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------------------------------------------------------------ |\n| Lisflood            | [Model docs](https://ec-jrc.github.io/lisflood-model/)    | https://github.com/ec-jrc/lisflood-code (this repository)    |\n|                     | [User guide](https://ec-jrc.github.io/lisflood-code/)     |                                                              |\n| Lisvap              | [Docs](https://ec-jrc.github.io/lisflood-lisvap/)         | https://github.com/ec-jrc/lisflood-lisvap                    |\n| Calibration tool    | [Docs](https://ec-jrc.github.io/lisflood-calibration/)    | https://github.com/ec-jrc/lisflood-calibration               |\n| Lisflood Utilities  |                                                           | https://github.com/ec-jrc/lisflood-utilities                 |\n| Lisflood Usecases   |                                                           | https://github.com/ec-jrc/lisflood-usecases                  |\n\n\n\n## Quick start\n\nYou can use conda environment to easily install dependencies.\n\n* Install [miniconda](https://docs.conda.io/en/latest/miniconda.html)\n* Create a conda env named \"lisflood\" and install dependencies:\n\n```bash\nconda create --name lisflood python=3.7 -c conda-forge\nconda activate lisflood\nconda install -c conda-forge pcraster gdal\n```\n\n* Install lisflood-model pypi package\n```bash\npip install lisflood-model\n```\n\nCommand above will also install the executable `lisflood` in the conda env, so that you can run LISFLOOD with the following:\n```bash\nlisflood /absolute_path/to/my/local/folder/LF_ETRS89_UseCase/settings/cold.xml\n```\n\nYou can also clone the repository which contains datasets to test the model.\nFollow this instruction for a basic test (included in this repository under [tests/data](https://github.com/ec-jrc/lisflood-code/tree/master/tests/data))\n\n* Clone the master branch of this repository (you need to have git installed on your machine).\n\n```bash\ngit clone --single-branch --branch master https://github.com/ec-jrc/lisflood-code.git\n```\n\n* **Install requirements into a python 3 conda env**\n\n```bash\nconda create --name lisflood python=3.7 -c conda-forge\nconda activate lisflood\nconda install -c conda-forge pcraster gdal\ncd lisflood-code\npip install -r requirements.txt\n```\n\nIf you don't use conda but a plain virtualenv, you need to install PCRaster and GDAL by your own and include its python interface in PYTHONPATH environment variable.\nFor details, please follow instruction on [official docs](http://pcraster.geo.uu.nl).\n\n3. Run a cold run for the test catchment\n\nNow your environment should be set up to run lisflood. Try with a prepared settings file for one of the two test catchments:\n\n```bash\nmkdir tests/data/LF_ETRS89_UseCase/out\npython src/lisf1.py tests/data/LF_ETRS89_UseCase/settings/cold.xml\n```\n\nIf the command above successed without errors, producing dis.nc into tests/data/LF_ETRS89_UseCase/out folder, your lisflood installation was correct.\n\n### Docker image\n\nYou can use the updated docker image to run lisflood, so without taking care to install dependencies on your system.\n\n#### Pull image from repository:\n\n```bash\ndocker pull jrce1/lisflood:latest\n```\n\n#### Run test catchments in image:\n\n```bash\ndocker run -v /absolute_path/to/my/local/folder:/usecases jrce1/lisflood:latest usecases\n```\n\nAfter this command, you can find all files to run tests against catchments under the directory you mapped: `/absolute_path/to/my/local/folder/`\n\n```\n/absolute_path/to/my/local/folder/LF_ETRS89_UseCase\n/absolute_path/to/my/local/folder/LF_lat_lon_UseCase\n```\n\nNow, you can run LISFLOOD as a docker container to test included catchments. Only thing you need to do is to map the test catchment folder to the container folder `input`, by using -v option. \n\nIn 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.\n\n\n#### Execute lisflood with a Docker image:\n\n```bash\ndocker run -v /absolute_path/to/my/local/folder/LF_ETRS89_UseCase:/input jrce1/lisflood /input/settings/cold.xml\n```\n\nOnce LISFLOOD finished, you find reported maps in `/absolute_path/to/my/local/folder/LF_ETRS89_UseCase/out/` folder.\n\n### Pypi packaged LISFLOOD\n\nLISFLOOD is also distributed as a standard python package. You can install the pip package in your Python 3 virtualenv:\n\n```bash\npip install lisflood-model\n```\n\nCommand above will also install the executable `lisflood` in the virtualenv, so that you can run LISFLOOD with the following:\n\n```bash\nlisflood /absolute_path/to/my/local/folder/LF_ETRS89_UseCase/settings/cold.xml\n```\n\n### IMPORTANT NOTE 1\n\nPlease note that there are known issues when installing the LISFLOOD code on Windows (source code and pypi package). We cannot provide Windows support and strongly recommend using LINUX to install the LISFLOOD code.\nWindows users are recommended to execute LISFLOOD with a Docker image.\n\n### IMPORTANT NOTE 2\n\nThe users are recommended to download the [reference settings xml](https://github.com/ec-jrc/lisflood-code/tree/master/src/lisfloodSettings_reference.xml) file and adapt it by inserting their own paths and modelling choices.\n\n\n\n## Collaborate\n\nIf you find an issue in our code, please follow the [GitHub flow](https://guides.github.com/introduction/flow/) to propose your changes (Fork, commit your changes and ask for a Pull Request).\nYou are required to run unit tests during your development and before to propose a pull request.\n\nTo execute unit tests:\n\n```bash\npytest tests/\n```\n\nFurthermore, before to propose a pull request, there are additional tests we ask you to execute: \n\n```bash\npytest tests/ -m \"slow\"\n```\n\nThese tests could take 30 minutes or several hours, depending on your machine.\n\nYou can find full description and implementation details at [Test documentation](https://ec-jrc.github.io/lisflood-code/4_annex_tests/) page.\n\n**Note**: If yuor pull request is about a new feature you may want to integrate in LISFLOOD,\nensure to include tests with good coverage for it.\n\nFor more info about pytest, see [official website](https://docs.pytest.org/en/latest/).\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["forecasting-models","netcdf4","pcraster","calibration","grib","interpolation","meteo"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/20838","html_url":"https://ost.ecosyste.ms/projects/20838"}