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Threedigrid: The 3Di grid admin framework

The Python package for the threedigrid administration.

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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

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.