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381 lines
15 KiB
Python
381 lines
15 KiB
Python
# python 3.5
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#requires LAStools to be installed (with the appropriate license). Note that LAStools requires no spaces in file names
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#should have previously run 2017088_las_manipulation to have a las that has the buildings and veg removed
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#note that the neilson volumes script must be in the same folder
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# this script will:
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#crop to a given polygon (crop away the swash zone)
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# extract values along a predefined profile,
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# do the volume analysis
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#export pngs of the surveys
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########################### IMPORTS ###########################################
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import os
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import subprocess
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import pandas as pd
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import numpy as np
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import neilson_volumes
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import matplotlib.pyplot as plt
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from matplotlib.ticker import MultipleLocator
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import datetime
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import xlsxwriter
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import math
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from cycler import cycler
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###############################################################################
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########################## FIXED INPUTS #######################################
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######### UNCOMMENT THIS SECTION IF YOU WANT TO DEFINE EACH INPUT INDIVIDUALLY ######################
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##path to LasTools NOTE THERE CAN BE NO SPACES
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path_2_lastools='C:/ProgramData/chocolatey'
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# input_file=r"J:\Project\wrl2017088 Central Coast Council Aerial Survey and Coastal Analysis\04_Working\Python\Survey 2\Parameter Files\las outputs survey2.xlsx"
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input_file='Parameter Files/las outputs survey2.xlsx'
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#input_file=r"J:\Project\wrl2017088 Central Coast Council Aerial Survey and Coastal Analysis\04_Working\Python\Survey 2\Parameter Files\las outputs survey2_V2.xlsx"
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##############################
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############################### SUB ROUTINES ##################################
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def check_output(command,console):
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if console == True:
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process = subprocess.Popen(command)
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else:
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process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
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output,error = process.communicate()
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returncode = process.poll()
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return returncode,output
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def crop_las(las, shp, output, lastools_loc):
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# output is the full path and filename (inc extension) to put in
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path_2_lasclip=lastools_loc+"\\bin\\lasclip"
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command="%s -i %s -poly %s -o %s" % (path_2_lasclip, las, shp, output)
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returncode,output = check_output(command, False)
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if returncode!= 0:
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print("Error. lasclip failed on %s" % shp.split('//')[-1].split('.')[0])
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else:
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return None
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def las_boundary(las, crop_poly_name, path_2_crop_polygon, lastools_loc, zone):
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path_2_lasboundary=lastools_loc+"\\bin\\lasboundary"
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fname=crop_poly_name
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prjfname="%s%s.prj" %(path_2_crop_polygon, fname)
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path_2_crop_poly='%s%s.shp' % (path_2_crop_polygon, fname)
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command="%s -i %s -o %s" % (path_2_lasboundary, las, path_2_crop_poly)
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prj=open(prjfname, 'w')
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if zone==56:
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prj.write('PROJCS["GDA_1994_MGA_Zone_56",GEOGCS["GCS_GDA_1994",DATUM["D_GDA_1994",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",10000000.0],PARAMETER["Central_Meridian",153.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]')
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elif zone==55:
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prj.write('PROJCS["GDA_1994_MGA_Zone_55",GEOGCS["GCS_GDA_1994",DATUM["D_GDA_1994",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",10000000.0],PARAMETER["Central_Meridian",147.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]')
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elif zone==57:
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prj.write('PROJCS["GDA_1994_MGA_Zone_57",GEOGCS["GCS_GDA_1994",DATUM["D_GDA_1994",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",10000000.0],PARAMETER["Central_Meridian",159.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]')
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prj.close()
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returncode,output = check_output(command, False)
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return None
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def make_raster(las, output, lastools_loc, keep_only_ground=False, step=0.2):
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#not that keep ground only option only rasters points classified as "2" in the lidar (ie ground)
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#this effectively creates a "bare earth dem"
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#note that this should only be used after remove_veg and/or remove_buildings has been run
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path_2_blastdem=lastools_loc+"\\bin\\blast2dem"
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if keep_only_ground==False:
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command="%s -i %s -o %s -step %s" % (path_2_blastdem, las, output, step)
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else:
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command="%s -i %s -o %s -step %s -keep_class 2" % (path_2_blastdem, las, output,step)
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returncode,output2 = check_output(command, False)
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if returncode!= 0:
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print("Error. blast2dem failed on %s" % las.split('\\')[-1].split('.')[0])
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else:
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return None
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def extract_pts(las_in, cp_in, survey_date, keep_only_ground=True):
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"""Extract elevations from a las surface based on x and y coordinates.
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Requires lastools in system path.
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Args:
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las_in: input point cloud (las)
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cp_in: point coordinates with columns: id, x, y, z (csv)
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survey_date: survey date string, e.g. '19700101'
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keep_only_ground: only keep points classified as 'ground' (boolean)
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Returns:
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Dataframe containing input coordinates with extracted elevations
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"""
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cmd = ['lascontrol', '-i', las_in, '-cp', cp_in, '-parse', 'sxyz']
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if keep_only_ground == True:
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cmd += ['-keep_class', '2']
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# Call lastools
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process = subprocess.Popen(
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cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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stdout, stderr = process.communicate()
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errcode = process.returncode
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# Handle errors, if detected
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if errcode != 0:
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print("Error. lascontrol failed on {}".format(
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os.path.basename(las_in)))
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print(stderr.decode())
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# Load result into pandas dataframe
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df = pd.read_csv(io.BytesIO(stdout))
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# Tidy up dataframe
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df = df.drop(columns=['diff'])
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df['lidar_z'] = pd.to_numeric(df['lidar_z'], errors='coerce')
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df['Beach'] = beach
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df = df[[
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'Beach', 'ProfileNum', 'Easting', 'Northing', 'Chainage', 'lidar_z'
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]]
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# Rename columns
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new_names = {
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'ProfileNum': 'Profile',
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'lidar_z': 'Elevation_{}'.format(survey_date),
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}
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df = df.rename(columns=new_names)
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return df
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def plot_profiles(profile_info, profile, output_loc, LL_limit):
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#plot the profile. expects output from CC_split_profile
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YminorLocator=MultipleLocator(0.5)
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XminorLocator=MultipleLocator(5)
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fig,ax=plt.subplots(figsize=(8, 3))
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num_plots=len(profile_info.keys())-1
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colormap = plt.cm.jet
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ax.set_prop_cycle(cycler('color', [colormap(i) for i in np.linspace(0, 0.9, num_plots)]))
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max_y=0
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for date in profile_info.keys():
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if date!='info':
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plt.plot(profile_info[date]['Chainage'], profile_info[date]['Elevation'], label=date)
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try:
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if max([i for i in profile_info[date]['Elevation'] if pd.isnull(i)==False])>max_y:
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max_y=max([i for i in profile_info[date]['Elevation'] if pd.isnull(i)==False])
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except:
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print("empty elevation section for %s" % date)
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plt.plot([LL_limit,LL_limit], [-1,max_y], 'r--', alpha=0.5, label="Landward Limit")
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plt.xlabel('Chainage (m)',weight='bold')
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plt.ylabel('Elevation (m AHD)',weight='bold')
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plt.legend(loc='upper right', bbox_to_anchor=(1.3,1))
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plt.title(profile)
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plt.rcParams['font.size']=8
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ax.set_ylim([-1,math.ceil(max_y)])
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ax.xaxis.set_minor_locator(XminorLocator)
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ax.yaxis.set_minor_locator(YminorLocator)
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ax.xaxis.grid(True, which='minor', color='k', linestyle='-', alpha=0.3)
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ax.yaxis.grid(True,which='minor',color='k', linestyle='-', alpha=0.3)
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plt.grid(which='major', color='k', linestyle='-')
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today=datetime.datetime.now().date().strftime('%Y%m%d')
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plt.savefig(os.path.join(output_loc, '%s_%s.png' % (today, profile)),bbox_inches='tight',dpi=900)
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plt.clf()
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return None
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def CC_split_profile(file2read):
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# this reads the profile files and splits it into dates
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file_master=pd.read_csv(file2read)
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beach_original=file_master['Beach'].tolist()
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profile_original=file_master['Profile'].tolist()
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date_original=file_master['Date'].tolist()
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chainage_original=file_master['Chainage'].tolist()
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elevation_original=file_master['Elevation'].tolist()
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easting_original=file_master['Easting'].tolist()
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northing_original=file_master['Northing'].tolist()
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data={}
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i=0
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#add info on the beach and profile number
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data['info']={'Profile':profile_original[0], 'Beach':beach_original[0]}
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date_now=date_original[0]
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while i<len(file_master):
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chainage_tmp=[]
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elevation_tmp=[]
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easting_tmp=[]
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northing_tmp=[]
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while i<len(file_master) and date_now==date_original[i]:
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chainage_tmp.append(chainage_original[i])
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elevation_tmp.append(elevation_original[i])
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easting_tmp.append(easting_original[i])
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northing_tmp.append(northing_original[i])
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i=i+1
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data[date_now]={'Beach': beach_original[i-1], 'Profile':profile_original[i-1],'Easting': easting_tmp, 'Northing':northing_tmp, 'Elevation':elevation_tmp, 'Chainage':chainage_tmp}
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if i<len(file_master):
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date_now=date_original[i]
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return data
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def profile_plots_volume(csv_loc, LL_xlsx, output_xlsx, graph_location):
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#get a list of all csvs which will each be analysed
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file_list=[]
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for file in os.listdir(csv_loc):
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if file.endswith(".csv"):
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file_list.append(os.path.join(csv_loc, file))
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#now read the LL file
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LL_limit_file=pd.read_excel(LL_xlsx, 'profile_locations')
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LL_info={}
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for i in range(0, len(LL_limit_file)):
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#make a dictionary that alllows you to search the LL
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prof="%s_%s" % (LL_limit_file['Profile'][i].split(" ")[0], LL_limit_file['Profile'][i].split(" ")[-1])
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LL_info[prof]=LL_limit_file['Landward Limit'][i]
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all_dates=[]
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results_volume={}
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for file in file_list:
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#read the profile data - this should have all dates
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profile_data=CC_split_profile(file)
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profile=profile_data['info']['Profile']
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#plot all of the profiles
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print(profile)
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plot_profiles(profile_data, profile, graph_location,LL_info[profile])
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results_volume[profile]={}
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#nowgo through each date and do a neilson volume calculations
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for date in profile_data.keys():
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if date!='info':
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if date not in all_dates:
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all_dates.append(date)
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chainage=profile_data[date]['Chainage']
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elevation=[0 if pd.isnull(profile_data[date]['Elevation'][i]) else profile_data[date]['Elevation'][i] for i in range(0, len(profile_data[date]['Elevation']))]
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LL_limit=LL_info[profile]
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#do a neilson calculation to get the ZSA volume
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if len(elevation)>2:
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#if there aren't enough available points don't do it
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volume=neilson_volumes.volume_available(chainage, elevation, LL_limit)
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if volume<0:
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volume=0
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print('%s %s has a negative volume available' % (profile, date))
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else:
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volume=0
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results_volume[profile][date]=volume
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#write an excel sheet which summarises the data
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workbook = xlsxwriter.Workbook(output_xlsx)
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worksheet=workbook.add_worksheet('Volumes')
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row=0
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col=0
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worksheet.write(row, col, 'Profile')
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for date in all_dates:
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col=col+1
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worksheet.write(row, col, date)
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col=0
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row=1
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for prof in results_volume.keys():
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worksheet.write(row, col, prof)
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for date in all_dates:
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col=col+1
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try:
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vol=results_volume[prof][date]
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except KeyError:
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print("error with profile %s on %s" % (prof, date))
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vol=None
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worksheet.write(row, col, vol)
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col=0
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row=row+1
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return results_volume
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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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###############################################################################
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########################### RUN CODE ##########################################
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params_file=pd.read_excel(input_file, sheet_name="PARAMS")
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for i in range(0, len(params_file)): #0, len(params_file)
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print("Starting to process %s" % params_file['Beach'][i])
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beach=params_file['Beach'][i]
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survey_date=params_file['SURVEY DATE'][i]
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input_las5=params_file['INPUT LAS4'][i]
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crop_swash_poly=params_file['CROP SWASH POLY'][i]
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heatmap_crop_poly=params_file['HEATMAP CROP POLY'][i]
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final_las = params_file['FINAL LAS'][i]
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heatmap_las = params_file['HEATMAP LAS'][i]
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zone_MGA=params_file['ZONE MGA'][i]
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output_poly_name=params_file['OUTPUT POLY NAME'][i]
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path_2_output_poly=params_file['PATH TO OUTPUT'][i]
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output_raster=params_file['OUTPUT RASTER'][i]
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input_csv=params_file['INPUT CSV'][i]
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tmp_csv = params_file['TMP CSV'][i]
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LL_file=params_file['LL FILE'][i]
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csv_loc=params_file['OUT CSV LOC'][i]
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graph_loc = params_file['GRAPH LOC'][i]
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volume_output=params_file['VOLUME OUTPUT'][i]
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tmp_dir=params_file['TEMP DIR'][i]
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int_dir=params_file['INTERIM DIR'][i]
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# crop and get the output las
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crop_las(input_las5, crop_swash_poly, final_las, path_2_lastools)
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#now crop out the heatmap las
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crop_las(final_las, heatmap_crop_poly, heatmap_las, path_2_lastools)
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#create a polygon to crop a raster
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las_boundary(heatmap_las, output_poly_name, path_2_output_poly, path_2_lastools, zone_MGA)
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#make a raster
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make_raster(heatmap_las, output_raster, path_2_lastools, keep_only_ground=True)
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#extract the points and get volumes
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df = extract_pts(final_las, input_csv, survey_date, keep_only_ground=True)
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update_survey_output(df, output_csv_dir)
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process_tmp_csv(tmp_csv, survey_date, csv_loc, beach)
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#colourise the point cloud
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#delete the temp files from the tmp_dir and the interim_dir
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remove_temp_files(tmp_dir)
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#remove_temp_files(int_dir)
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print("doing the volume analysis")
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test=profile_plots_volume(csv_loc, LL_file, volume_output, graph_loc)
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