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توضیحات

Gis raster data processing tool

ویژگی	مقدار
سیستم عامل	OS Independent
نام فایل	TronGisPy-1.4.7
نام	TronGisPy
نسخه کتابخانه	1.4.7
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Thinktron
ایمیل نویسنده	jeremywang@thinktronltd.com
آدرس صفحه اصلی	https://github.com/thinktron/TronGisPy
آدرس اینترنتی	https://pypi.org/project/TronGisPy/
مجوز	-

![TronGisPy](https://raw.githubusercontent.com/thinktron/TronGisPy/master/static/trongispy.02-01.png) # Introduction TronGisPy aims to automate the whole GIS process on raster data using python interface. To get start, please see [GettingStarted.ipynb](https://github.com/thinktron/TronGisPy/blob/master/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](https://github.com/thinktron/TronGisPy/blob/master/GettingStarted.ipynb). # Install ## Windows 1. Install preinstalls from pre-build wheel package - [GDAL>=3.0.4](https://www.lfd.uci.edu/~gohlke/pythonlibs/#gdal) - [Fiona>=1.8.13](https://www.lfd.uci.edu/~gohlke/pythonlibs/#fiona) - [Shapely>=1.6.4.post2](https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely) - [geopandas>=0.7.0](https://www.lfd.uci.edu/~gohlke/pythonlibs/#geopandas) - [Rtree>=0.9.4](https://www.lfd.uci.edu/~gohlke/pythonlibs/#rtree) - [opencv_python>=4.1.2](https://www.lfd.uci.edu/~gohlke/pythonlibs/#opencv) 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 ``` ## Docker ``` docker pull jeremy4555:trongispy:latest ``` # Author & Instructure  # For Developer ## Build ```bash python setup.py sdist bdist_wheel ``` ## Document Generation 0. [Installaion](https://sphinx-rtd-tutorial.readthedocs.io/en/latest/install.html) ``` 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 ``` ## Docker Build ``` docker build -it --rm <dockerhub_id>/trongispy:latest ``` # Reference 1. [Logo](https://github.com/thinktron/TronGisPy/blob/master/static/trongispy.01-01.png) # For Thinktron Worker ## Install on Windows 1. Install preinstall thinktron pypi server ``` # python36 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 # python37 pip install pyproj pip install -U --index-url http://192.168.0.128:28181/simple --trusted-host 192.168.0.128 GDAL Fiona Shapely geopandas Rtree opencv_python ``` 2. Install TronGisPy from thinktron pypi server (Windows) ``` pip install TronGisPy ```

نیازمندی

مقدار	نام
-	numba
-	affine
-	scikit-learn
-	descartes
-	matplotlib

نحوه نصب

نصب پکیج whl TronGisPy-1.4.7:

pip install TronGisPy-1.4.7.whl

نصب پکیج tar.gz TronGisPy-1.4.7:

pip install TronGisPy-1.4.7.tar.gz