os.path.splitext(os.path.basename(input_las))[0]
las_basename='%s_%s' % (beach.lower().replace(" ","_"), survey_date) las_basename='%s_%s' % (beach.lower().replace(" ","_"), survey_date)
# # Crop to beach boundary # # Crop to beach boundary
print('Clipping...') print('Clipping...')
las_clipped_name = os.path.join(tmp_dir, las_basename + '_clipped.las') las_clipped_name = os.path.join(tmp_dir, las_basename + '_clipped.las')
call_lastools('lasclip', input=input_las, output=las_clipped_name, call_lastools('lasclip', input=input_las, output=las_clipped_name,
args=['-poly', initial_crop_poly], verbose=False) args=['-poly', initial_crop_poly], verbose=False)
# Classify ground points # Classify ground points
print('Classifying ground...') print('Classifying ground...')
las_classified_name = os.path.join(las_dir, las_basename + '.las') las_classified_name = os.path.join(las_dir, las_basename + '.las')
call_lastools('lasground_new', input=las_clipped_name, output=las_classified_name, call_lastools('lasground_new', input=las_clipped_name, output=las_classified_name,
args=['-step', lasground_step], verbose=False) args=['-step', lasground_step], verbose=False)
# Interpolate point cloud onto a grid # Interpolate point cloud onto a grid
print('Interpolating to grid...') print('Interpolating to grid...')
xyz_name = os.path.join(tmp_dir, las_basename + '.xyz') xyz_name = os.path.join(tmp_dir, las_basename + '.xyz')
call_lastools('blast2dem', input=las_classified_name, output=xyz_name, call_lastools('blast2dem', input=las_classified_name, output=xyz_name,
args=['-step', 0.1], verbose=False) args=['-step', 1], verbose=False)
# Make runup clipping mask from gridded point cloud # Make runup clipping mask from gridded point cloud
print('Calculating runup clipping mask...') print('Calculating runup clipping mask...')
shp_name = os.path.join(shp_dir, las_basename + '.shp') shp_name = os.path.join(shp_dir, las_basename + '.shp')
polygon_wave_runup(xyz_name, direct, shp_name, check_value, check_distance, zone_MGA) polygon_wave_runup(xyz_name, direct, shp_name, check_value, check_distance, zone_MGA)
#NOTE THAT YOU NEED TO CHECK THE OUTPUT SHP FILE AND ADJUST AS REQUIRED #NOTE THAT YOU NEED TO CHECK THE OUTPUT SHP FILE AND ADJUST AS REQUIRED
#delete the temp files #delete the temp files
remove_temp_files(tmp_dir) remove_temp_files(tmp_dir)
def main(): def main():
example_text = """examples: example_text = """examples:
# Process single survey at specific beach # Process single survey at specific beach
python las_manipulation.py survey-1-avoca.yaml python las_manipulation.py survey-1-avoca.yaml
# Process single survey at multiple beaches # Process single survey at multiple beaches
python las_manipulation.py survey-1-avoca.yaml survey-1-pearl.yaml python las_manipulation.py survey-1-avoca.yaml survey-1-pearl.yaml
# Process all surveys at specific beach # Process all surveys at specific beach
python las_manipulation.py *avoca.yaml python las_manipulation.py *avoca.yaml
# Process all beaches for specific survey date # Process all beaches for specific survey date
python las_manipulation.py survey-1*.yaml python las_manipulation.py survey-1*.yaml
""" """
# Set up command line arguments # Set up command line arguments
parser = argparse.ArgumentParser( parser = argparse.ArgumentParser(
epilog=example_text, epilog=example_text,
formatter_class=argparse.RawDescriptionHelpFormatter) formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('input', help='path to yaml file(s)', nargs='*') parser.add_argument('input', help='path to yaml file(s)', nargs='*')
# Print usage if no arguments are provided # Print usage if no arguments are provided
if len(sys.argv) == 1: if len(sys.argv) == 1:
parser.print_help(sys.stderr) parser.print_help(sys.stderr)
sys.exit(1) sys.exit(1)
# Parse arguments # Parse arguments
args = parser.parse_args() args = parser.parse_args()
yaml_files = [] yaml_files = []
[yaml_files.extend(glob(f)) for f in args.input] [yaml_files.extend(glob(f)) for f in args.input]
for yaml_file in yaml_files: for yaml_file in yaml_files:
process(yaml_file) process(yaml_file)
if __name__ == '__main__': if __name__ == '__main__':
main() main()

662
las_outputs.py
Unescape Escape View File

@ -1,321 +1,341 @@
"""las_outputs.py """las_outputs.py
Crop swash zone, plot survey profiles, and complete a volume analysis based Crop swash zone, plot survey profiles, and complete a volume analysis based
on the output from `las_manipulation.py`. on the output from `las_manipulation.py`.
Example usage: Example usage:
# Process single survey at specific beach # Process single survey at specific beach
python las_outputs.py survey-1-avoca.yaml python las_outputs.py survey-1-avoca.yaml
# Process single survey at multiple beaches # Process single survey at multiple beaches
python las_outputs.py survey-1-avoca.yaml survey-1-pearl.yaml python las_outputs.py survey-1-avoca.yaml survey-1-pearl.yaml
# Process all surveys at specific beach # Process all surveys at specific beach
python las_outputs.py *avoca.yaml python las_outputs.py *avoca.yaml
# Process all beaches for specific survey date # Process all beaches for specific survey date
python las_outputs.py survey-1*.yaml python las_outputs.py survey-1*.yaml
""" """
import os import os
import io import io
import re import re
import sys import sys
import math import math
import yaml import yaml
import argparse import argparse
import datetime import datetime
import subprocess import subprocess
import numpy as np import numpy as np
import pandas as pd import pandas as pd
from glob import glob from glob import glob
from cycler import cycler from cycler import cycler
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator from matplotlib.ticker import MultipleLocator
import nielsen_volumes import nielsen_volumes
from survey_tools import call_lastools, extract_pts, update_survey_output from survey_tools import call_lastools, extract_pts, update_survey_output
def get_datestring(x): def get_datestring(x):
"""Format a date integer into an ISO-8601 date string """Format a date integer into an ISO-8601 date string
Args: Args:
x: unformatted date x: unformatted date
Returns: Returns:
formatted date string formatted date string
Examples: Examples:
>> get_datestring(19700101) >> get_datestring(19700101)
'1970-01-01' '1970-01-01'
""" """
datestr = pd.datetime.strptime(str(x), '%Y%m%d').strftime('%Y-%m-%d') datestr = pd.datetime.strptime(str(x), '%Y%m%d').strftime('%Y-%m-%d')
return datestr return datestr
def remove_temp_files(directory): def remove_temp_files(directory):
for f in os.listdir(directory): for f in os.listdir(directory):
os.unlink(os.path.join(directory, f)) os.unlink(os.path.join(directory, f))
return None return None
def plot_profiles(profile_name, csv_output_dir, graph_loc, ch_limits, delta_vol, survey_date, scale_figures=False): def plot_profiles(profile_name, csv_output_dir, graph_loc, ch_limits, delta_vol, survey_date, scale_figures=False):
csv_name = profile_name + '.csv' csv_name = profile_name + '.csv'
profiles = pd.read_csv(os.path.join(csv_output_dir, csv_name)) profiles = pd.read_csv(os.path.join(csv_output_dir, csv_name))
# Remove metadata, and extract profile coordinates # Remove metadata, and extract profile coordinates
profiles = profiles.loc[:, 'Chainage':].set_index('Chainage') profiles = profiles.loc[:, 'Chainage':].set_index('Chainage')
# Determine if current survey is the latest # Determine if current survey is the latest
current_survey_col = 'Elevation_' + survey_date current_survey_col = 'Elevation_' + survey_date
is_latest = profiles.columns[-1] == current_survey_col is_latest = profiles.columns[-1] == current_survey_col
# Only plot profiles up to current survey date # Only plot profiles up to current survey date
profiles = profiles.loc[:, :current_survey_col] profiles = profiles.loc[:, :current_survey_col]
# Find landward limit of profile (behind beach) # Find landward limit of profile (behind beach)
ch_min = ch_limits.loc[profile_name, 'Landward Limit'] ch_min = ch_limits.loc[profile_name, 'Landward Limit']
# Set figure dimensions based on beach size # Set figure dimensions based on beach size
vertical_exag = 5 vertical_exag = 5
m_per_inch = 8 m_per_inch = 8
try: try:
fig_h = profiles.dropna().values.max() / m_per_inch * vertical_exag fig_h = profiles.dropna().values.max() / m_per_inch * vertical_exag
fig_w = (profiles.index.max() - ch_min) / m_per_inch fig_w = (profiles.index.max() - ch_min) / m_per_inch
except ValueError: except ValueError:
fig_h = 2.3 fig_h = 2.3
fig_w = 10 fig_w = 10
if scale_figures: if scale_figures:
fig, ax = plt.subplots(figsize=(fig_w, fig_h)) fig, ax = plt.subplots(figsize=(fig_w, fig_h))
ax.set_aspect(vertical_exag) ax.set_aspect(vertical_exag)
else: else:
fig, ax = plt.subplots(figsize=(10, 2.3)) fig, ax = plt.subplots(figsize=(10, 2.3))
for col in profiles.columns: for col in profiles.columns:
profile = profiles.loc[:, col] profile = profiles.loc[:, col]
date_str = col.split('_')[-1] date_str = col.split('_')[-1]
date = get_datestring(date_str) date = get_datestring(date_str)
ax.plot(profile.index, profile, label=date) ax.plot(profile.index, profile, label=date)
# Show landward limit of volume calculations # Show landward limit of volume calculations
ax.axvline(x=ch_min, color='#222222', linestyle='--', label='Landward limit') ax.axvline(x=ch_min, color='#222222', linestyle='--', label='Landward limit')
ax.set_title(profile_name) ax.set_title(profile_name)
ax.set_xlabel('Chainage (m)', labelpad=10) ax.set_xlabel('Chainage (m)', labelpad=10)
ax.set_ylabel('Elevation (m AHD)', labelpad=10) ax.set_ylabel('Elevation (m AHD)', labelpad=10)
Ylim=ax.get_ylim()[1] Ylim=ax.get_ylim()[1]
if Ylim<10: if Ylim<10:
ax.set_ylim([ax.get_ylim()[0], 10]) ax.set_ylim([ax.get_ylim()[0], 10])
# Remove empty space at left of figure # Remove empty space at left of figure
try: try:
ax.set_xlim(left=profile.first_valid_index() - 10) ax.set_xlim(left=profile.first_valid_index() - 10)
except TypeError: except TypeError:
pass pass
# Show most recent volume change # Show most recent volume change
if delta_vol is not None: if delta_vol is not None:
ax.annotate('Most recent\nvolume change:\n{:+0.1f} m$^3$/m'.format(delta_vol), ax.annotate('Most recent\nvolume change:\n{:+0.1f} m$^3$/m'.format(delta_vol),
(0.05, 0.15), xycoords='axes fraction', fontsize=9, (0.05, 0.15), xycoords='axes fraction', fontsize=9,
backgroundcolor='#ffffff', linespacing=1.5) backgroundcolor='#ffffff', linespacing=1.5)
ax.legend(edgecolor='none', facecolor='#ffffff', fontsize=9, labelspacing=1) ax.legend(edgecolor='none', facecolor='#ffffff', fontsize=9, labelspacing=1)
loc='lower left', bbox_to_anchor=(1.02,0))
ax.xaxis.set_minor_locator(MultipleLocator(5)) ax.xaxis.set_minor_locator(MultipleLocator(5))
ax.yaxis.set_minor_locator(MultipleLocator(0.5)) ax.yaxis.set_minor_locator(MultipleLocator(0.5))
ax.xaxis.grid(True, which='minor', color='k', linewidth=0.5, alpha=0.3) 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) ax.yaxis.grid(True,which='minor',color='k', linewidth=0.5, alpha=0.3)
# Save in folder with current date # Save in folder with current date
png_dirs = [os.path.join(graph_loc, get_datestring(survey_date))] png_dirs = [os.path.join(graph_loc, get_datestring(survey_date))]
if is_latest: if is_latest:
# Save copy in'latest' if survey is most recent # Save copy in'latest' if survey is most recent
png_dirs += [os.path.join(graph_loc, 'latest')] png_dirs += [os.path.join(graph_loc, 'latest')]
for png_dir in png_dirs: for png_dir in png_dirs:
# Prepare output directory # Prepare output directory
try: try:
os.makedirs(png_dir) os.makedirs(png_dir)
except FileExistsError: except FileExistsError:
pass pass
png_name = os.path.join(png_dir, profile_name + '.png') png_name = os.path.join(png_dir, profile_name + '.png')
plt.savefig(png_name, bbox_inches='tight', dpi=300) plt.savefig(png_name, bbox_inches='tight', dpi=300)
plt.close() plt.close()
def calculate_volumes(profile_name, current_survey_date, previous_survey_date, def calculate_volumes(profile_name, current_survey_date, previous_survey_date,
csv_output_dir, ch_limits, volume_output_dir): csv_output_dir, ch_limits, volume_output_dir):
csv_prof = profile_name + '.csv' csv_prof = profile_name + '.csv'
beach = re.search('.*(?=_\d)', profile_name).group() beach = re.search('.*(?=_\d)', profile_name).group()
profiles = pd.read_csv(os.path.join(csv_output_dir, csv_prof)) profiles = pd.read_csv(os.path.join(csv_output_dir, csv_prof))
# Remove metadata, and extract profile coordinates # Remove metadata, and extract profile coordinates
profiles = profiles.loc[:, 'Chainage':].set_index('Chainage') profiles = profiles.loc[:, 'Chainage':].set_index('Chainage')
# Find landward limit of profile (behind beach) # Find landward limit of profile (behind beach)
ch_min = ch_limits.loc[profile_name, 'Landward Limit'] ch_min = ch_limits.loc[profile_name, 'Landward Limit']
# Open volume spreadsheet # Open volume spreadsheet
csv_vol = os.path.join(volume_output_dir, 'volumes.csv') csv_vol = os.path.join(volume_output_dir, 'volumes.csv')
try: try:
volumes = pd.read_csv(csv_vol, index_col=0) volumes = pd.read_csv(csv_vol, index_col=0)
except FileNotFoundError: except FileNotFoundError:
# Create new dataframe if csv does not exist # Create new dataframe if csv does not exist
volumes = pd.DataFrame() volumes = pd.DataFrame()
# Get Nielsen erosion volumes for current survey date # Get Nielsen erosion volumes for current survey date
current_survey = 'Elevation_' + current_survey_date current_survey = 'Elevation_' + current_survey_date
chainage = profiles.loc[:, current_survey].dropna().index chainage = profiles.loc[:, current_survey].dropna().index
elevation = profiles.loc[:, current_survey].dropna().values elevation = profiles.loc[:, current_survey].dropna().values
try: try:
volume = nielsen_volumes.volume_available(chainage, elevation, ch_min) volume = nielsen_volumes.volume_available(chainage, elevation, ch_min)
except ValueError: except ValueError:
volume = np.nan volume = np.nan
# Update spreadsheet # Update spreadsheet
volumes.loc[profile_name, 'Volume_' + current_survey_date] = volume volumes.loc[profile_name, 'Volume_' + current_survey_date] = volume
# Save updated volumes spreadsheet # Save updated volumes spreadsheet
volumes = volumes[volumes.columns.sort_values()] volumes = volumes[volumes.columns.sort_values()]
volumes = volumes.sort_index() volumes = volumes.sort_index()
volumes.to_csv(csv_vol) volumes.to_csv(csv_vol)
# Get most recent volume difference for current profile
current_date_idx = volumes.columns.get_loc('Volume_' + current_survey_date)
if previous_survey_date=="nan": previous_date_idx = volumes.columns.get_loc('Volume_' + previous_survey_date)
# Return None if there is only one survey
delta_vol = None if previous_date_idx < 0:
else: # Return None if there is only one survey
# Get most recent volume difference for current profile delta_vol = None
current_date_idx = volumes.columns.get_loc('Volume_' + current_survey_date) else:
previous_date_idx = volumes.columns.get_loc('Volume_' + previous_survey_date) previous_vol = volumes.loc[profile_name][previous_date_idx]
previous_vol = volumes.loc[profile_name][previous_date_idx] current_vol = volumes.loc[profile_name][current_date_idx]
current_vol = volumes.loc[profile_name][current_date_idx] delta_vol = current_vol - previous_vol
delta_vol = current_vol - previous_vol
return delta_vol
return delta_vol
def process(yaml_file):
def process(yaml_file): with open(yaml_file, 'r') as f:
with open(yaml_file, 'r') as f: params = yaml.safe_load(f)
params = yaml.safe_load(f)
print("Starting to process %s" % params['BEACH'])
print("Starting to process %s" % params['BEACH']) beach = params['BEACH']
beach = params['BEACH'] survey_date = str(params['SURVEY DATE'])
survey_date = str(params['SURVEY DATE']) survey_date_previous = str(params['PREVIOUS SURVEY DATE'])
survey_date_previous = str(params['PREVIOUS SURVEY DATE']) original_las = params['INPUT LAS']
original_las = params['INPUT LAS'] classified_las_dir = params['LAS CLASSIFIED FOLDER']
classified_las_dir = params['LAS CLASSIFIED FOLDER'] shp_swash_dir = params['SHP SWASH FOLDER']
shp_swash_dir = params['SHP SWASH FOLDER'] crop_heatmap_poly = params['HEATMAP CROP POLY']
crop_heatmap_poly = params['HEATMAP CROP POLY'] output_las_dir = params['LAS OUTPUT FOLDER']
output_las_dir = params['LAS OUTPUT FOLDER'] zone_MGA = params['ZONE MGA']
zone_MGA = params['ZONE MGA'] output_poly_dir = params['SHP RASTER FOLDER']
output_poly_dir = params['SHP RASTER FOLDER'] output_tif_dir = params['TIF DEM FOLDER']
output_tif_dir = params['TIF DEM FOLDER'] cp_csv = params['INPUT CSV']
cp_csv = params['INPUT CSV'] profile_limit_file = params['PROFILE LIMIT FILE']
profile_limit_file = params['PROFILE LIMIT FILE'] csv_output_dir = params['CSV OUTPUT FOLDER']
csv_output_dir = params['CSV OUTPUT FOLDER'] graph_loc = params['PNG OUTPUT FOLDER']
graph_loc = params['PNG OUTPUT FOLDER'] volume_output_dir = params['CSV VOLUMES FOLDER']
volume_output_dir = params['CSV VOLUMES FOLDER'] tmp_dir = params['TMP FOLDER']
tmp_dir = params['TMP FOLDER']
# Get base name of input las
# Get base name of input las #las_basename = os.path.splitext(os.path.basename(original_las))[0]
#las_basename = os.path.splitext(os.path.basename(original_las))[0]
las_basename='%s_%s' % (beach.lower().replace(" ","_"), survey_date)
las_basename='%s_%s' % (beach.lower().replace(" ","_"), survey_date)
# Get name of input point cloud
# Get name of input point cloud input_las = os.path.join(classified_las_dir, las_basename + '.las')
input_las = os.path.join(classified_las_dir, las_basename + '.las')
# Get name of swash cropping polygon
# Get name of swash cropping polygon crop_swash_poly = os.path.join(shp_swash_dir, las_basename + '.shp')
crop_swash_poly = os.path.join(shp_swash_dir, las_basename + '.shp')
# Crop point cloud to swash boundary
# Make a raster from point cloud print('Cropping swash...')
print('Creating heat map raster...') las_data = call_lastools('lasclip', input=input_las, output='-stdout',
tif_name = os.path.join(output_tif_dir, las_basename + '.tif') args=['-poly', crop_swash_poly], verbose=False)
call_lastools('blast2dem', input=input_las, output=tif_name,
args=['-step', 0.1, '-keep_class', 2], verbose=True) # Export classified, clipped las for delivery to client
# IF THIS STEP ISN'T WORKING: las_name = os.path.join(output_las_dir, las_basename + '.las')
# might mean there are no data lines with open (las_name, 'wb') as f:
# trying running with args=['-step', 1, '-keep_class', 2, '-rescale', 0.001,0.001,0.001] f.write(las_data)
#call_lastools('las2dem', input=las_data, output=tif_name,
# args=['-step', 1, '-keep_class', 2, '-rescale', 0.001,0.001,0.001], verbose=False) # Apply sea-side clipping polygon
print('Cropping back of beach...')
# Extract elevations along profiles from triangulated surface las_data = call_lastools('lasclip', input=las_data, output='-stdout',
print('Extracting profile elevations...') args=['-poly', crop_heatmap_poly], verbose=False)
df = extract_pts(
input_las,
cp_csv, # Create clipping polygon for heatmap raster
survey_date, print('Creating heat map cropping polygon...')
beach, shp_name = os.path.join(output_poly_dir, las_basename + '.shp')
args=['-parse', 'sxyz', '-keep_class', '2'], call_lastools('lasboundary', input=las_data, output=shp_name, verbose=False)
verbose=False)
# Make a raster from point cloud
# Update survey profiles print('Creating heat map raster...')
update_survey_output(df, csv_output_dir) tif_name = os.path.join(output_tif_dir, las_basename + '_DEM.tif')
call_lastools('las2dem', input=las_data, output=tif_name,
# Get landward limit of surveys args=['-step', 1, '-keep_class', 2], verbose=False)
ch_limits = pd.read_excel(profile_limit_file, index_col='Profile') # IF THIS STEP ISN'T WORKING:
# might mean there are no data lines
# Plot profiles, and save sand volumes for current beach # trying running with args=['-step', 1, '-keep_class', 2, '-rescale', 0.001,0.001,0.001]
print('Updating figures...') #call_lastools('las2dem', input=las_data, output=tif_name,
profile_names = df['Profile'].unique() # args=['-step', 1, '-keep_class', 2, '-rescale', 0.001,0.001,0.001], verbose=False)
for profile_name in profile_names:
delta_vol = calculate_volumes(profile_name, survey_date, survey_date_previous, # Extract elevations along profiles from triangulated surface
csv_output_dir, ch_limits, print('Extracting profile elevations...')
volume_output_dir) df = extract_pts(
plot_profiles(profile_name, csv_output_dir, graph_loc, ch_limits, las_data,
delta_vol, survey_date) cp_csv,
survey_date,
# Remove temprary files beach,
remove_temp_files(tmp_dir) args=['-parse', 'sxyz', '-keep_class', '2'],
verbose=False)
def main(): # Update survey profiles
example_text = """examples: update_survey_output(df, csv_output_dir)
# Process single survey at specific beach # Get landward limit of surveys
python las_outputs.py survey-1-avoca.yaml ch_limits = pd.read_excel(profile_limit_file, index_col='Profile')
# Process single survey at multiple beaches # Plot profiles, and save sand volumes for current beach
python las_outputs.py survey-1-avoca.yaml survey-1-pearl.yaml print('Updating figures...')
profile_names = df['Profile'].unique()
# Process all surveys at specific beach for profile_name in profile_names:
python las_outputs.py *avoca.yaml delta_vol = calculate_volumes(profile_name, survey_date, survey_date_previous,
csv_output_dir, ch_limits,
# Process all beaches for specific survey date volume_output_dir)
python las_outputs.py survey-1*.yaml plot_profiles(profile_name, csv_output_dir, graph_loc, ch_limits,
""" delta_vol, survey_date)
# Set up command line arguments # Remove temprary files
parser = argparse.ArgumentParser( remove_temp_files(tmp_dir)
epilog=example_text,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('input', help='path to yaml file(s)', nargs='*') def main():
example_text = """examples:
# Print usage if no arguments are provided
if len(sys.argv) == 1: # Process single survey at specific beach
parser.print_help(sys.stderr) python las_outputs.py survey-1-avoca.yaml
sys.exit(1)
# Process single survey at multiple beaches
# Parse arguments python las_outputs.py survey-1-avoca.yaml survey-1-pearl.yaml
args = parser.parse_args()
yaml_files = [] # Process all surveys at specific beach
[yaml_files.extend(glob(f)) for f in args.input] python las_outputs.py *avoca.yaml
for yaml_file in yaml_files: # Process all beaches for specific survey date
process(yaml_file) python las_outputs.py survey-1*.yaml
"""
if __name__ == '__main__': # Set up command line arguments
main() parser = argparse.ArgumentParser(
epilog=example_text,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('input', help='path to yaml file(s)', nargs='*')
# Print usage if no arguments are provided
if len(sys.argv) == 1:
parser.print_help(sys.stderr)
sys.exit(1)
# Parse arguments
args = parser.parse_args()
yaml_files = []
[yaml_files.extend(glob(f)) for f in args.input]
for yaml_file in yaml_files:
process(yaml_file)
if __name__ == '__main__':
main()

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plot_heatmaps.py
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@ -70,7 +70,7 @@ def process(yaml_name):
raise ValueError('No previous survey date provided') raise ValueError('No previous survey date provided')
# Set paths for raster files # Set paths for raster files
heatmap_raster = os.path.join(input_tif_dir, base_name + '.tif') heatmap_raster = os.path.join(input_tif_dir, base_name + '_heatmap.tif')
print('processing {}'.format(beach)) print('processing {}'.format(beach))
mxd = arcpy.mapping.MapDocument(mxd_name) mxd = arcpy.mapping.MapDocument(mxd_name)

92
polyline_to_points.py
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@ -0,0 +1,92 @@
"""polyline_to_points.py
Extract interpolated points along transects in a shapefile.
D. Howe
d.howe@wrl.unsw.edu.au
2020-02-19
"""
import sys
import argparse
import numpy as np
import pandas as pd
import geopandas as gpd
from shapely.geometry import LineString
def extract(shp_path, spacing=1, field=None):
rows = []
shp = gpd.read_file(shp_path)
if field is None:
# Assume profile name is second field in shapefile
field = shp.columns[1]
for i, line in shp.iterrows():
g = line.geometry
chainages = np.arange(0, g.length, step=spacing)
for chainage in chainages:
easting, northing = g.interpolate(chainage).xy
row = {
'ProfileNum': line[field],
'Easting': easting[0],
'Northing': northing[0],
'Chainage': chainage,
}
rows.append(row)
# Create output dataframe
df = pd.DataFrame(rows)
# Re-order columns
df = df[['ProfileNum', 'Easting', 'Northing', 'Chainage']]
# Export to csv
csv_path = shp_path.replace('.shp', '.csv')
df.to_csv(csv_path, index=False)
def main():
example_text = """examples:
# Extract points at default spacing (1m)
$ python polyline_to_points.py path/to/shp
# Extract points at 5m increments
$ python polyline_to_points.py -s 5 path/to/shp
# Use profile names from field "ProfileID" in the attribute table
$ python polyline_to_points.py -f ProfileID path/to/shp
"""
# Set up command line arguments
parser = argparse.ArgumentParser(
epilog=example_text,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('shp_path',
metavar='SHAPEFILE',
help='path to input shapefile')
parser.add_argument('-s',
'--spacing',
metavar='SPACING',
default=1,
type=int,
help='space between points (default=1)')
parser.add_argument('-f',
'--field',
metavar='FIELDNAME',
help='profile field name in attribute table')
# Print usage if no arguments are provided
if len(sys.argv) == 1:
parser.print_help(sys.stderr)
sys.exit(1)
# Parse arguments
args = parser.parse_args()
# Combine images
extract(**vars(args))
if __name__ == '__main__':
main()
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