Metadata-Version: 2.1
Name: hipims-io
Version: 0.5
Summary: To process input and output files of the HiPIMS model
Home-page: https://github.com/mingxiaodong/hipims_io_python
Author: Xiaodong Ming
Author-email: x.ming@lboro.ac.uk
License: LICENSE.txt
Keywords: hipims model IO
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown

hipims_io
--------
Python code to process input and output files of [HiPIMS flood model](https://github.com/HEMLab/hipims). This code follows [Google Python Style Guide](http://google.github.io/styleguide/pyguide.html).

Python version: >=3.6. To use the full function of this package for processing raster and shapefile, gdal and pyshp are required.

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 = hpio.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 hpio
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 = hpio.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 hpio

obj_dem, model_data = hpio.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 = hpio.InputHipims(dem_data=obj_dem, 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:

![Domain map](https://github.com/mingxiaodong/hipims_io_python/blob/master/hipims_io/sample/domain_map.png)


