central-coast-aerial-survey/outputs_2017088_Survey2.py

# python 3.5
#requires LAStools to be installed (with the appropriate license). Note that LAStools requires no spaces in file names

#should have previously run 2017088_las_manipulation to have a las that has the buildings and veg removed
#note that the neilson volumes script must be in the same folder

# this script will:
#crop to a given polygon (crop away the swash zone)
# extract values along a predefined profile,
# do the volume analysis
#export pngs of the surveys
########################### IMPORTS ###########################################
import os
import io
import subprocess
import pandas as pd
import numpy as np
import neilson_volumes
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
import datetime
import xlsxwriter
import math
from cycler import cycler

from survey_tools import call_lastools, extract_pts, update_survey_output


def plot_profiles(profile_info, profile, output_loc, LL_limit):
    #plot the profile.  expects output from CC_split_profile

    YminorLocator=MultipleLocator(0.5)
    XminorLocator=MultipleLocator(5)


    fig,ax=plt.subplots(figsize=(8, 3))

    num_plots=len(profile_info.keys())-1
    colormap = plt.cm.jet
    ax.set_prop_cycle(cycler('color', [colormap(i) for i in np.linspace(0, 0.9, num_plots)]))

    max_y=0
    for date in profile_info.keys():
        if date!='info':

            plt.plot(profile_info[date]['Chainage'], profile_info[date]['Elevation'], label=date)
            try:
                if max([i for i in profile_info[date]['Elevation'] if pd.isnull(i)==False])>max_y:
                    max_y=max([i for i in profile_info[date]['Elevation'] if pd.isnull(i)==False])
            except:
                print("empty elevation section for  %s" %  date)


    plt.plot([LL_limit,LL_limit], [-1,max_y], 'r--', alpha=0.5, label="Landward Limit")


    plt.xlabel('Chainage (m)',weight='bold')
    plt.ylabel('Elevation (m AHD)',weight='bold')
    plt.legend(loc='upper right', bbox_to_anchor=(1.3,1))
    plt.title(profile)
    plt.rcParams['font.size']=8

    ax.set_ylim([-1,math.ceil(max_y)])

    ax.xaxis.set_minor_locator(XminorLocator)
    ax.yaxis.set_minor_locator(YminorLocator)
    ax.xaxis.grid(True, which='minor', color='k', linestyle='-', alpha=0.3)
    ax.yaxis.grid(True,which='minor',color='k', linestyle='-', alpha=0.3)

    plt.grid(which='major', color='k', linestyle='-')
    today=datetime.datetime.now().date().strftime('%Y%m%d')
    plt.savefig(os.path.join(output_loc, '%s_%s.png' % (today, profile)),bbox_inches='tight',dpi=900)
    plt.clf()

    return None


def CC_split_profile(file2read):
    # this reads the profile files and splits it into dates

    file_master=pd.read_csv(file2read)

    beach_original=file_master['Beach'].tolist()
    profile_original=file_master['Profile'].tolist()
    date_original=file_master['Date'].tolist()
    chainage_original=file_master['Chainage'].tolist()
    elevation_original=file_master['Elevation'].tolist()
    easting_original=file_master['Easting'].tolist()
    northing_original=file_master['Northing'].tolist()

    data={}
    i=0

    #add info on the beach and profile number
    data['info']={'Profile':profile_original[0], 'Beach':beach_original[0]}
    date_now=date_original[0]

    while i<len(file_master):
        chainage_tmp=[]
        elevation_tmp=[]
        easting_tmp=[]
        northing_tmp=[]
        while i<len(file_master) and date_now==date_original[i]:
            chainage_tmp.append(chainage_original[i])
            elevation_tmp.append(elevation_original[i])
            easting_tmp.append(easting_original[i])
            northing_tmp.append(northing_original[i])
            i=i+1

        data[date_now]={'Beach': beach_original[i-1], 'Profile':profile_original[i-1],'Easting': easting_tmp, 'Northing':northing_tmp, 'Elevation':elevation_tmp, 'Chainage':chainage_tmp}

        if i<len(file_master):
            date_now=date_original[i]

    return data

def profile_plots_volume(csv_loc, LL_xlsx, output_xlsx, graph_location):
    #get a list of all csvs which will each be analysed
    file_list=[]
    for file in os.listdir(csv_loc):
        if file.endswith(".csv"):
            file_list.append(os.path.join(csv_loc, file))

    #now read the LL file
    LL_limit_file=pd.read_excel(LL_xlsx, 'profile_locations')
    LL_info={}
    for i in range(0, len(LL_limit_file)):
        #make a dictionary that alllows you to search the LL
        prof="%s_%s" % (LL_limit_file['Profile'][i].split(" ")[0], LL_limit_file['Profile'][i].split(" ")[-1])

        LL_info[prof]=LL_limit_file['Landward Limit'][i]

    all_dates=[]
    results_volume={}
    for file in file_list:
        #read the profile data - this should have all dates
        profile_data=CC_split_profile(file)
        profile=profile_data['info']['Profile']

        #plot all of the profiles
        print(profile)
        plot_profiles(profile_data, profile, graph_location,LL_info[profile])

        results_volume[profile]={}

        #nowgo through each date and do a neilson volume calculations
        for date in profile_data.keys():
            if date!='info':
                if date not in all_dates:
                    all_dates.append(date)

                chainage=profile_data[date]['Chainage']
                elevation=[0 if pd.isnull(profile_data[date]['Elevation'][i]) else profile_data[date]['Elevation'][i] for i in range(0, len(profile_data[date]['Elevation']))]
                LL_limit=LL_info[profile]
                #do a neilson calculation to get the ZSA volume
                if len(elevation)>2:
                    #if there aren't enough available points don't do it
                    volume=neilson_volumes.volume_available(chainage, elevation, LL_limit)
                    if volume<0:
                        volume=0
                        print('%s %s has a negative volume available' % (profile, date))
                else:
                    volume=0

                results_volume[profile][date]=volume

    #write an excel sheet which summarises the data
    workbook = xlsxwriter.Workbook(output_xlsx)
    worksheet=workbook.add_worksheet('Volumes')

    row=0
    col=0

    worksheet.write(row, col, 'Profile')
    for date in all_dates:
        col=col+1
        worksheet.write(row, col, date)

    col=0
    row=1

    for prof in results_volume.keys():

        worksheet.write(row, col, prof)

        for date in all_dates:
            col=col+1
            try:
                vol=results_volume[prof][date]
            except KeyError:
                print("error with profile %s on %s" % (prof, date))
                vol=None
            worksheet.write(row, col, vol)
        col=0
        row=row+1
    return results_volume

def remove_temp_files(directory):
    for f in os.listdir(directory):
        os.unlink(os.path.join(directory, f))

    return None

input_file = 'Parameter Files/las-manipulation-survey-2.xlsx'
params_file=pd.read_excel(input_file, sheet_name="PARAMS")

for i in range(0, len(params_file)): #0, len(params_file)
    print("Starting to process %s" % params_file['Beach'][i])
    beach=params_file['Beach'][i]
    survey_date=params_file['SURVEY DATE'][i]
    original_las=params_file['INPUT LAS'][i]
    classified_las_dir=params_file['LAS CLASSIFIED FOLDER'][i]
    shp_swash_dir=params_file['SHP SWASH FOLDER'][i]
    crop_heatmap_poly=params_file['HEATMAP CROP POLY'][i]
    output_las_dir=params_file['LAS OUTPUT FOLDER'][i]
    zone_MGA=params_file['ZONE MGA'][i]
    output_poly_dir=params_file['SHP RASTER FOLDER'][i]
    output_tif_dir=params_file['TIF OUTPUT FOLDER'][i]
    cp_csv=params_file['INPUT CSV'][i]
    profile_limit_file=params_file['PROFILE LIMIT FILE'][i]
    csv_output_dir=params_file['CSV OUTPUT FOLDER'][i]
    graph_loc = params_file['PNG OUTPUT FOLDER'][i]
    volume_output=params_file['CSV VOLUMES FOLDER'][i]
    tmp_dir=params_file['TMP FOLDER'][i]

    # Get base name of input las
    las_basename = os.path.splitext(os.path.basename(original_las))[0]

    # Get name of input point cloud
    input_las = os.path.join(classified_las_dir, las_basename + '.las')

    # Get name of swash cropping polygon
    crop_swash_poly = os.path.join(shp_swash_dir, las_basename + '.shp')

    # Crop point cloud to swash boundary
    las_data = call_lastools('lasclip', input=input_las, output='-stdout',
                             args=['-poly', crop_swash_poly], verbose=False)
    # crop_las(input_las5, crop_swash_poly, final_las, path_2_lastools)

    # Apply sea-side clipping polygon
    las_data = call_lastools('lasclip', input=las_data, output='-stdout',
                             args=['-poly', crop_heatmap_poly], verbose=False)
    # crop_las(final_las, heatmap_crop_poly, heatmap_las, path_2_lastools)

    # Create clipping polygon for heatmap raster
    shp_name = os.path.join(output_poly_dir, las_basename + '.shp')
    call_lastools('lasboundary', input=las_data, output=shp_name, verbose=False)
    # las_boundary(heatmap_las, output_poly_name, output_poly_dir, path_2_lastools, zone_MGA)

    #make a raster
    # make_raster(heatmap_las, output_raster, path_2_lastools, keep_only_ground=True)
    tif_name = os.path.join(output_tif_dir, las_basename + '.tif')
    call_lastools('blast2dem', input=las_data, output=tif_name,
                  args=['-step', 0.2], verbose=False)

    #extract the points and get volumes
    df = extract_pts(
        las_data,
        cp_csv,
        survey_date,
        beach,
        args=['-parse', 'sxyz', '-keep_class', '2'],
        verbose=False)
    update_survey_output(df, csv_output_dir)


    #colourise the point cloud


    #delete the temp files from the tmp_dir and the interim_dir
    remove_temp_files(tmp_dir)
    #remove_temp_files(int_dir)

print("doing the volume analysis")
test=profile_plots_volume(csv_output_dir, profile_limit_file, volume_output, graph_loc)