Threedigrid: The 3Di grid admin framework
The Python package for the threedigrid administration.
Free software: BSD license
Documentation: https://threedigrid.readthedocs.io.
Overview
Features
access to the threedicore administration by a single instance of the GridH5Admin object
query the model data by pre-defined subsets and django style filters
export model data to gis formats like shapefile, geopackage
serialize model data as geojson
Quick start
The standard threedigrid distribution is pretty lightweight, installing as little dependencies as possible. If you want to make use of all capabilities threedigrid has to ofter (e.g. spatial operations and command line tools) install like this:
$ pip install threedigrid[geo,results]
Console scripts
Using the 3digrid_explore shortcut, simply run:
$ 3digrid_explore --grid-file=<path to grid file> --ipy
This will invoke an ipython session with a GridH5Admin instance already loaded.
To get a quick overview of the threedimodels meta data omit the --ipy option or explicitly run:
$ 3digrid_explore --grid-file=<the to grid file> --no-ipy
This will give you output like this:
Overview of model specifics: model slug: v2_bergermeer-v2_bergermeer_bres_maalstop-58-b1f8179f1f3c2333adb08c9e6933fa7b9a8cd163 threedicore version: 0-20180315-3578e9b-1 threedi version: 1.63.dev0 has 1d: True has 2d: True has groundwater: True has levees: True has breaches: True has pumpstations: True
(I)Python shell
Get a grid admin instance:
from threedigrid.admin.gridadmin import GridH5Admin f = 'gridadmin.h5' ga = GridH5Admin(f)
The grid admin directly holds some model specific attributes like whether the model has a 1D or 2D or groundwater section:
In [4]: ga.has_groundwater Out[4]: False In [5]: ga.has_1d Out[5]: True
There are different types of filters but a filter, generally speaking, acts on field. That means you can filter by value. If you have a line model instance you can filter the data by the kcu field:
ga.lines.filter(kcu__in=[100,102])
or by the lik value:
ga.lines.filter(lik__eq=4)
The filtering is lazy, that is, to retrieve data you have to call data explicitly:
ga.lines.filter(lik__eq=4).data # will return an ordered dict
The structure control actions netcdf can also be analyzed and exported using threedigrid:
from threedigrid.admin.gridresultadmin import GridH5StructureControl
from threedigrid.admin.structure_controls.exporters import structure_control_actions_to_csv
gst = GridH5StructureControl("gridadmin.h5", "structure_control_actions_3di.nc")
gst.table_control
structure_control_actions_to_csv(gst, "test.csv")
Remote procedure calls
Currently only the client-side is included. The server-side might be added in a later stage. Note: this is an advanced feature used inside the 3Di stack, probably you don’t need this. Note2: you need Python 3.7 or higher for this to work.
Installation:
$ pip install threedigrid[rpc]
Basic usage:
ga = GridH5ResultAdmin('rpc://REDIS_HOST/SIMULATION_ID', 'rpc://REDIS_HOST/SIMULATION_ID')
# Replace REDIS_HOST and SIMULATION_ID with actual values.
future_result = ga.nodes.filter(lik__eq=4).data
data = await future_result.resolve()
Subscription usage:
subscription = await future_result.subscribe()
async for item in subscription.enumerate():
# do something with item
Local development
In order to set up a virtual environment, perform the following steps:
Clone the repo and fetch the LFS objects:
git lfs fetch origin refs/remotes/origin/master git lfs checkout
Install platform dependencies:
sudo apt-get update && sudo apt-get install --yes --no-install-recommends libgdal-dev
Create and activate a virtual environment:
python -m venv ./venv source ./venv/bin/activate
Install the dependencies. For your distribution, check the dependency matrix in .github/workflows/test.yml. For Python 3.12 on Ubuntu 24:
pip install --disable-pip-version-check --upgrade pip setuptools wheel pip install -e .[geo,results] GDAL==$(gdal-config --version).* ipython pytest
Credits
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
6 maintainers
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