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98 lines
2.8 KiB
Python
98 lines
2.8 KiB
Python
import os
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import io
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import subprocess
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import pandas as pd
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import matplotlib.pyplot as plt
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survey_date = '20180517'
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beach = 'Avoca'
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output_csv_dir = 'csv'
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las_in = 'C:/Users/z3161860/Downloads/LASTools/XXFiles/S2_Delivery/avoca_20180517.las'
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cp_in = 'C:/Users/z3161860/Downloads/LASTools/XXFiles/CC_Profiles/Avoca_profiles.csv'
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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 update_survey_output(df, output_dir):
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"""Update survey profile output csv files with current survey.
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Args:
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df: dataframe containing current survey elevations
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output_dir: directory where csv files are saved
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Returns:
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None
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"""
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# Merge current survey with existing data
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profiles = df['Profile'].unique()
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for profile in profiles:
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csv_name = os.path.join(output_csv_dir, profile + '.csv')
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try:
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# Load existing results
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master = pd.read_csv(csv_name)
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except FileNotFoundError:
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master = df.copy()
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# Add (or update) current survey
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current_survey_col = df.columns[-1]
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master[current_survey_col] = df[current_survey_col]
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# Export updated results
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master.to_csv(csv_name)
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df = extract_pts(las_in, cp_in, survey_date, keep_only_ground=True)
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update_survey_output(df, output_csv_dir)
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