Python库 | geostatspy-0.0.8.tar.gz

<p> <img src="https://github.com/GeostatsGuy/GeostatsPy/blob/master/geostatspy_logo.png?raw=true" width="220" height="240" /> </p> ## GeostatsPy Package The GeostatsPy Package brings GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functions to Python. GSLIB is extremely robust and practical set of code for building spatial modeling workflows. I need it in Python to support my students in my **Data Analytics**, **Geostatistics** and **Machine Learning** courses. I find my students benefit from hands-on opportunities, in fact it is hard to imagine teaching these topics without providing the opportunity to handle the numerical methods and build workflows. I tried to have the use the original FORTRAN executables last year and even with support and worked out examples, it was an uphill battle. In addition, all my students and I are now working in Python for our research. Having geostatistical methods in Python directly impact the research of my group. Finally, I like to code. I have over 25 years of experience in FORTRAN, C++ and Visual Basic programing. This includes frontend (Qt interfaces in C++) and backend development with small and at times very large engineering and geoscience projects. This package includes 2 parts: 1. geostatspy.geostats includes GSLIB functions rewritten in Python. This currently includes all the variogram, distribution transformations, and spatial estimation and simulation (SGSIM soon) methods. I will continue adding functions to support modeling operations for practical subsurface model cosntruction. 2. geostatspy.GSLIB includes reimplimentation of the GSLIB visualizations and low tech wrappers of the numerical methods (note: the low-tech wrapper require access to GSLIB executables). <p> <img src="https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true" width="220" height="240" /> </p> ### The Authors This package is being developed at the University of Texas in the Texas Center for Geostatistics. * **Michael J. Pyrcz, Ph.D., P.Eng.** - associate professor with the University of Texas at Austin. Primary author of the package. * **Anton Kupenko** - bug fixes, added docstrings, code refractory for PEP8, removed duplicated functions and variables. Thank you Anton! * **Wendi Liu** - Ph.D. student working with Michael Pyrcz at the University of Texas at Austin. GSLIB compiles in Mac OSX, 3D variogram calculation low-tech wrapper. * **Alex E. Gigliotti** - undergraduate student working with Michael Pyrcz at the University of Texas at Austin. Established unit testing. ### Package Inventory Here's a list and some details on each of the functions available. #### geostatspy.GSLIB Functions Utilities to support moving between Python DataFrames and ndarrays, and Data Tables, Gridded Data and Models in Geo-EAS file format (standard to GSLIB): 1. **ndarray2GSLIB** - utility to convert 1D or 2D numpy ndarray to a GSLIB Geo-EAS file for use with GSLIB methods 2. **GSLIB2ndarray** - utility to convert GSLIB Geo-EAS files to a 1D or 2D numpy ndarray for use with Python methods 3. **Dataframe2GSLIB(data_file,df)** - utility to convert pandas DataFrame to a GSLIB Geo-EAS file for use with GSLIB methods 4. **GSLIB2Dataframe** - utility to convert GSLIB Geo-EAS files to a pandas DataFrame for use with Python methods 5. **DataFrame2ndarray** - take spatial data from a DataFrame and make a sparse 2D ndarray (NaN where no data in cell) Visualization functions with the same parameterization as GSLIB using matplotlib: 6. **pixelplt** - reimplemention in Python of GSLIB pixelplt with matplotlib methods 7. **pixelplt_st** - reimplemention in Python of GSLIB pixelplt with matplotlib methods with support for sub plots 8. **pixelplt_log_st** - reimplemention in Python of GSLIB pixelplt with matplotlib methods with support for sub plots and log color bar 9. **locpix** - pixel plot and location map, reimplementation in Python of a GSLIB MOD with MatPlotLib methods 10. **locpix_st** - pixel plot and location map, reimplementation in Python of a GSLIB MOD with MatPlotLib methods with support for sub plots 11. **locpix_log_st** - pixel plot and location map, reimplementation in Python of a GSLIB MOD with MatPlotLib methods with support for sub plots and log color bar 12. **hist** - histograms reimplemented in Python of GSLIB hist with MatPlotLib methods 13. **hist_st** - histograms reimplemented in Python of GSLIB hist with MatPlotLib methods with support for sub plots Data transformations 14. **affine** - affine distribution transformation to correct feature mean and standard deviation 15. **nscore** - normal score transform, wrapper for nscore from GSLIB (GSLIB's nscore.exe must be in working directory) 16. **declus** - cell-based declustering, 2D wrapper for declus from GSLIB (GSLIB's declus.exe must be in working directory) Spatial Continuity 17. **make_variogram** - make a dictionary of variogram parameters to for application with spatial estimation and simulation 18. **gamv** - irregularly sampled variogram, 2D wrapper for gam from GSLIB (.exe must be in working directory) 19. **varmap** - regular spaced data, 2D wrapper for varmap from GSLIB (.exe must be in working directory) 20. **varmapv** - irregular spaced data, 2D wrapper for varmap from GSLIB (.exe must be in working directory) 21. **vmodel** - variogram model, 2D wrapper for vmodel from GSLIB (.exe must be in working directory) Spatial Modeling 22. **kb2d** - kriging estimation, 2D wrapper for kb2d from GSLIB (GSLIB's kb2d.exe must be in working directory) 23. **sgsim_uncond** - sequential Gaussian simulation, 2D unconditional wrapper for sgsim from GSLIB (GSLIB's sgsim.exe must be in working directory) 24. **sgsim** - sequential Gaussian simulation, 2D wrapper for sgsim from GSLIB (GSLIB's sgsim.exe must be in working directory) 25. **cosgsim_uncond** - sequential Gaussian simulation, 2D unconditional wrapper for sgsim from GSLIB (GSLIB's sgsim.exe must be in working directory) Spatial Model Resampling 26. **sample** - sample 2D model with provided X and Y and append to DataFrame 27. **gkern** - make a Gaussian kernel for convolution, moving window averaging (from Teddy Hartano, Stack Overflow) 28. **regular_sample** - extract regular spaced samples from a 2D spatial model 29. **random_sample** - extract random samples from a 2D spatial model 30. **DataFrame2ndarray** - convent spatial point data in a DataFrame to a sparse ndarray grid #### geostatspy.geostats Functions Numerical methods in GSLIB (Deutsch and Journel, 1998) translated to Python: 30. **correct_trend** - correct the order relations of an indicator-based trend model 30. **backtr** - GSLIB's backtr function to transform a distribution 30. **declus** - GSLIB's DECLUS program reimplimented for cell-based declustering in 2D 31. **gam** - GSLIB's GAM program reimplimented for variogram calculation with regular data in 2D 32. **gamv** - GSLIB's GAMV program reimplimented for variogram calculation with iregular data in 2D 33. **varmapv** - GSLIB's VARMAP program reimplimented for irregularly spaced spatial data in 2D 33. **vmodel** - GSLIB's VMODEL program reimplimented for visualization of nested variogram models in 2D 33. **nscore** - GSLIB's NSCORE program reimplimented for normal score distribution transformation 34. **kb2d** - GSLIB's KB2D program reimplimented for 2D spaitial estimation 34. **ik2d** - GSLIB's IK3D program reimplimented for 2D indicator-based kriging estimation More functionality will be added soon. ### Package Dependencies The functions rely on the following packages: 1. **numpy** - for ndarrays 2. **pandas** - for DataFrames 3. **numpy.linalg** - for linear algebra 4. **numba** - for numerical speed up 5. **scipy** - for fast nearest neighbor search 6. **matplotlib.pyplot** - for plotting These packages should be available with any modern Python distribution (e.g. https://www.anaconda.com/downl