Python库 | starepandas-0.5.4.tar.gz

# STAREPandas STAREpandas adds [SpatioTemporal Adaptive Resolution Encoding (STARE)](https://github.com/SpatioTemporal) support to [pandas DataFrames](https://pandas.pydata.org/).  ## Introduction STAREPandas is the STARE pendant to [GeoPandas](https://geopandas.org/). It makes working with geospatial data in python easier. It provides file and database I/O functionality and allows to easily perform STARE based spatial operations that would otherwise require a (STARE-extended) spatial database or a geographic information system. In STAREDataFrames, geometries are represented as sets of STARE triangles or ”trixels”; analogously to GeoPandas geodataframes which represent geometries as WKT. In STARE dataframes, points are represented as STARE trixels at the HTM tree’s leaf level. Polygons are represented as sets of STARE trixels that cover the polygon. STAREPandas also extends the geopandas file I/O functionality to load some (raster) formats of remote sensing granules and tiles (MOD09, MOD09GA, VNP03) through pyhdf and netcdf4. ## Installation ### pyhdf STAREPandas depends on pyhdf to read hdf4-eos granules, requiring libhdf4-dev, to build. Tested on python 3.7.6 On Ubuntu 20.04: ```shell apt install libhdf4-dev ``` On Centos7: ```shell yum install hdf-devel.x86_64 ``` Alternatively, pyhdf can also be found on conda ```shell conda install -c conda-forge pyhdf ``` ### pystare STAREPandas is built on top of [pystare](https://github.com/SpatioTemporal/pystare). ```shell pip3 install pystare ``` ### STAREPandas It is recommendable to install pip packages in a [Virtual Environment](https://pip.pypa.io/warnings/venv) ``` mkvirtualevironment starepandas ``` Make sure pip is up-to-date. Then install STAREPandas from github. ```shell pip3 install starepandas ``` ## Note Some of the examples require Rtree-linux to be installed to run geopandas spatial joins. As of 2020-08-20, I could not make this work on Centos7 with rtree>0.9 (9.4) as it requires GLIBCXX_3.4.21. I therefor downgrade rtree to rtree-0.8.3 on Centos7 ```shell pip3 install "rtree>=0.8,<0.9 ``` This is likely related to [rtree issue 120](https://github.com/Toblerity/rtree/issues/120) ## Tests ```shell cd starepandas/ pytests ``` ## Documentation starepandas uses sphinx The dependencies are in ```docs/source/requirements.txt``` ``` pip3 install -r docs/source/requirements.txt ``` Build the docs with e.g. ``` cd docs/ make html ``` ## Features and usage The examples/ folder contains notebooks that highlight the usage. STAREPandas helps integrating STARE in the geospatial data workflow. Building on top of fiona and geopandas, STAREPandas allows to read almost any vector-based spatial data format and convert lat/lon and well-known-text (WKT) representation to STARE indices and covers. ```python path = geopandas.datasets.get_path('naturalearth_lowres') world = geopandas.read_file(path) africa = world[world.continent == 'Africa'] stare = starepandas.sids_from_gdf(africa, resolution=7, force_ccw=True) africa = starepandas.STAREDataFrame(africa, stare=stare) ``` STAREPandas extends the geopandas rich plotting abilities and provides a simple method to generate visualizations of trixels: ```python trixels = africa.make_trixels() africa.set_trixels(trixels, inplace=True) africa.plot(ax=ax, trixels=True, boundary=True, column='name', linewidth=0.2) ```  STAREPandas extends the file I/O capability with the ability to read common remote-sensing granule data from HDF and netCDF files. STARE indices for the granules can either be generated on demand or read from a companion / sidedcar file. ```python path= 'data/MYD05_L2.A2020060.1635.061.2020061153519.hdf' modis = starepandas.read_mod09(path, add_stare=True, adapt_resolution=True) ```  STAREPandas allows to carry out STARE-based spatial relation tests and spatial joins. ```python cities = ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas', 'Sao Paulo', 'Bridgetown'] latitudes = [-34.58, -15.78, -33.45, 4.60, 10.48, -23.55, 13.1] longitudes = [-58.66, -47.91, -70.66, -74.08, -66.86, -46.63, -59.62] data = {'City': cities, 'Latitude': latitudes, 'Longitude': longitudes} cities = starepandas.STAREDataFrame(data) stare = starepandas.sids_from_xy(cities.Longitude, cities.Latitude, resolution=27) cities.set_sids(stare, inplace=True) countries = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) countries = countries.sort_values(by='name') samerica = countries[countries.continent == 'South America'] stare = starepandas.sids_from_gdf(samerica, resolution=10, force_ccw=True) samerica = starepandas.STAREDataFrame(samerica, stare=stare) starepandas.stare_join(samerica, cities, how='left').head() ```  STAREPandas further allows for STARE-bases intersections: ```python fname = 'zip://data/amapoly_ivb.zip' amazon = geopandas.read_file(fname) # Nice flex amazon = amazon.to_crs('EPSG:4326') stare = starepandas.sids_from_gdf(amazon, resolution=10, force_ccw=True) amazon = starepandas.STAREDataFrame(amazon, stare=stare) stare_amazon = samerica.stare_intersection(amazon.make_sids.iloc[0]) ```  # Acknowledgments 2018-2021 STARE development supported by NASA/ACCESS-17 grant 80NSSC18M0118.