lisflood-model 2.11.3.0

LISFLOOD model python module

Lisflood OS

This repository hosts source code of LISFLOOD model. Go to Lisflood OS page for more information.

Other useful resources

Project	Documentation	Source code
Lisflood	Model docs	https://github.com/ec-jrc/lisflood-code (this repository)
	User guide
Lisvap	Docs	https://github.com/ec-jrc/lisflood-lisvap
Calibration tool	Docs	https://github.com/ec-jrc/lisflood-calibration
Lisflood Utilities		https://github.com/ec-jrc/lisflood-utilities
Lisflood Usecases		https://github.com/ec-jrc/lisflood-usecases

Quick start

You can download code and datasets for testing the model. Follow this instruction for a basic test (included in this repository under tests/data/TestCatchment1)

1. Clone the master branch of this repository (you need to have git installed on your machine).

git clone --single-branch --branch master https://github.com/ec-jrc/lisflood-code.git

2. Install requirements into a Python 3 virtualenv. We recommend to follow the instructions on virtualenv docs. Assuming you've activated your virtual environment, you can now install requirements with pip:

cd lisflood-code  # move into lisflood-code project directory
pip install -r requirements.txt

• GDAL should be installed as well. To install GDAL C library and gdal python library on debian/ubuntu systems, we found good instructions here.

If you already have GDAL installed in your computer, make sure that the GDAL and the python gdal library have the same version.

You need to install PCRaster (4.2.x is first version which works with Python3) and include its python interface in PYTHONPATH environment variable. For details, please follow instruction on official docs.

3. Compile the cython module kinematic_wave_parallel_tool

To compile this Cython module to enable OpenMP multithreading (parallel kinematic wave):

• Delete the files *.so (if any) in directory hydrological-modules

• Inside the hydrological_modules folder, execute "python compile_kinematic_wave_parallel_tools.py build_ext --inplace"

Important: the module has to be compiled on the machine where the model is run - the resulting binary is not portable.

Then in the settings file the option "numberParallelThreadsKinematicWave" may take the following values: - "0" : auto-detection of the machine/node's number of CPUs (all CPUs are used minus 1) (do not set it if other simulations are running on the same machine/node) - "1" : serial execution (not parallel) - "2", "3", ... : manual setting of the number of parallel threads. (if exceeding the number of CPUs, the option is set to "0") -->

<textvar name="numCPUs_parallelKinematicWave" value="3"/>

4. Run a cold run for the test catchment

Now your environment should be set up to run lisflood. Try with a prepared settings file for one of the two test catchments:

python src/lisf1.py tests/data/TestCatchment/settings/cold_day_base.xml

If the command above successed without errors, producing dis.nc into tests/data/TestCatchment1/outputs folder, your lisflood installation was correct.

Docker image

You can use the updated docker image to run lisflood, so without taking care to install dependencies on your system.

Pull image from repository:

docker pull efas/lisflood:latest

Run tests in image:

docker run -v /absolute_path/to/my/local/folder:/usecases efas/lisflood:latest usecases

Copy catchment files from container to your host, using mapped directories:

docker run -v /absolute_path/to/my/local/folder:/usecases efas/lisflood:latest usecases

After this command, you can find all files to run a test against a catchment under the directory you mapped: /absolute_path/to/my/local/folder/TestCatchment1

Now, you can run LISFLOOD as a docker container to test included catchments. Only thing you need to do is to map the TestCatchment1 folder to the container folder input, by using -v option. In the XML settings file, all paths are adjusted to be relative to the very same settings file, so you don't need to edit paths, as long as you keep same folders structure.

Execute lisflood with a Docker image:

docker run -v /absolute_path/to/my/local/folder/TestCatchment:/input efas/lisflood /input/settings/cold_day_base.xml

Once LISFLOOD finished, you find reported maps in /absolute_path/to/my/local/folder/TestCatchment/outputs/ folder.

Pypi packaged LISFLOOD

LISFLOOD is also distributed as a standard python package. You can install the pip package in your Python 3 virtualenv:

pip install lisflood-model

Command above will also install the executable lisflood in the virtualenv, so that you can run LISFLOOD with the following:

lisflood /absolute_path/to/my/local/folder/TestCatchment/settings/cold_day_base.xml

Collaborate

If you find an issue in our code, please follow the GitHub flow to propose your changes (Fork, commit your changes and ask for a Pull Request). When you develop, you need to run our unit tests before to propose a pull request. Simply execute tox or pytest on command line from project folder.

Tox tests can last minutes. You can also just use pytest and run tests in a single environment (e.g. Python 3.7). This is often enough and will save you some time if you need to run tests frequently.

pytest tests/ -x -l -ra

See official website for more info about pytest.