TronGisPy 1.4.0

Gis raster data processing tool

Introduction

TronGisPy aims to automate the whole GIS process on raster data using python interface. To get start, please see GettingStarted.ipynb. The main module are listed below:

• Raster: This module is Main class in TronGisPy. Use ras = tgp.read_raster('<file_path>') to read the file as Raster object. A Raster object contains all required attribute for a gis raster file such as .tif or .geotiff file including digital number for each pixel (ras.data), number of rows (ras.rows), number of columns (ras.cols), number of bands (ras.bands), geo_transform (ras.geo_transform), projection (ras.projection), no_data_value and metadata. The Raster object can also be plot with GeoDataFrame(shapefile) on the same canvas using ras.plot(). Functions like ras.reproject(), ras.remap() and ras.refine_resolution() are useful functions.

• CRS: Convert the projection sys between well known text (WKT) and epsg(tgp.epsg_to_wkt, tgp.wkt_to_epsg). Convert the indexing sys tem between numpy index and coordinate system(tgp.coords_to_npidxs, tgp.npidxs_to_coords).

• ShapeGrid: Interaction between raster and vector data including tgp.ShapeGrid.rasterize_layer, tgp.ShapeGrid.rasterize_layer_by_ref_raster, tgp.ShapeGrid.vectorize_layer, tgp.ShapeGrid.clip_raster_with_polygon and tgp.ShapeGrid.clip_raster_with_extent.

• DEMProcessor: General dem processing functions including tgp.DEMProcessor.dem_to_hillshade, tgp.DEMProcessor.dem_to_slope, tgp.DEMProcessor.dem_to_aspect, tgp.DEMProcessor.dem_to_TRI, tgp.DEMProcessor.dem_to_TPI and tgp.DEMProcessor.dem_to_roughness. normalizer.

• Interpolation: Interpolation for raster data on specific cells which are usually nan cells. Once majority or mean value in the filter (convolution) are prefered value for interpolation, tgp.Interpolation.majority_interpolation, tgp.Interpolation.mean_interpolation are written in numba to speed up the process. If Inverse Distance Weight (IDW) method is appropriate, tgp.Interpolation.gdal_fillnodata impolemented by GDAL can be called.

• Normalizer: Normalize the Image data for model training or plotting. Normalizer can be initialize from normalizer = tgp.Normalizer(). Function normalizer.fit_transform() can help to normalize the data. Function normalizer.clip_by_percentage can be used to clip the head and tail of the data to avoid the outlier affecting plotting.

• SplittedImage: Split raster images for machine learning model training. Use splitted_image = tgp.SplittedImage(raster, box_size, step_size=step_size) to initialize SplittedImage object. SplittedImage object have n_steps_h, n_steps_w, padded_rows, padded_cols, shape, n_splitted_images, padded_image attributes. Function splitted_image.apply() can be used to process all splitted images using the funtion. Function splitted_image.get_geo_attribute() helps to get the vector of all splitted images and return GeoDataFrame object. When the prediction on each image is done, splitted_image.write_splitted_images() can be called to combine the prediction results on each splitted images to have the same size as original raster image.

• TypeCast: Mapping the data type betyween gdal and numpy, and convert the gdal data type from integer to readable string. Because gdal use integer to represent defferent data types, tgp.get_gdaldtype_name() helps to convert the integer to its data type name in string. Also, once converting the data type between numpy and gdal is required, tgp.gdaldtype_to_npdtype and tgp.npdtype_to_gdaldtype can help.

• io: Create, read and update the raster from the raster file. Use tgp.read_raster to read raster file as Raster object. Functions tgp.get_raster_info and tgp.get_raster_extent can be used when you don't want to read all digital value of the raster into the memory. Function tgp.update_raster_info can used to update the infomation of the raster file such as projection and geo_transform. Finally, if you want to get the testing file, tgp.get_testing_fp can help.

Getting Started

To get start, please see GettingStarted.ipynb.

Install

Windows

1. Install preinstalls from pre-build wheel package

   • GDAL==3.0.4
   • Fiona==1.8.13
   • Shapely==1.6.4.post2
   • geopandas==0.7.0
   • Rtree==0.9.4
   • opencv_python==4.1.2

2. Install TronGisPy

pip install TronGisPy

Linux

1. Build GDAL==3.0.4 by yourself
2. Build opencv==4.1.2 by yourself
3. install other preinstalls from public pypi server

pip install GDAL==3.0.4 Fiona==1.8.13 Shapely==1.6.4.post2 geopandas==0.7.0 Rtree==0.9.4

4. Install TronGisPy

pip install TronGisPy

For Developer

Build

python setup.py sdist bdist_wheel

Reference

1. Logo

Document Generation

0. Installaion

pip install sphinx
pip install sphinx-rtd-theme
pip install numpydoc

1. generatate index.rst (https://docs.readthedocs.io/en/stable/intro/getting-started-with-sphinx.html)

mkdir docs
cd docs
sphinx-quickstart

2. modify docs/source/conf.py (https://www.sphinx-doc.org/en/master/usage/extensions/napoleon.html)

vim source/conf.py

base_dir = os.path.dirname(os.path.realpath(__file__))
sys.path.insert(0, os.path.abspath(os.path.join(base_dir, '..', '..')))

html_theme = "classic"
extensions = ['sphinx.ext.napoleon']
exclude_patterns = ['setup.py', 'req_generator.py', 'test.py']

3. generate TronGisPy rst

cd ..
python clean_docs_source.py
sphinx-apidoc --force --separate --module-first -o docs\source .

4. generate html

cd docs
make clean
make html

For Thinktron Worker

Install on Windows

1. Install preinstall thinktron pypi server

pip install -U --index-url http://192.168.0.128:28181/simple --trusted-host 192.168.0.128 GDAL==3.0.4 Fiona==1.8.13 Shapely==1.6.4.post2 geopandas==0.7.0 Rtree==0.9.4 opencv_python==4.1.2

2. Install TronGisPy from thinktron pypi server (Windows)

pip install -U --extra-index-url http://192.168.0.128:28181/simple --trusted-host 192.168.0.128 TronGisPy