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central-coast-aerial-survey
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Allow input files to be specified from the command line

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etta-drone
Dan Howe 6 years ago
parent 8ca180f93f
commit 74c9351fe7
2 changed files with 513 additions and 486 deletions
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1
las_manipulation.py
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@ -360,7 +360,6 @@ def main():
# read the parameters file and scroll through it
input_file = args.input_file
input_file = 'Parameter Files/las-manipulation-survey-2.xlsx'
params_file=pd.read_excel(input_file, sheet_name="PARAMS")
for i, row in params_file.iterrows():

172
las_outputs.py
Unescape Escape View File

@ -13,7 +13,9 @@
import os
import io
import re
import sys
import math
import argparse
import datetime
import subprocess
import numpy as np
@ -91,7 +93,7 @@ def calculate_volumes(profile_name, survey_date, csv_output_dir, ch_limits, volu
# Get Nielsen erosion volumes
chainage = profiles.loc[:, current_date].dropna().index
elevation = profiles.loc[:, current_date].dropna().values
volume = neilson_volumes.volume_available(chainage, elevation, ch_min)
volume = nielsen_volumes.volume_available(chainage, elevation, ch_min)
# Update spreadsheet
volumes.loc[profile_name, date] = volume
@ -101,74 +103,100 @@ def calculate_volumes(profile_name, survey_date, csv_output_dir, ch_limits, volu
volumes.to_csv(csv_vol)
input_file = 'Parameter Files/las-manipulation-survey-2.xlsx'
params_file=pd.read_excel(input_file, sheet_name="PARAMS")
for i, row in params_file.iterrows():
print("Starting to process %s" % row['Beach'])
beach=row['Beach']
survey_date = row['SURVEY DATE']
original_las = row['INPUT LAS']
classified_las_dir = row['LAS CLASSIFIED FOLDER']
shp_swash_dir = row['SHP SWASH FOLDER']
crop_heatmap_poly = row['HEATMAP CROP POLY']
output_las_dir = row['LAS OUTPUT FOLDER']
zone_MGA = row['ZONE MGA']
output_poly_dir = row['SHP RASTER FOLDER']
output_tif_dir = row['TIF OUTPUT FOLDER']
cp_csv = row['INPUT CSV']
profile_limit_file = row['PROFILE LIMIT FILE']
csv_output_dir = row['CSV OUTPUT FOLDER']
graph_loc = row['PNG OUTPUT FOLDER']
volume_output_dir = row['CSV VOLUMES FOLDER']
tmp_dir = row['TMP FOLDER']
# 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)
# Apply sea-side clipping polygon
las_data = call_lastools('lasclip', input=las_data, output='-stdout',
args=['-poly', crop_heatmap_poly], verbose=False)
# 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)
# Make a raster from point cloud
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 elevations along profiles from triangulated surface
df = extract_pts(
las_data,
cp_csv,
survey_date,
beach,
args=['-parse', 'sxyz', '-keep_class', '2'],
verbose=False)
# Update survey profiles
update_survey_output(df, csv_output_dir)
# Get landward limit of surveys
ch_limits = pd.read_excel(profile_limit_file, index_col='Profile')
# Plot profiles, and save sand volumes for current beach
profile_names = df['Profile'].unique()
for profile_name in profile_names:
plot_profiles(profile_name, survey_date, csv_output_dir, graph_loc, ch_limits)
calculate_volumes(profile_name, survey_date, csv_output_dir, ch_limits, volume_output_dir)
# Remove temprary files
remove_temp_files(tmp_dir)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'input_file',
metavar='PARAMS_FILE',
help='name of parameter file',
default=None)
# Print usage if no arguments are provided
if len(sys.argv) == 1:
parser.print_help(sys.stderr)
sys.exit(1)
args = parser.parse_args()
# read the parameters file and scroll through it
input_file = args.input_file
params_file=pd.read_excel(input_file, sheet_name="PARAMS")
for i, row in params_file.iterrows():
print("Starting to process %s" % row['Beach'])
beach=row['Beach']
survey_date = row['SURVEY DATE']
original_las = row['INPUT LAS']
classified_las_dir = row['LAS CLASSIFIED FOLDER']
shp_swash_dir = row['SHP SWASH FOLDER']
crop_heatmap_poly = row['HEATMAP CROP POLY']
output_las_dir = row['LAS OUTPUT FOLDER']
zone_MGA = row['ZONE MGA']
output_poly_dir = row['SHP RASTER FOLDER']
output_tif_dir = row['TIF OUTPUT FOLDER']
cp_csv = row['INPUT CSV']
profile_limit_file = row['PROFILE LIMIT FILE']
csv_output_dir = row['CSV OUTPUT FOLDER']
graph_loc = row['PNG OUTPUT FOLDER']
volume_output_dir = row['CSV VOLUMES FOLDER']
tmp_dir = row['TMP FOLDER']
# 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
print('Cropping swash...')
las_data = call_lastools('lasclip', input=input_las, output='-stdout',
args=['-poly', crop_swash_poly], verbose=False)
# Apply sea-side clipping polygon
print('Cropping back of beach...')
las_data = call_lastools('lasclip', input=las_data, output='-stdout',
args=['-poly', crop_heatmap_poly], verbose=False)
# Create clipping polygon for heatmap raster
print('Creating heat map cropping polygon...')
shp_name = os.path.join(output_poly_dir, las_basename + '.shp')
call_lastools('lasboundary', input=las_data, output=shp_name, verbose=False)
# Make a raster from point cloud
print('Creating heat map raster...')
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 elevations along profiles from triangulated surface
print('Extracting profile elevations...')
df = extract_pts(
las_data,
cp_csv,
survey_date,
beach,
args=['-parse', 'sxyz', '-keep_class', '2'],
verbose=False)
# Update survey profiles
update_survey_output(df, csv_output_dir)
# Get landward limit of surveys
ch_limits = pd.read_excel(profile_limit_file, index_col='Profile')
# Plot profiles, and save sand volumes for current beach
print('Updating figures...')
profile_names = df['Profile'].unique()
for profile_name in profile_names:
plot_profiles(profile_name, survey_date, csv_output_dir, graph_loc, ch_limits)
calculate_volumes(profile_name, survey_date, csv_output_dir, ch_limits, volume_output_dir)
# Remove temprary files
remove_temp_files(tmp_dir)
if __name__ == '__main__':
main()

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