d3dtools 0.15.2

A collection of tools for working with shapefiles and converting them for Delft3D modeling

D3DTOOLS

A collection of Python tools for working with shapefiles and converting them for Delft3D modeling.

CAUTION: The ncrain function currently only works for Taiwan data in EPSG:3826 projection.

GDAL Installation: GDAL is required for this package. For conda environments, use conda install gdal to install GDAL. For non-conda environments, download the appropriate wheel file from https://github.com/cgohlke/geospatial-wheels/releases to install GDAL.

Installation

pip install d3dtools

Features

This package provides several utilities for converting shapefiles to various formats used in Delft3D modeling:

• ncrain: Generate a NetCDF file from rainfall data and thiessen polygon shapefiles
• snorain: Process rainfall scenario data and generate time series CSV files
• shp2ldb: Convert boundary line shapefiles to LDB files
• shpbc2pli (alias: shp2pli): Convert boundary line shapefiles to PLI files
• shpblock2pol (alias: shp2pol): Convert shapefile blocks to POL files
• shpdike2pliz (alias: shp2pliz): Convert bankline shapefiles to PLIZ files
• shp2xyz: Convert point shapefiles to XYZ files
• evaluate: Calculate flood simulation accuracy metrics by comparing simulated and observed flood extents
• evaluate_sensor: Calculate flood simulation accuracy metrics by comparing simulated flood extents with point-based sensor data (with configurable buffer radius and depth threshold)
• sensor: Extract time series data from Delft3D FM NetCDF files at observation points
• getfacez: Extract Mesh2d_face_z values (bed level/bathymetry) from Delft3D FM NetCDF files at observation points

Usage Examples

Process and generate rainfall scenario data

from d3dtools import snorain

# Process a scenario rainfall CSV file
snorain.generate(
    input_file='rainfall_scenarios.csv',
    output_folder='custom/TAB',
    verbose=True
)

Generate NetCDF from rainfall data (with unit of mm/hr)

from d3dtools import ncrain

# Default usage - processes first CSV file in the input folder
ncrain.generate()

# With custom parameters
ncrain.generate(
    input_shp_folder='custom/SHP',
    input_tab_folder='custom/TAB',
    output_nc_folder='custom/NC',
    intermediate_ras_folder='custom/RAS_RAIN',
    intermediate_shp_folder='custom/SHP_RAIN',
    clean_intermediate=True,
    raster_resolution=320
)

# Process a specific CSV file
ncrain.generate(
    input_tab_folder='custom/TAB',
    rainfall_file='specific_rainfall.csv',
    verbose=True
)

# Process all CSV files in the input folder
ncrain.generate_all(
    input_shp_folder='custom/SHP',
    input_tab_folder='custom/TAB',
    output_nc_folder='custom/NC',
    verbose=True
)

Convert boundary shapefiles to PLI

from d3dtools import shpbc2pli

# Default usage
shpbc2pli.convert()

# With custom parameters
shpbc2pli.convert(
    input_folder='custom/SHP_BC',
    output_folder='custom/PLI_BC'
)

# With custom ID field name
shpbc2pli.convert(
    input_folder='custom/SHP_BC',
    output_folder='custom/PLI_BC',
    id_field='BoundaryName'  # Use 'BoundaryName' column instead of default 'ID'/'Id'/'id'/'iD'
)

Convert block shapefiles to POL

from d3dtools import shpblock2pol

# Default usage
shpblock2pol.convert()

# With custom parameters
shpblock2pol.convert(
    input_folder='custom/SHP_BLOCK',
    output_folder='custom/POL_BLOCK'
)

Convert dike shapefiles to PLIZ

from d3dtools import shpdike2pliz

# Default usage
shpdike2pliz.convert()

# With custom parameters
shpdike2pliz.convert(
    input_folder='custom/SHP_DIKE',
    output_folder='custom/PLIZ_DIKE',
    output_filename='CustomDike'
)

# With custom ID field name
shpdike2pliz.convert(
    input_folder='custom/SHP_DIKE',
    output_folder='custom/PLIZ_DIKE',
    output_filename='CustomDike',
    id_field='DikeName'  # Use 'DikeName' column instead of default 'ID'/'Id'/'id'/'iD'
)

Convert boundary shapefiles to LDB

from d3dtools import shp2ldb

# Default usage
shp2ldb.convert()

# With custom parameters
shp2ldb.convert(
    input_folder='custom/SHP_LDB',
    output_folder='custom/LDB'
)

# With custom ID field name
shp2ldb.convert(
    input_folder='custom/SHP_LDB',
    output_folder='custom/LDB',
    id_field='BoundaryName'  # Use 'BoundaryName' column instead of default 'ID'/'Id'/'id'/'iD'
)

Convert point shapefiles to XYZ

from d3dtools import shp2xyz

# Default usage
shp2xyz.convert()

# With custom parameters
shp2xyz.convert(
    input_folder='custom/SHP_SAMPLE',
    output_folder='custom/XYZ_SAMPLE'
)

# With custom Z-field name
shp2xyz.convert(
    input_folder='custom/SHP_SAMPLE',
    output_folder='custom/XYZ_SAMPLE',
    z_field='ELEVATION'  # Use 'ELEVATION' column instead of default Z-field detection
)

Extract time series data from NetCDF files

from d3dtools import sensor

# Extract data from NetCDF file at observation points
data = sensor.getdata(
    nc_file='path/to/model_output.nc',
    obs_shp='path/to/observation_points.shp',
    output_csv='water_depth.csv',
    output_excel='water_depth.xlsx',
    plot=True  # Display a plot of the time series
)

# Process the data further if needed
print(data.head())
stats = data.describe().transpose()
print(stats)

Extract Mesh2d_face_z values from NetCDF files

from d3dtools import getfacez

# Extract bed level/bathymetry data from NetCDF file at observation points
data = getfacez.extract_mesh2d_face_z(
    nc_file='path/to/model_output.nc',
    obs_shp='path/to/observation_points.shp',
    output_csv='bathymetry.csv',
    output_excel='bathymetry.xlsx',
    verbose=True  # Display additional information during processing
)

# Process the data further if needed
print(data.head())
print(f"Bathymetry range: {data['Mesh2d_face_z'].min():.3f} to {data['Mesh2d_face_z'].max():.3f}")

Calculate flood simulation accuracy using sensor data

from d3dtools import evaluate_sensor

# Compare simulated flood extents with sensor observations
results = evaluate_sensor.confusion_matrix(
    sim_path='path/to/simulated_flood.shp',
    obs_path='path/to/sensor_observations.shp',
    buffer_radius=30,               # Buffer radius around sensor points in meters (default: 30)
    depth_threshold=30,             # Water depth threshold in centimeters (default: 30)
    output_csv='sensor_accuracy.csv'
)

print(f"Accuracy: {results['accuracy']:.2f}%")
print(f"Recall (Catch Rate): {results['recall']:.2f}%")

Calculate flood simulation accuracy

from d3dtools import evaluate

# Compare simulated and observed flood extents
results = evaluate.confusion_matrix(
    sim_path='path/to/simulated_flood.shp',
    obs_path='path/to/observed_flood.shp',
    output_path='accuracy_results.csv'
)

print(f"Accuracy: {results['accuracy']:.2f}%")
print(f"Recall (Catch Rate): {results['recall']:.2f}%")

Command-line Usage

d3dtools-info: Access Tool Information

The package provides the d3dtools-info command-line utility that serves as a central information hub for all available tools:

# Display the package version
d3dtools-info --version

# Get help on d3dtools-info itself
d3dtools-info --help

# Display description of all available tools
d3dtools-info

# Display detailed information about a specific tool
d3dtools-info ncrain
d3dtools-info snorain
d3dtools-info shp2ldb
d3dtools-info shp2pli
d3dtools-info shp2pliz
d3dtools-info shp2pol
d3dtools-info shp2xyz
d3dtools-info shpbc2pli
d3dtools-info shpblock2pol
d3dtools-info shpdike2pliz
d3dtools-info sensor
d3dtools-info evaluate
d3dtools-info evaluate_sensor
d3dtools-info getfacez

# Display help for specific tools
ncrain --help
snorain --help
shp2ldb --help
shp2pli --help
shp2pliz --help
shp2pol --help
shp2xyz --help
shpbc2pli --help
shpblock2pol --help
shpdike2pliz --help
sensor --help
evaluate --help
evaluate_sensor --help
getfacez --help

The d3dtools-info tool helps you discover available functionality, learn about tool options, and access usage examples without having to remember all command-line parameters.

The package also provides command-line utilities for each specific tool:

# Generate NetCDF from rainfall data
ncrain                      # Process all CSV files in the input folder
ncrain --shp-folder custom/SHP --tab-folder custom/TAB --nc-folder custom/NC --resolution 320
ncrain --verbose            # Display additional processing information
ncrain --no-clean           # Keep intermediate files
ncrain --single rainfall.csv  # Process only a specific CSV file

# Process rainfall scenario data
snorain -i rainfall_scenarios.csv -o custom/TAB
snorain --input rainfall_scenarios.csv --output custom/TAB --verbose

# Convert boundary shapefiles to LDB
shp2ldb
shp2ldb -i custom/SHP_LDB -o custom/LDB  # Specify input and output folders
shp2ldb --id_field BoundaryName  # Specify custom ID field

# Convert boundary shapefiles to PLI
shpbc2pli  # or use the alias: shp2pli
shpbc2pli --id_field BoundaryName  # Specify custom ID field

# Convert block shapefiles to POL
shpblock2pol  # or use the alias: shp2pol
shpblock2pol -i custom/SHP_BLOCK -o custom/POL_BLOCK  # Specify input and output folders

# Convert dike shapefiles to PLIZ
shpdike2pliz  # or use the alias: shp2pliz
shpdike2pliz --id_field DikeName  # Specify custom ID field

# Convert point shapefiles to XYZ
shp2xyz
shp2xyz -i custom/SHP_SAMPLE -o custom/XYZ_SAMPLE  # Specify input and output folders
shp2xyz --z_field ELEVATION  # Specify custom Z-field name

# Extract time series data at observation points
sensor --nc-file path/to/model_output.nc --obs-shp path/to/observation_points.shp
sensor --nc-file path/to/model_output.nc --obs-shp path/to/observation_points.shp --output-csv water_depth.csv --output-excel water_depth.xlsx --plot
sensor --verbose  # Display additional processing information

# Calculate flood simulation accuracy metrics
evaluate --sim path/to/simulated_flood.shp --obs path/to/observed_flood.shp
evaluate --sim path/to/simulated_flood.shp --obs path/to/observed_flood.shp --output accuracy_results.csv

# Calculate flood simulation accuracy using sensor data
evaluate_sensor --sim path/to/simulated_flood.shp --obs path/to/sensor_points.shp
evaluate_sensor --sim path/to/simulated_flood.shp --obs path/to/sensor_points.shp --buffer 30 --threshold 30 --output sensor_accuracy.csv

# Extract Mesh2d_face_z values at observation points
getfacez --nc-file path/to/model_output.nc --obs-shp path/to/observation_points.shp
getfacez --nc-file path/to/model_output.nc --obs-shp path/to/observation_points.shp --output-csv bathymetry.csv --output-excel bathymetry.xlsx
getfacez --verbose  # Display additional processing information

Requirements

• numpy>=1.20.0
• pandas>=1.3.0
• geopandas>=0.10.0
• rasterio>=1.2.0
• netCDF4>=1.5.0
• pyproj>=3.0.0
• shapely>=1.8.0
• matplotlib>=3.4.0
• openpyxl>=3.0.0

License

MIT