example_usage.py

from gef_parser import read_gef_file, read_alt_gef_file
from utils.pytess_main import pytess_triangulate, pytess_voronoi # original code from pytess, see https://github.com/karimbahgat/Pytess
from dotenv import load_dotenv
from os import getenv
from glob import glob

if __name__ == '__main__':
    # Load environment variable (paths, etc.)
    load_dotenv("./.env")
    
    ############
    # Example 1: Parse a single file
    if(getenv("RUN_EXAMPLE1") != "FALSE"):
        file_path = './sample_cptu_file.GEF.txt'
        cpt_header, measurement_columns_and_units, measurements = read_gef_file(file_path)
        
        print(measurements)
        print(cpt_header)
        print(measurement_columns_and_units)

    ############
    # Example 2: Parse a single file and create a pandas dataframe from the measurements
    if(getenv("RUN_EXAMPLE2") != "FALSE"):
        import pandas as pd
        import matplotlib.pyplot as plt

        file_path = './sample_cptu_file.GEF.txt'
        cpt_header, measurement_columns_and_units, measurements = read_gef_file(file_path)

        # ## Create a pandas df from it and plot qc vs. depth
        df = pd.DataFrame.from_dict(measurements)
        
        # ## Data can easily be plotted
        plt.plot(df['qc'], df['Tiefe'])
        plt.gca().invert_yaxis()
        plt.xlabel("qc [MPa]")
        plt.ylabel("Depth [m]")
        plt.show()




    ############
    # Example 3: Read all .gef.txt files in a folder and create a big pandas df of the measurement data
    if(getenv("RUN_EXAMPLE3") != "FALSE"):
        from glob import glob
        folder_path = './GEF_SAMPLES/**/*.GEF.txt'
        all_measurements = []
        all_cpt_header_info = []
        for file_path in glob(folder_path):
            cpt_header, measurement_columns_and_units, measurements = read_gef_file(file_path)
            measurements = [dict(item, **{'aufschluss_name':cpt_header['aufschluss_name']}) for item in measurements] # add aufschluss_name to the measurements
            all_measurements += measurements
            all_cpt_header_info.append(cpt_header)

        ## optionally add them to a pd.Dataframe and export it to a single excel file
        import pandas as pd
        header_df = pd.DataFrame.from_dict(all_cpt_header_info)
        measurements_df = pd.DataFrame.from_dict(all_measurements)
        excel_outpath = './CPT_Summary.xlsx'

        # depending on the ampount of cpt measurement data the excel file can take some time
        with pd.ExcelWriter(excel_outpath) as writer:
            header_df.to_excel(writer, sheet_name="Sheet0_cpt_header_info", index=False)
            measurements_df.to_excel(writer, sheet_name="Sheet1_cpt_measurements", index=False)

        # optionally export the data into two csv files that can be imported into Leapfrog Works
        header_df.to_csv('collar.csv')
        measurements_df.to_csv('intervals.csv')


    ############
    # Example 4: Create Thiessen Polygons and a Delauny triangulation from CPT xyz-Coordinates
    if(getenv("RUN_EXAMPLE4") != "FALSE"):
        from glob import glob
        folder_path = getenv("EXAMPLE_4_FOLDER_PATH")
        all_measurements = []
        all_cpt_header_info = []
        for file_path in glob(folder_path):
            cpt_header, measurement_columns_and_units, measurements = read_gef_file(file_path)
            measurements = [dict(item, **{'aufschluss_name':cpt_header['aufschluss_name']}) for item in measurements] # add aufschluss_name to the measurements
            all_measurements += measurements
            all_cpt_header_info.append(cpt_header)

        # Create Delauny and Thiessen Polygons
        default_points = [(0,0,0),(5,5,0),(5,0,0), (10,3,2)]
        points = [(cpt.get('RW'), cpt.get('HW')) for cpt in all_cpt_header_info if(cpt.get('RW') is not None and cpt.get('HW') is not None)]
        if(len(points)==0):
            points = default_points

        triangle_polygons = pytess_triangulate(points)
        print(triangle_polygons)

        thiessen_polygons = pytess_voronoi(points, buffer_percent=100)
        print(thiessen_polygons)

    ############
    # Example 5: Read all .gef files in a folder and create a big pandas df of the measurement data  
    if(getenv("RUN_EXAMPLE5") != "FALSE"):
        folder_path = getenv("EXAMPLE_5_FOLDER_PATH") #'./GEF_SAMPLES/**/*.gef'
        all_measurements = []
        all_cpt_header_info = []
        for file_path in glob(folder_path):
            cpt_header, measurement_columns_and_units, measurements = read_alt_gef_file(file_path=file_path)

            measurements = [dict(item, **{'hole_id':cpt_header['TESTID']}) for item in measurements] # add aufschluss_name to the measurements
            all_measurements += measurements
            all_cpt_header_info.append(cpt_header)

        ## optionally add them to a pd.Dataframe and export it to a single excel file
        import pandas as pd
        header_df = pd.DataFrame.from_dict(all_cpt_header_info)
        measurements_df = pd.DataFrame.from_dict(all_measurements)
        excel_outpath = './output_data/Example_5_CPT_Summary.xlsx'

        # depending on the ampount of cpt measurement data the excel file can take some time
        with pd.ExcelWriter(excel_outpath) as writer:
            header_df.to_excel(writer, sheet_name="Sheet0_cpt_header_info", index=False)
            measurements_df.to_excel(writer, sheet_name="Sheet1_cpt_measurements", index=False)

        # optionally export the data into two csv files that can be imported into Leapfrog Works
        header_df.to_csv('./output_data/example_5_collar.csv')
        measurements_df.to_csv('./output_data/example_5_intervals.csv')