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Python code to process input and output files of HiPIMS flood model. This code follows Google Python Style Guide.
Python version: >=3.6. The main content of this package is also included in a Python API of HiPIMS.
To install hipims_io from command window/terminal:
pip install hipims_io
To install using github repo:
git clone https://github.com/mingxiaodong/hipims_io_python.git cd hipims_io_python pip install .
A quick demonstration to setup a HiPIMS input object with a sample DEM:
import hipims_io as hp obj_in = hp.demo_input() # create an input object and show domain map
A quick demonstration to setup a HiPIMS input object with a data path contaning the following files:
- DEM.gz/.asc/.tif (essential file, in projected crs)
- 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]
- landcover.gz/.asc/.tif (optional file for setting landcover-based parameters, having the same crs with DEM)
import os
import hipims_io as hp
from hipims_io.demo_functions import get_sample_data
dem_path, _ = get_sample_data(return_path=True) # get the path of sample data
data_path = os.path.dirname(dem_path)
case_folder = os.path.join(os.getcwd(), 'hipims_case') # define a case folder in the current directory
obj_in = hp.InputHipims(case_folder=case_folder, num_of_sections=1,
data_path=data_path) # create input object
obj_in.domain_show() # show domain map
print(obj_in) # show case information
A step-by-step tutorial to setup a HiPIMS input object with sample data:
import os
import numpy as np
import hipims_io as hp
dem_file, model_data, _ = hp.get_sample_data() # get sample data
case_folder = os.path.join(os.getcwd(), 'hipims_case') # define a case folder in the current directory
# create a single-gpu input object
obj_in = hp.InputHipims(dem_data=dem_file, num_of_sections=1, case_folder=case_folder)
# set a initial water depth of 0.5 m
obj_in.set_initial_condition('h0', 0.5)
# set boundary condition
bound_list = model_data['boundary_condition'] # with boundary information
obj_in.set_boundary_condition(bound_list, outline_boundary='fall')
# set rainfall mask and source
rain_source = model_data['rain_source']
obj_in.set_rainfall(rain_mask=0, rain_source=rain_source)
# set manning parameter
manning_array = np.zeros(obj_in.DEM.shape)+0.03 # create an array with the same shape of the DEM array
obj_in.set_grid_parameter(manning=manning_array)
# set monitor positions
gauges_pos = model_data['gauges_pos']
obj_in.set_gauges_position(gauges_pos)
# display model information
obj_in.domain_show() # show domain map
print(obj_in) # print model summary
# write all input files for HiPIMS to the case folder
obj_in.write_input_files()
The domain map will be shown like this:
