Update 'las_manipulation.py'
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"""las_manipulation.py
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Clip, classify, and detect swash zone for an input las file.
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Example usage:
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# Process single survey at specific beach
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python las_manipulation.py survey-1-avoca.yaml
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# Process single survey at multiple beaches
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python las_manipulation.py survey-1-avoca.yaml survey-1-pearl.yaml
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# Process all surveys at specific beach
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python las_manipulation.py *avoca.yaml
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# Process all beaches for specific survey date
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python las_manipulation.py survey-1*.yaml
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"""
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import os
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import sys
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import yaml
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import argparse
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import subprocess
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from glob import glob
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from tqdm import tqdm
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import numpy as np
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from scipy import interpolate
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import pandas as pd
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import geopandas as gpd
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from shapely.geometry import Point, Polygon
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from survey_tools import call_lastools
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def remove_problems(x_list, y_list, z_list, x_now, y_now, check_value):
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z_ave=nine_pt_moving_average(z_list)
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deriv_ave, chainage=forward_derivative(x_list, y_list, z_ave)
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deriv_real, chainage=two_point_derivative(x_list, y_list, z_list)
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#first find the reference contour on the beach
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#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)
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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)
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if index_contour<len(chainage): #other wise keep everthing
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#find the beach slope, get the interpolated line (beach line) and the index of the reference contour +1
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beach_slope, beach_line, index_high=find_beach_slope(chainage, z_ave,index_contour, check_value)
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#find the natural deviation of the lower beach
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nat_dev=get_natural_deviation(chainage, z_list, index_contour, index_high, beach_line)
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for i in range(index_contour, len(z_list)):
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if abs(z_list[i]-float(beach_line(chainage[i])))>nat_dev:
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break
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else:
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i=index_contour
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z_return=z_list[0:i]
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chainage_return=chainage[0:i]
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return z_return, chainage_return, x_now, y_now, distance
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def two_point_derivative(x_list, y_list, z_list):
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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))]
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deriv=[(z_list[i+1]-z_list[i-1])/(chain[i+1]-chain[i-1]) for i in range(1, len(z_list)-1)]
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deriv.insert(0,0)
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deriv.append(0)
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return deriv, chain
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def forward_derivative(x_list, y_list, z_list):
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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))]
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deriv=[(z_list[i]-z_list[i-1])/(chain[i]-chain[i-1]) for i in range(0, len(z_list)-1)]
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deriv.insert(0,0)
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return deriv, chain
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def find_first_over_reference(z_list, value):
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i=len(z_list)-1
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while i>0 and z_list[i]<value:
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i=i-1
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return i
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def nine_pt_moving_average(z_list):
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i=0
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move_ave=[]
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while i<len(z_list):
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if i<5:
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ave=np.mean([z_list[j] for j in range(0,i+5)])
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elif i>len(z_list)-5:
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ave=np.mean([z_list[j] for j in range(i-4,len(z_list))])
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else:
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ave=np.mean([z_list[j] for j in range(i-4,i+5)])
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move_ave.append(ave)
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i=i+1
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return move_ave
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def find_neg_derivative(z_list, deriv_list):
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i=len(z_list)-5
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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:
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i=i-1
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return i
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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):
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#note that z_list should be the 9 point moving average
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#assumes that beaches are shallow (|derivative|<0.3), sloping and between 0 - 4 m AHD
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i=0
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choice_list=[]
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distance_list=[]
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if z_ave_list[i]>check_value:
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state_now='over'
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else:
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state_now='under'
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while i<len(z_ave_list):
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if state_now=='under' and z_ave_list[i]>check_value: #only keep if it is downward sloping
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state_now='over'
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elif state_now=='over' and z_ave_list[i]<check_value:
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choice_list.append(i)
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state_now='under'
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if x_last!=None:
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distance_list.append(((x_last - x_list[i])**2+(y_last - y_list[i])**2)**0.5)
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i=i+1
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if len(choice_list)>0 and x_last==None: #choose the first time for the first point
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i=choice_list[0]
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distance=0
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elif len(choice_list)>0 and x_last!=None:
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assert(len(choice_list)==len(distance_list))
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i=choice_list[distance_list.index(min(distance_list))]
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distance=min(distance_list)
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if i>=len(x_list):
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i=len(x_list)-1
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if x_last!=None:
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distance=((x_last - x_list[i])**2+(y_last - y_list[i])**2)**0.5
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else:
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distance=0
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x=x_list[i]
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y=y_list[i]
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return i, x, y, distance
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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):
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#note that z_list should be the 9 point moving average
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#assumes that beaches are shallow (|derivative|<0.3), sloping and between 0 - 4 m AHD
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i=len(z_ave_list)-1
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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
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i=i-1
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#find the first time it gets to check_value after this
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while i>=0 and z_ave_list[i]<check_value:
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i=i-1
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if i==0:
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i=len(z_ave_list)-1 # the whole this is above the beach
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if x_last!=None:
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distance=((x_last - x_list[i])**2+(y_last - y_list[i])**2)**0.5
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else:
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distance=0
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x=x_list[i]
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y=y_list[i]
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return i, x, y, distance
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def find_beach_slope(chain_list, z_ave_list, ref_index, check_value):
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#ref index is the index of the check value
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#find the beach slope between this point and 1 m above this point
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i=ref_index
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while i>0 and z_ave_list[i]<check_value+1:
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i=i-1
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# Hide numpy floating point arithmetic warnings
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np.seterr(all='ignore')
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slope=(z_ave_list[i]-z_ave_list[ref_index])/(chain_list[i]-chain_list[ref_index])
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# Show numpy floating point arithmetic warnings
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np.seterr(all=None)
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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)])
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return slope, beach_ave, i
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def get_natural_deviation(chain_list, z_list, ref_index, ref_high, beach_ave):
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#for the points considered to be on the beach (reference contour to reference contour +1), find the average natural deviation
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deviation=[]
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for i in range(ref_high, ref_index+1):
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dev_tmp=abs(z_list[i] - float(beach_ave(chain_list[i])))
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deviation.append(dev_tmp)
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natural_deviation=min(np.max(deviation),0.4) #THIS MAY BE TOO CONSERVATIVE
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return natural_deviation
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def distance_point_to_poly(x_list, y_list, x_now, y_now):
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#make a line from the mid of x_list, y_list
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end=Point(x_list[-1], y_list[-1])
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point=Point(x_now, y_now)
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dist=point.distance(end)
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return dist
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def polygon_wave_runup(xyz_1m, direction, shp_name, set_check_value, distance_check, zone):
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#print('starting processing of wave runup')
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all_data=pd.read_csv(xyz_1m, header=None, names=['X','Y','Z'])
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if direction=='north_south':
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all_data_sorted=all_data.sort_values(by=['X', 'Y'], ascending=[1,0])
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elif direction=='west_east':
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all_data_sorted=all_data.sort_values(by=['Y', 'X'], ascending=[0,1])
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fixed_now=0
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a=0
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X_tmp=[]
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processed_data = pd.DataFrame(columns=['X','Y','Z'])
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list_to_print=[10,20,30,40,50,60,70,80,90]
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crop_line=[]
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top_line=[]
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tmp_x_last=None
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tmp_y_last=None
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exceed_list=[]
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# Create progress bar
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pbar = tqdm(all_data_sorted.iterrows(), total=all_data_sorted.shape[0])
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for index, line in pbar:
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a=a+1
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percent_done=round(a/len(all_data_sorted)*100,1)
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if percent_done in list_to_print:
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#print("Finished %s%% of the processing" % percent_done)
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list_to_print=list_to_print[1:len(list_to_print)]
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if direction=='north_south':
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check_this=line['X']
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elif direction=='west_east':
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check_this=line['Y']
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if check_this==fixed_now:
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X_tmp.append(line['X'])
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Y_tmp.append(line['Y'])
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Z_tmp.append(line['Z'])
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else:
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if len(X_tmp)!=0:
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#try: ########may need to change!~!
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if len(X_tmp)>10:
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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)
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#except:
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else:
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Z_set=Z_tmp
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temp_x=X_tmp[len(Z_set)-1]
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temp_y=Y_tmp[len(Z_set)-1]
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distance=0
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distance_2_old=distance_point_to_poly(X_tmp, Y_tmp, temp_x, temp_y)
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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)
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tmp_x_last=temp_x
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tmp_y_last=temp_y
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crop_line.append([X_tmp[len(Z_set)-1], Y_tmp[len(Z_set)-1]])
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top_line.append([X_tmp[0], Y_tmp[0]])
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#otherwise crop by the distance_check
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else:
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exceed_list.append(1)
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try:
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tmp_x_last=X_tmp[len(X_tmp)-distance_check] #beacuse this is a 1m DSM, this works
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tmp_y_last=Y_tmp[len(Y_tmp)-distance_check]
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crop_line.append([tmp_x_last, tmp_y_last])
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top_line.append([X_tmp[0], Y_tmp[0]])
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except:
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print('problem with the last crop point, keeping whole line')
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crop_line.append([X_tmp[-1], Y_tmp[-1]])
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top_line.append([X_tmp[0], Y_tmp[0]])
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if direction=='north_south':
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fixed_now=line['X']
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elif direction=='west_east':
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fixed_now=line['Y']
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X_tmp=[line['X']]
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Y_tmp=[line['Y']]
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Z_tmp=[line['Z']]
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else:
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if direction=='north_south':
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fixed_now=line['X']
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elif direction=='west_east':
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fixed_now=line['Y']
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X_tmp=[line['X']]
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Y_tmp=[line['Y']]
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Z_tmp=[line['Z']]
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#for the last line
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derivative, chainage=forward_derivative(X_tmp, Y_tmp, Z_tmp)
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if len(X_tmp)>10:
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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)
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#except:
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else:
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Z_set=Z_tmp
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temp_x=X_tmp[len(Z_set)-1]
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temp_y=Y_tmp[len(Z_set)-1]
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distance=0
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X_set=X_tmp[0:len(Z_set)]
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Y_set=Y_tmp[0:len(Z_set)]
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#write to new data frame
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#if len(Z_set)>0:
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# for i in range(0, len(Z_set)):
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# 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)
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#add to crop line
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distance_2_old=distance_point_to_poly(X_tmp, Y_tmp, temp_x, temp_y)
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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)
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tmp_x_last=temp_x
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tmp_y_last=temp_y
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crop_line.append([X_tmp[len(Z_set)-1], Y_tmp[len(Z_set)-1]])
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top_line.append([X_tmp[0], Y_tmp[0]])
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#otherwise crop by the distance_check
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else:
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exceed_list.append(1)
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tmp_x_last=X_tmp[len(X_tmp)-distance_check]
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tmp_y_last=Y_tmp[len(Y_tmp)-distance_check]
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crop_line.append(tmp_x_last, tmp_y_last)
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top_line.append([X_tmp[0], Y_tmp[0]])
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#otherwise dont add. straight line is better
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if direction=='north_south':
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y_filtered=nine_pt_moving_average([i[1] for i in crop_line])
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crop_new=[[crop_line[i][0],y_filtered[i]] for i in range(0, len(crop_line))]
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elif direction=='west_east':
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x_filtered=nine_pt_moving_average([i[0] for i in crop_line])
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crop_new=[[x_filtered[i],crop_line[i][1]] for i in range(0, len(crop_line))]
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for_shape=crop_new+top_line[::-1]
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for_shape.append(crop_new[0])
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#print('exceeded the manual distance_check %s%% of the time. manually cropping undertaken' % (round(len(exceed_list)/a,2)*100))
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#making the cropping shapefile
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#print('making the crop polygon')
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# Simplify polygon to remove invalid geometry
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#geom = Polygon(for_shape).simplify(10)
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geom = Polygon(for_shape)
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# Export polygon as shapefile
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df = gpd.GeoDataFrame(geometry=[geom])
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df.crs = {'init': 'epsg:283{}'.format(zone), 'no_defs': True}
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df.to_file(shp_name, driver='ESRI Shapefile')
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return None
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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 process(yaml_file):
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with open(yaml_file, 'r') as f:
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params = yaml.safe_load(f)
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print("Starting to process %s" % params['BEACH'])
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input_las = params['INPUT LAS']
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initial_crop_poly = params['INITIAL CROP POLY']
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lasground_step = params['LASGROUND STEP']
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zone_MGA = params['ZONE MGA']
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check_value = params['CHECK VALUE']
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direct = params['DIRECTION']
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check_distance = params['CHECK DISTANCE']
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las_dir = params['LAS CLASSIFIED FOLDER']
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shp_dir = params['SHP SWASH FOLDER']
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tmp_dir = params['TMP FOLDER']
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survey_date=params['SURVEY DATE']
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beach=params['BEACH']
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# Get base name of input las
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#las_basename = os.path.splitext(os.path.basename(input_las))[0]
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las_basename='%s_%s' % (beach.lower().replace(" ","_"), survey_date)
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# # Crop to beach boundary
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print('Clipping...')
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las_clipped_name = os.path.join(tmp_dir, las_basename + '_clipped.las')
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call_lastools('lasclip', input=input_las, output=las_clipped_name,
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args=['-poly', initial_crop_poly], verbose=False)
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# Classify ground points
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print('Classifying ground...')
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las_classified_name = os.path.join(las_dir, las_basename + '.las')
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call_lastools('lasground_new', input=las_clipped_name, output=las_classified_name,
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args=['-step', lasground_step], verbose=False)
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# Interpolate point cloud onto a grid
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print('Interpolating to grid...')
|
||||
xyz_name = os.path.join(tmp_dir, las_basename + '.xyz')
|
||||
call_lastools('las2dem', input=las_classified_name, output=xyz_name,
|
||||
args=['-step', 1], verbose=False)
|
||||
|
||||
# Make runup clipping mask from gridded point cloud
|
||||
print('Calculating runup clipping mask...')
|
||||
shp_name = os.path.join(shp_dir, las_basename + '.shp')
|
||||
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
|
||||
|
||||
#delete the temp files
|
||||
remove_temp_files(tmp_dir)
|
||||
|
||||
|
||||
def main():
|
||||
example_text = """examples:
|
||||
|
||||
# Process single survey at specific beach
|
||||
python las_manipulation.py survey-1-avoca.yaml
|
||||
|
||||
# Process single survey at multiple beaches
|
||||
python las_manipulation.py survey-1-avoca.yaml survey-1-pearl.yaml
|
||||
|
||||
# Process all surveys at specific beach
|
||||
python las_manipulation.py *avoca.yaml
|
||||
|
||||
# Process all beaches for specific survey date
|
||||
python las_manipulation.py survey-1*.yaml
|
||||
"""
|
||||
|
||||
# Set up command line arguments
|
||||
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()
|
||||
"""las_manipulation.py
|
||||
Clip, classify, and detect swash zone for an input las file.
|
||||
|
||||
Example usage:
|
||||
|
||||
# Process single survey at specific beach
|
||||
python las_manipulation.py survey-1-avoca.yaml
|
||||
|
||||
# Process single survey at multiple beaches
|
||||
python las_manipulation.py survey-1-avoca.yaml survey-1-pearl.yaml
|
||||
|
||||
# Process all surveys at specific beach
|
||||
python las_manipulation.py *avoca.yaml
|
||||
|
||||
# Process all beaches for specific survey date
|
||||
python las_manipulation.py survey-1*.yaml
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import yaml
|
||||
import argparse
|
||||
import subprocess
|
||||
from glob import glob
|
||||
from tqdm import tqdm
|
||||
import numpy as np
|
||||
from scipy import interpolate
|
||||
import pandas as pd
|
||||
import geopandas as gpd
|
||||
from shapely.geometry import Point, Polygon
|
||||
|
||||
from survey_tools import call_lastools
|
||||
|
||||
|
||||
def remove_problems(x_list, y_list, z_list, x_now, y_now, check_value):
|
||||
|
||||
z_ave=nine_pt_moving_average(z_list)
|
||||
deriv_ave, chainage=forward_derivative(x_list, y_list, z_ave)
|
||||
deriv_real, chainage=two_point_derivative(x_list, y_list, z_list)
|
||||
|
||||
#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(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
|
||||
#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)
|
||||
#find the natural deviation of the lower beach
|
||||
nat_dev=get_natural_deviation(chainage, z_list, index_contour, index_high, beach_line)
|
||||
|
||||
for i in range(index_contour, len(z_list)):
|
||||
if abs(z_list[i]-float(beach_line(chainage[i])))>nat_dev:
|
||||
break
|
||||
else:
|
||||
i=index_contour
|
||||
|
||||
z_return=z_list[0:i]
|
||||
chainage_return=chainage[0:i]
|
||||
return z_return, chainage_return, x_now, y_now, distance
|
||||
|
||||
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))]
|
||||
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.append(0)
|
||||
|
||||
return deriv, chain
|
||||
|
||||
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))]
|
||||
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)
|
||||
|
||||
return deriv, chain
|
||||
|
||||
def find_first_over_reference(z_list, value):
|
||||
i=len(z_list)-1
|
||||
|
||||
while i>0 and z_list[i]<value:
|
||||
i=i-1
|
||||
|
||||
return i
|
||||
|
||||
def nine_pt_moving_average(z_list):
|
||||
i=0
|
||||
move_ave=[]
|
||||
while i<len(z_list):
|
||||
if i<5:
|
||||
ave=np.mean([z_list[j] for j in range(0,i+5)])
|
||||
elif i>len(z_list)-5:
|
||||
ave=np.mean([z_list[j] for j in range(i-4,len(z_list))])
|
||||
else:
|
||||
ave=np.mean([z_list[j] for j in range(i-4,i+5)])
|
||||
|
||||
move_ave.append(ave)
|
||||
i=i+1
|
||||
return move_ave
|
||||
|
||||
def find_neg_derivative(z_list, deriv_list):
|
||||
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:
|
||||
i=i-1
|
||||
|
||||
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):
|
||||
#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
|
||||
i=0
|
||||
choice_list=[]
|
||||
distance_list=[]
|
||||
if z_ave_list[i]>check_value:
|
||||
state_now='over'
|
||||
else:
|
||||
state_now='under'
|
||||
|
||||
while i<len(z_ave_list):
|
||||
if state_now=='under' and z_ave_list[i]>check_value: #only keep if it is downward sloping
|
||||
state_now='over'
|
||||
elif state_now=='over' and z_ave_list[i]<check_value:
|
||||
choice_list.append(i)
|
||||
state_now='under'
|
||||
if x_last!=None:
|
||||
distance_list.append(((x_last - x_list[i])**2+(y_last - y_list[i])**2)**0.5)
|
||||
i=i+1
|
||||
|
||||
if len(choice_list)>0 and x_last==None: #choose the first time for the first point
|
||||
i=choice_list[0]
|
||||
distance=0
|
||||
elif len(choice_list)>0 and x_last!=None:
|
||||
assert(len(choice_list)==len(distance_list))
|
||||
i=choice_list[distance_list.index(min(distance_list))]
|
||||
distance=min(distance_list)
|
||||
|
||||
|
||||
if i>=len(x_list):
|
||||
i=len(x_list)-1
|
||||
if x_last!=None:
|
||||
distance=((x_last - x_list[i])**2+(y_last - y_list[i])**2)**0.5
|
||||
else:
|
||||
distance=0
|
||||
|
||||
x=x_list[i]
|
||||
y=y_list[i]
|
||||
|
||||
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):
|
||||
#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
|
||||
|
||||
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
|
||||
i=i-1
|
||||
|
||||
#find the first time it gets to check_value after this
|
||||
while i>=0 and z_ave_list[i]<check_value:
|
||||
i=i-1
|
||||
|
||||
if i==0:
|
||||
i=len(z_ave_list)-1 # the whole this is above the beach
|
||||
|
||||
if x_last!=None:
|
||||
distance=((x_last - x_list[i])**2+(y_last - y_list[i])**2)**0.5
|
||||
else:
|
||||
distance=0
|
||||
x=x_list[i]
|
||||
y=y_list[i]
|
||||
|
||||
return i, x, y, distance
|
||||
|
||||
|
||||
def find_beach_slope(chain_list, z_ave_list, ref_index, check_value):
|
||||
#ref index is the index of the check value
|
||||
#find the beach slope between this point and 1 m above this point
|
||||
|
||||
i=ref_index
|
||||
while i>0 and z_ave_list[i]<check_value+1:
|
||||
i=i-1
|
||||
|
||||
# Hide numpy floating point arithmetic warnings
|
||||
np.seterr(all='ignore')
|
||||
|
||||
slope=(z_ave_list[i]-z_ave_list[ref_index])/(chain_list[i]-chain_list[ref_index])
|
||||
|
||||
# Show numpy floating point arithmetic warnings
|
||||
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)])
|
||||
|
||||
return slope, beach_ave, i
|
||||
|
||||
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
|
||||
deviation=[]
|
||||
for i in range(ref_high, ref_index+1):
|
||||
dev_tmp=abs(z_list[i] - float(beach_ave(chain_list[i])))
|
||||
deviation.append(dev_tmp)
|
||||
|
||||
natural_deviation=min(np.max(deviation),0.4) #THIS MAY BE TOO CONSERVATIVE
|
||||
|
||||
return natural_deviation
|
||||
|
||||
def distance_point_to_poly(x_list, y_list, x_now, y_now):
|
||||
#make a line from the mid of x_list, y_list
|
||||
end=Point(x_list[-1], y_list[-1])
|
||||
|
||||
point=Point(x_now, y_now)
|
||||
|
||||
|
||||
dist=point.distance(end)
|
||||
|
||||
|
||||
return dist
|
||||
|
||||
def polygon_wave_runup(xyz_1m, direction, shp_name, set_check_value, distance_check, zone):
|
||||
#print('starting processing of wave runup')
|
||||
|
||||
all_data=pd.read_csv(xyz_1m, header=None, names=['X','Y','Z'])
|
||||
|
||||
if direction=='north_south':
|
||||
all_data_sorted=all_data.sort_values(by=['X', 'Y'], ascending=[1,0])
|
||||
elif direction=='west_east':
|
||||
all_data_sorted=all_data.sort_values(by=['Y', 'X'], ascending=[0,1])
|
||||
|
||||
fixed_now=0
|
||||
a=0
|
||||
X_tmp=[]
|
||||
processed_data = pd.DataFrame(columns=['X','Y','Z'])
|
||||
list_to_print=[10,20,30,40,50,60,70,80,90]
|
||||
crop_line=[]
|
||||
top_line=[]
|
||||
tmp_x_last=None
|
||||
tmp_y_last=None
|
||||
exceed_list=[]
|
||||
|
||||
# Create progress bar
|
||||
pbar = tqdm(all_data_sorted.iterrows(), total=all_data_sorted.shape[0])
|
||||
for index, line in pbar:
|
||||
a=a+1
|
||||
percent_done=round(a/len(all_data_sorted)*100,1)
|
||||
if percent_done in list_to_print:
|
||||
#print("Finished %s%% of the processing" % percent_done)
|
||||
list_to_print=list_to_print[1:len(list_to_print)]
|
||||
if direction=='north_south':
|
||||
check_this=line['X']
|
||||
elif direction=='west_east':
|
||||
check_this=line['Y']
|
||||
if check_this==fixed_now:
|
||||
X_tmp.append(line['X'])
|
||||
Y_tmp.append(line['Y'])
|
||||
Z_tmp.append(line['Z'])
|
||||
else:
|
||||
if len(X_tmp)!=0:
|
||||
#try: ########may need to change!~!
|
||||
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)
|
||||
#except:
|
||||
else:
|
||||
Z_set=Z_tmp
|
||||
temp_x=X_tmp[len(Z_set)-1]
|
||||
temp_y=Y_tmp[len(Z_set)-1]
|
||||
distance=0
|
||||
|
||||
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)
|
||||
tmp_x_last=temp_x
|
||||
tmp_y_last=temp_y
|
||||
crop_line.append([X_tmp[len(Z_set)-1], Y_tmp[len(Z_set)-1]])
|
||||
top_line.append([X_tmp[0], Y_tmp[0]])
|
||||
|
||||
#otherwise crop by the distance_check
|
||||
else:
|
||||
exceed_list.append(1)
|
||||
try:
|
||||
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]
|
||||
|
||||
crop_line.append([tmp_x_last, tmp_y_last])
|
||||
top_line.append([X_tmp[0], Y_tmp[0]])
|
||||
except:
|
||||
print('problem with the last crop point, keeping whole line')
|
||||
crop_line.append([X_tmp[-1], Y_tmp[-1]])
|
||||
top_line.append([X_tmp[0], Y_tmp[0]])
|
||||
|
||||
if direction=='north_south':
|
||||
fixed_now=line['X']
|
||||
elif direction=='west_east':
|
||||
fixed_now=line['Y']
|
||||
X_tmp=[line['X']]
|
||||
Y_tmp=[line['Y']]
|
||||
Z_tmp=[line['Z']]
|
||||
|
||||
else:
|
||||
if direction=='north_south':
|
||||
fixed_now=line['X']
|
||||
elif direction=='west_east':
|
||||
fixed_now=line['Y']
|
||||
X_tmp=[line['X']]
|
||||
Y_tmp=[line['Y']]
|
||||
Z_tmp=[line['Z']]
|
||||
|
||||
|
||||
#for the last line
|
||||
derivative, chainage=forward_derivative(X_tmp, Y_tmp, Z_tmp)
|
||||
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)
|
||||
#except:
|
||||
else:
|
||||
Z_set=Z_tmp
|
||||
temp_x=X_tmp[len(Z_set)-1]
|
||||
temp_y=Y_tmp[len(Z_set)-1]
|
||||
distance=0
|
||||
X_set=X_tmp[0:len(Z_set)]
|
||||
Y_set=Y_tmp[0:len(Z_set)]
|
||||
|
||||
|
||||
#write to new data frame
|
||||
#if len(Z_set)>0:
|
||||
# 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)
|
||||
#add to crop line
|
||||
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)
|
||||
tmp_x_last=temp_x
|
||||
tmp_y_last=temp_y
|
||||
crop_line.append([X_tmp[len(Z_set)-1], Y_tmp[len(Z_set)-1]])
|
||||
top_line.append([X_tmp[0], Y_tmp[0]])
|
||||
#otherwise crop by the distance_check
|
||||
else:
|
||||
exceed_list.append(1)
|
||||
tmp_x_last=X_tmp[len(X_tmp)-distance_check]
|
||||
tmp_y_last=Y_tmp[len(Y_tmp)-distance_check]
|
||||
crop_line.append(tmp_x_last, tmp_y_last)
|
||||
top_line.append([X_tmp[0], Y_tmp[0]])
|
||||
|
||||
#otherwise dont add. straight line is better
|
||||
if direction=='north_south':
|
||||
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))]
|
||||
elif direction=='west_east':
|
||||
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))]
|
||||
|
||||
for_shape=crop_new+top_line[::-1]
|
||||
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))
|
||||
#making the cropping shapefile
|
||||
#print('making the crop polygon')
|
||||
|
||||
# Simplify polygon to remove invalid geometry
|
||||
#geom = Polygon(for_shape).simplify(10)
|
||||
geom = Polygon(for_shape)
|
||||
|
||||
# Export polygon as shapefile
|
||||
df = gpd.GeoDataFrame(geometry=[geom])
|
||||
df.crs = {'init': 'epsg:283{}'.format(zone), 'no_defs': True}
|
||||
df.to_file(shp_name, driver='ESRI Shapefile')
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def remove_temp_files(directory):
|
||||
for f in os.listdir(directory):
|
||||
os.unlink(os.path.join(directory, f))
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def process(yaml_file):
|
||||
with open(yaml_file, 'r') as f:
|
||||
params = yaml.safe_load(f)
|
||||
|
||||
print("Starting to process %s" % params['BEACH'])
|
||||
input_las = params['INPUT LAS']
|
||||
initial_crop_poly = params['INITIAL CROP POLY']
|
||||
lasground_step = params['LASGROUND STEP']
|
||||
zone_MGA = params['ZONE MGA']
|
||||
check_value = params['CHECK VALUE']
|
||||
direct = params['DIRECTION']
|
||||
check_distance = params['CHECK DISTANCE']
|
||||
las_dir = params['LAS CLASSIFIED FOLDER']
|
||||
shp_dir = params['SHP SWASH FOLDER']
|
||||
tmp_dir = params['TMP FOLDER']
|
||||
survey_date=params['SURVEY DATE']
|
||||
beach=params['BEACH']
|
||||
|
||||
# Get base name of input las
|
||||
#las_basename = os.path.splitext(os.path.basename(input_las))[0]
|
||||
las_basename='%s_%s' % (beach.lower().replace(" ","_"), survey_date)
|
||||
|
||||
# # Crop to beach boundary
|
||||
print('Clipping...')
|
||||
las_clipped_name = os.path.join(tmp_dir, las_basename + '_clipped.las')
|
||||
call_lastools('lasclip', input=input_las, output=las_clipped_name,
|
||||
args=['-poly', initial_crop_poly], verbose=False)
|
||||
|
||||
# Classify ground points
|
||||
print('Classifying ground...')
|
||||
las_classified_name = os.path.join(las_dir, las_basename + '.las')
|
||||
call_lastools('lasground_new', input=las_clipped_name, output=las_classified_name,
|
||||
args=['-step', lasground_step], verbose=False)
|
||||
|
||||
# Interpolate point cloud onto a grid
|
||||
print('Interpolating to grid...')
|
||||
xyz_name = os.path.join(tmp_dir, las_basename + '.xyz')
|
||||
call_lastools('blast2dem', input=las_classified_name, output=xyz_name,
|
||||
args=['-step', 1], verbose=False)
|
||||
|
||||
# Make runup clipping mask from gridded point cloud
|
||||
print('Calculating runup clipping mask...')
|
||||
shp_name = os.path.join(shp_dir, las_basename + '.shp')
|
||||
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
|
||||
|
||||
#delete the temp files
|
||||
remove_temp_files(tmp_dir)
|
||||
|
||||
|
||||
def main():
|
||||
example_text = """examples:
|
||||
|
||||
# Process single survey at specific beach
|
||||
python las_manipulation.py survey-1-avoca.yaml
|
||||
|
||||
# Process single survey at multiple beaches
|
||||
python las_manipulation.py survey-1-avoca.yaml survey-1-pearl.yaml
|
||||
|
||||
# Process all surveys at specific beach
|
||||
python las_manipulation.py *avoca.yaml
|
||||
|
||||
# Process all beaches for specific survey date
|
||||
python las_manipulation.py survey-1*.yaml
|
||||
"""
|
||||
|
||||
# Set up command line arguments
|
||||
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()
|
||||
|
||||
Loading…
Reference in New Issue