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Python APIs for hipims

Project description

Python APIs for hipims

This package provides python APIs for running the open source hydraulic model hipims-cuda. It also includes the hipims-io package for pre-processing and result visualisation. The full documentation is at https://pypims.readthedocs.io/en/latest/.

Installation

Before installing pypims, you need to install Cmake, NVIDIA CUDA Toolkit and the C++ compiler. Cmake can be simply installed by

pip install cmake

The CUDA Toolkit can be downloaded from the NVIDIA website. If you use Linux system, the C++ compiler 'GCC' is usually on the system as default. If you use windows, you should download and install Visual Studio. If there exists several different versions of CUDA Toolkits on your system, cmake may struggle to decide which one to use and return an error. In such a case, you need to define the CUDAToolkit_ROOT environmental varialble. On Linux, this can be done by

export CUDAToolkit_ROOT=<path to your cuda directory>

For example

export CUDAToolkit_ROOT=/usr/local/cuda-10.1

Once the aforementioned dependencies have been properly installed. Installing pypims is straightforward, simple type in the following in your terminal

pip install pypims

Basic usage

The flood simulation engine can be imported by

from pypims import flood

The inputs can be prepared by hipims-io. This package has already been included in pypims, you can import it by

from pypims import IO

A quick demonstration to prepare input files with attached sample data contaning the following files:

  • DEM.gz/.asc/.tif (essential file, in projected crs, map unit:m)
  • rain_mask.gz/.asc/.tif (optional file for setting rainfall, having the same crs with DEM)
  • rain_source.csv (optional file for setting rainfall rate in timeseries, unit:m/s]
  • landcover.gz/.asc/.tif (optional file for setting landcover-based parameters, having the same crs with DEM)
import os
from pypims.IO.demo_functions import get_sample_data
data_path = get_sample_data(return_path=True) # get the path of sample data
case_folder = os.path.join(os.getcwd(), 'model_case') # define a case folder in the current directory
num_of_devices = 1
obj_in = IO.InputHipims(case_folder=case_folder, num_of_sections=num_of_devices, 
                        data_path=data_path) # create input object
obj_in.domain_show() # show domain map
print(obj_in) # show case information
obj_in.write_input_files() # create all input files

Once the inputs have all been prepared, the simulations can be started by

flood.run(case_folder)

or

flood.run_mgpus(case_folder) # using this if num_of_devices > 1

for multiple GPUs.

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