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