Remove 'parse_stdout.py'

etta-drone
Dan Howe 6 years ago
parent 3adc2a84fb
commit e49e5e31d0

@ -1,107 +0,0 @@
import os
import io
import subprocess
import pandas as pd
import matplotlib.pyplot as plt
survey_date = '20180517'
beach = 'Avoca'
output_csv_dir = 'csv'
las_in = 'C:/Users/z3161860/Downloads/LASTools/XXFiles/S2_Delivery/avoca_20180517.las'
cp_in = 'C:/Users/z3161860/Downloads/LASTools/XXFiles/CC_Profiles/Avoca_profiles.csv'
def extract_pts(las_in, cp_in, survey_date, keep_only_ground=True):
"""Extract elevations from a las surface based on x and y coordinates.
Requires lastools in system path.
Args:
las_in: input point cloud (las)
cp_in: point coordinates with columns: id, x, y, z (csv)
survey_date: survey date string, e.g. '19700101'
keep_only_ground: only keep points classified as 'ground' (boolean)
Returns:
Dataframe containing input coordinates with extracted elevations
"""
cmd = ['lascontrol', '-i', las_in, '-cp', cp_in, '-parse', 'sxyz']
if keep_only_ground == True:
cmd += ['-keep_class', '2']
# Call lastools
process = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout, stderr = process.communicate()
errcode = process.returncode
# Handle errors, if detected
if errcode != 0:
print("Error. lascontrol failed on {}".format(
os.path.basename(las_in)))
print(stderr.decode())
# Load result into pandas dataframe
df = pd.read_csv(io.BytesIO(stdout))
# Tidy up dataframe
df = df.drop(columns=['diff'])
df['lidar_z'] = pd.to_numeric(df['lidar_z'], errors='coerce')
df['Beach'] = beach
df = df[[
'Beach', 'ProfileNum', 'Easting', 'Northing', 'Chainage', 'lidar_z'
]]
# Rename columns
new_names = {
'ProfileNum': 'Profile',
'lidar_z': 'Elevation_{}'.format(survey_date),
}
df = df.rename(columns=new_names)
return df
def update_survey_output(df, output_dir):
"""Update survey profile output csv files with current survey.
Args:
df: dataframe containing current survey elevations
output_dir: directory where csv files are saved
Returns:
None
"""
# Merge current survey with existing data
profiles = df['Profile'].unique()
for profile in profiles:
csv_name = os.path.join(output_csv_dir, profile + '.csv')
# Extract survey data for current profile
current_profile = df[df['Profile'] == profile]
try:
# Load existing results
master = pd.read_csv(csv_name)
except FileNotFoundError:
master = current_profile.copy()
# Add (or update) current survey
current_survey_col = df.columns[-1]
master[current_survey_col] = current_profile[current_survey_col]
# Export updated results
master.to_csv(csv_name)
df = extract_pts(las_in, cp_in, survey_date, keep_only_ground=True)
update_survey_output(df, output_csv_dir)
master.shape
current_profile.shape
df.shape
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