hydro-raster 0.0.4

To process raster data for hydro-logical/dynamic modelling

hydro_raster

Python code to process raster data for hydroligical or hydrodynamic modelling, e.g., HiPIMS flood model. This code follows Google Python Style Guide.

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

The CRS of both DEM and Shapfiles must be projected crs whose map unit is meter.

Functions included in this package:

1. merge raster files
2. edit raster cell values based on shapefile
3. convert cross-section lines to river bathymetry raster
4. remove overhead buildings/bridges on raster
5. read, write, and visualise raster file

To install hydro_raster from command window/terminal:

pip install hydro_raster

To install using github repo:

git clone https://github.com/mingxiaodong/hydro-raster
cd hydro-raster
pip install .

Tutorial

1. Read a raster file

from hydro_raster.Raster import Raster
from hydro_raster import get_sample_data
tif_file_name = get_sample_data('tif')
ras_obj = Raster(tif_file_name)

2. Visualize a raster file

ras_obj.mapshow()
ras_obj.rankshow(breaks=[0, 10, 20, 30, 40, 50])

3. Clip raster file

clip_extent = (340761, 341528, 554668, 555682) # left, right, bottom, top
ras_obj_cut = ras_obj.rect_clip(clip_extent) # raster can be cutted by a shapfile as well using clip function
ras_obj_cut.mapshow()

3. Rasterize polygons on a raster and return an index array with the same dimension of the raster array

shp_file_name = get_sample_data('shp')
index_array = ras_obj_cut.rasterize(shp_file_name)

4. Change raster cell values within the polygons by adding 20

ras_obj_new = ras_obj_cut.duplicate()
ras_obj_new.array[index_array] = ras_obj_cut.array[index_array]+20

5. Show the edited raster with the shapefile polygons

import matplotlib.pyplot as plt
from hydro_raster.grid_show import plot_shape_file
fig, ax = plt.subplots(1, 2)
ras_obj_cut.mapshow(ax=ax[0])
plot_shape_file(shp_file_name, ax=ax[0], linewidth=1)
ras_obj_new.mapshow(ax=ax[1])
plot_shape_file(shp_file_name, ax=ax[1], linewidth=1)
# values can also be changed based on the attributes of each shapefile features