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)
• evaluate_sensor2 (alias: eval_iot): Calculate flood simulation accuracy metrics using sensor data with dual-threshold shapefiles (separate low and high depth threshold simulations)
• 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. Uses a spatial index (shapely STRtree for point-in-polygon matching, scipy cKDTree for nearest-neighbor matching) instead of scanning every mesh face for every observation point, which is much faster on large meshes. Supports -if/--id-field to specify which shapefile field to use for point names, and -p/--project to resolve the NetCDF file from a D-Flow FM .dsproj project instead of passing --nc-file
• getfacez2: Original brute-force implementation of getfacez (no spatial index), kept as a fallback. Same CLI arguments, Python API, and output format as getfacez, including -if/--id-field
• fou2shp: Reconstruct Delft3D FM 2D mesh face polygons from a FOU (Fourier) NetCDF output file and export threshold-filtered shapefiles; supports -r/--remove to remove output polygons that intersect mask shapefiles (filtered copies written to <output-folder>_RM/)
• pliz2shp: Convert Delft3D/D-Flow FM .pliz weir/dike polyline files (with Z) to 3D ESRI Shapefiles
• pli2shp: Convert Delft3D polyline files (.pli/.ldb) to ESRI Shapefiles
• pol2shp: Convert Delft3D/D-Flow FM .pol polygon files to ESRI Shapefiles
• xyz2shp: Convert XYZ point files (.xyz/.csv) to ESRI Shapefiles
• rmgrid: Remove (clear) the 2D computational mesh and 1D2D links from a D-Flow FM .dsproj project while preserving the 1D network (pipes/branches)
• rsgrid: Restore the 2D computational mesh (including Mesh2d_face_z bed levels) into a D-Flow FM .dsproj project by cloning it from a source project, while preserving the target's 1D network. The inverse of rmgrid. Also restores the 2D spatial fields (infiltration capacity, roughness) that are lost along with the mesh, via -f/--fields

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 (spatial-index accelerated)

from d3dtools import getfacez

# Extract bed level/bathymetry data from NetCDF file at observation points.
# Uses an STRtree (point-in-polygon) or cKDTree (nearest neighbor) spatial index
# instead of a per-point full mesh scan, so it stays fast on large meshes.
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',
    id_field='StationName',  # Optional; field to use for point names (default: auto-detect)
    verbose=True  # Display additional information during processing
)

# Alternatively, resolve the NetCDF file from a D-Flow FM project instead of
# passing nc_file. The project's MDU is located under <project>.dsproj_data/ and
# its [geometry] NetFile entry is used. nc_file and project are mutually exclusive.
data = getfacez.extract_mesh2d_face_z(
    project='path/to/MyProject.dsproj',  # Or 'path/to/MyProject', or a directory containing one .dsproj
    obs_shp='path/to/observation_points.shp',
    output_csv='bathymetry.csv',
    output_excel='bathymetry.xlsx',
    verbose=True
)

# 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}")

Extract Mesh2d_face_z values from NetCDF files (original brute-force fallback)

from d3dtools import getfacez2

# Same signature and output as getfacez, but uses the original per-point full mesh
# scan (no spatial index). Kept as a fallback in case the spatial-index approach
# ever misbehaves on unusual mesh data.
data = getfacez2.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',
    id_field='StationName',  # Optional; field to use for point names (default: auto-detect)
    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 using sensor data with dual thresholds

from d3dtools import evaluate_sensor2

# Compare simulated flood extents (low/high threshold) with sensor observations
results = evaluate_sensor2.confusion_matrix(
    low_threshold_sim_path='path/to/simulated_flood_low.shp',
    high_threshold_sim_path='path/to/simulated_flood_high.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_accuracy2.csv'
)

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

Reconstruct FOU mesh faces as threshold shapefiles

# Run via command line (recommended)
# fou2shp --input NC/FlowFM_fou.nc -of SHP
# fou2shp --input NC/FlowFM_fou.nc --var Mesh2d_fourier002_max_depth --output-folder output

# Remove polygons intersecting a mask shapefile; filtered copies go to SHP_RM/
# fou2shp --input NC/FlowFM_fou.nc -r SHP/EXCLUDE.shp
# fou2shp --input NC/FlowFM_fou.nc -r SHP/*.shp
# fou2shp --input NC/FlowFM_fou.nc --remove SHP/ROAD.shp SHP/BUILDING.shp

Convert PLIZ files to Shapefiles

from d3dtools import pliz2shp

# Convert a single .pliz file
pliz2shp.pliz_to_shp(
    input_file='PLIZ/MyDike.pliz',
    output_dir='SHP_LINES3D',       # Optional; default: SHP_LINES3D
    crs='EPSG:3826'                 # Optional; default: EPSG:3826
)

# Batch convert via CLI (recommended for multiple files)
# pliz2shp -i Dike001.pliz
# pliz2shp -if custom/PLIZ -of custom/SHP

Convert PLI/LDB files to Shapefiles

from d3dtools import pli2shp

# Convert a single .pli or .ldb file
pli2shp.polyline_to_shp(
    input_file='PLI/boundary.pli',
    output_dir='SHP_LINES',         # Optional; default: SHP_LINES
    crs='EPSG:3826'                 # Optional; default: EPSG:3826
)

# Batch convert via CLI (recommended for multiple files)
# pli2shp -i boundary.pli
# pli2shp -if custom/PLI -of custom/SHP

Convert POL files to Shapefiles

from d3dtools import pol2shp

# Convert a single .pol file
pol2shp.pol_to_shp(
    input_file='POL/POL_001.pol',
    output_dir='SHP_POLYGONS',      # Optional; default: SHP_POLYGONS
    crs='EPSG:3826'                 # Optional; default: EPSG:3826
)

# Batch convert via CLI (recommended for multiple files)
# pol2shp -i POL_001.pol
# pol2shp -if custom/POL -of custom/SHP

Convert XYZ/CSV point files to Shapefiles

from d3dtools import xyz2shp

# Convert a single .xyz or .csv point file
xyz2shp.xyz_to_shp(
    input_file='XYZ/XYZ_001.xyz',
    output_dir='SHP_XYZ',           # Optional; default: SHP_XYZ
    crs='EPSG:3826',                # Optional; default: EPSG:3826
    dimension='3'                   # Optional; '3' for x,y,z points, '2' for x,y only
)

# Batch convert via CLI (recommended for multiple files)
# xyz2shp -i XYZ_001.xyz
# xyz2shp -if custom/XYZ -of custom/SHP

Remove the 2D mesh from a D-Flow FM project

# Recommended usage via the command line (operates on a .dsproj project)
# rmgrid                                  # Auto-detect the .dsproj in the current folder
# rmgrid -i MyProject.dsproj              # Specify the project explicitly
# rmgrid -i MyProject.dsproj --force-backup  # Overwrite an existing .nc.bak
# rmgrid -i MyProject.dsproj --restore    # Restore the original net file from .nc.bak

The tool empties the 2D mesh in the project's UGRID NetCDF net file while preserving the 1D network (pipes/branches), strips 2D-specific blocks from the IniFieldFile, and creates a <name>.nc.bak backup so the change can be reverted with --restore.

Restore the 2D mesh into a D-Flow FM project

# Recommended usage via the command line (operates on .dsproj projects)
# rsgrid -s Intact.dsproj                   # Restore the mesh into first .dsproj in cwd
# rsgrid -i Stripped.dsproj -s Intact.dsproj
# rsgrid -s source_net.nc                   # Source given directly as a net file
# rsgrid -i target_net.nc -s source_net.nc

# Restore the 2D spatial fields (infiltration capacity, roughness) as well/instead
# rsgrid -f                                 # Restore fields from the current directory
# rsgrid -i Target.dsproj -f -d fields/     # Take the *.xyz files from fields/
# rsgrid -i Target.dsproj -s Intact.dsproj -f   # Mesh first, then the fields
# rsgrid -f -q frictioncoefficient=rough2024.xyz  # Map an oddly named sample file

The tool clones the 2D mesh (including Mesh2d_face_z bed levels) from a source project's net file into the target's net file, keeping the target's own 1D network, coordinate system, and other settings intact. It backs up the target net file with a timestamped copy before overwriting. This is the inverse of rmgrid.

Removing and re-adding a 2D grid also drops the spatial fields that live on it: the initial infiltration capacity and the 2D roughness (friction coefficient), which live in loose *.xyz sample files next to the MDU rather than in the net file. -f/--fields restores these: it copies the *.xyz sample files (default: from the current directory, or -d DIR) into the model's input folder and re-registers them in the MDU (IniFieldFile, FrictFile, and Infiltrationmodel when an infiltration field is present). The project's initialFields.ini is created if it doesn't have one, or updated in place (just the dataFile entries, leaving interpolation/averaging settings alone) if it does. Sample files are matched to a quantity by name; use -q NAME=FILE for files named something else, e.g. -q frictioncoefficient=rough2024.xyz.

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
d3dtools-info -v

# 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 evaluate_sensor2
d3dtools-info eval_iot
d3dtools-info getfacez
d3dtools-info getfacez2
d3dtools-info fou2shp
d3dtools-info pliz2shp
d3dtools-info pli2shp
d3dtools-info pol2shp
d3dtools-info xyz2shp
d3dtools-info rmgrid
d3dtools-info rsgrid

# 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
evaluate_sensor2 --help
eval_iot --help
getfacez --help
getfacez2 --help
fou2shp --help
pliz2shp --help
pli2shp --help
pol2shp --help
xyz2shp --help
rmgrid --help
rsgrid --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 -of custom/TAB
snorain --input rainfall_scenarios.csv --output-folder custom/TAB --verbose

# Convert boundary shapefiles to LDB
shp2ldb
shp2ldb -i custom/SHP_LDB -of 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 -of 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 -of 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

# Calculate flood simulation accuracy using sensor data with dual-threshold shapefiles
evaluate_sensor2 --sim-low SHP/SIM_thrd125.shp --sim-high SHP/SIM_thrd475.shp --obs SHP/OBS_SENSOR.shp
evaluate_sensor2 --sim-low SHP/SIM_thrd125.shp --sim-high SHP/SIM_thrd475.shp --obs SHP/OBS_SENSOR.shp --buffer 50 --threshold 20 --output sensor_accuracy2.csv
eval_iot --sim-low SHP/SIM_thrd125.shp --sim-high SHP/SIM_thrd475.shp --obs SHP/OBS_SENSOR.shp  # Alias for evaluate_sensor2
eval_iot --sim-low SHP/SIM_thrd125.shp --sim-high SHP/SIM_thrd475.shp --obs SHP/OBS_SENSOR.shp --buffer 30 --threshold 20 --output sensor_accuracy2.csv

# Extract Mesh2d_face_z values at observation points (spatial-index accelerated)
getfacez --nc-file path/to/model_output.nc --obs-shp path/to/observation_points.shp
getfacez --obs-shp path/to/observation_points.shp                    # Auto-detect a single .dsproj in the current directory
getfacez -p MyProject.dsproj --obs-shp path/to/observation_points.shp  # Resolve the NetCDF from a project's MDU NetFile
getfacez -p MyProject --obs-shp path/to/observation_points.shp         # Project name without the .dsproj extension
getfacez --nc-file path/to/model_output.nc --obs-shp path/to/observation_points.shp --output-csv bathymetry.csv --output-excel bathymetry.xlsx
getfacez --nc-file path/to/model_output.nc --obs-shp path/to/observation_points.shp -if StationName  # Specify custom id field
getfacez --verbose  # Display additional processing information

# Extract Mesh2d_face_z values at observation points (original brute-force fallback)
getfacez2 --nc-file path/to/model_output.nc --obs-shp path/to/observation_points.shp
getfacez2 --nc-file path/to/model_output.nc --obs-shp path/to/observation_points.shp --output-csv bathymetry.csv --output-excel bathymetry.xlsx
getfacez2 --nc-file path/to/model_output.nc --obs-shp path/to/observation_points.shp -if StationName  # Specify custom id field
getfacez2 --verbose  # Display additional processing information

# Reconstruct FOU mesh faces as threshold-filtered shapefiles
fou2shp                                         # Use defaults (NC/FlowFM_fou.nc -> SHP/)
fou2shp --input NC/FlowFM_fou.nc -of SHP        # Specify input and output directory
fou2shp --input NC/FlowFM_fou.nc --var Mesh2d_fourier002_max_depth --output-folder output
fou2shp --input NC/FlowFM_fou.nc -r SHP/EXCLUDE.shp             # Remove polygons intersecting a mask; output -> SHP_RM/
fou2shp --input NC/FlowFM_fou.nc -r SHP/*.shp                   # Glob pattern for multiple masks
fou2shp --input NC/FlowFM_fou.nc --remove SHP/ROAD.shp SHP/BUILDING.shp  # Multiple explicit masks

# Convert a Delft3D/D-Flow FM .pliz file (weir/dike polyline with Z) to a 3D ESRI Shapefile
pliz2shp -i Dike001.pliz
pliz2shp -i Dike001.pliz -of output --crs EPSG:4326  # Specify output folder and CRS
pliz2shp -if custom/PLIZ -of custom/SHP              # Convert every .pliz file in a folder
pliz2shp --help

# Convert a Delft3D polyline file (.pli/.ldb) to an ESRI Shapefile
pli2shp -i boundary.pli
pli2shp -i LDB_001.ldb -of output --crs EPSG:4326
pli2shp -if custom/PLI -of custom/SHP
pli2shp --help

# Convert a Delft3D/D-Flow FM .pol file to a polygon ESRI Shapefile
pol2shp -i POL_001.pol
pol2shp -i POL_001.pol -of output --crs EPSG:4326
pol2shp -if custom/POL -of custom/SHP
pol2shp --help

# Convert an XYZ/CSV point file to an ESRI Shapefile
xyz2shp -i XYZ_001.xyz
xyz2shp -i XYZ_001.csv -of output --crs EPSG:4326
xyz2shp -i XYZ_001.xyz -d 2                         # Write 2D (x,y) points instead of 3D
xyz2shp -if custom/XYZ -of custom/SHP
xyz2shp --help

# Remove the 2D computational mesh from a D-Flow FM .dsproj project
rmgrid                                # Auto-detect the .dsproj in the current folder
rmgrid -i MyProject.dsproj            # Specify the project explicitly
rmgrid -i MyProject.dsproj --force-backup  # Overwrite an existing .nc.bak
rmgrid -i MyProject.dsproj --restore  # Restore the original net file from .nc.bak

# Restore the 2D computational mesh into a D-Flow FM .dsproj project
rsgrid -s Intact.dsproj                   # Restore into first .dsproj in cwd
rsgrid -i Stripped.dsproj -s Intact.dsproj # Specify target and source explicitly
rsgrid -s source_net.nc                   # Source given directly as a net file
rsgrid -i target_net.nc -s source_net.nc  # Operate directly on net files

# Restore the 2D spatial fields (infiltration capacity, roughness) too
rsgrid -f                                 # Restore fields from the current directory
rsgrid -i Target.dsproj -f -d fields/     # Take the *.xyz files from fields/
rsgrid -i Target.dsproj -s Intact.dsproj -f    # Mesh first, then the fields
rsgrid -f -q frictioncoefficient=rough2024.xyz # Map an oddly named sample file

Changelog

See CHANGELOG.md for the full version history.

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>=2.0.0
• scipy>=1.7.0
• matplotlib>=3.4.0
• openpyxl>=3.0.0

License

MIT