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+/gstools-1.4.1.tar.gz
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+%global _empty_manifest_terminate_build 0
+Name:		python-gstools
+Version:	1.4.1
+Release:	1
+Summary:	GSTools: A geostatistical toolbox.
+License:	LGPL-3.0
+URL:		https://geostat-framework.org/#gstools
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/18/fb/9ac3d888a17109c61645021c797b3c2ea022626f62ee8e4a7351594eb9f8/gstools-1.4.1.tar.gz
+
+Requires:	python3-emcee
+Requires:	python3-hankel
+Requires:	python3-meshio
+Requires:	python3-numpy
+Requires:	python3-pyevtk
+Requires:	python3-scipy
+Requires:	python3-m2r2
+Requires:	python3-matplotlib
+Requires:	python3-meshzoo
+Requires:	python3-numpydoc
+Requires:	python3-pykrige
+Requires:	python3-pyvista
+Requires:	python3-sphinx
+Requires:	python3-sphinx-gallery
+Requires:	python3-sphinx-rtd-theme
+Requires:	python3-sphinxcontrib-youtube
+Requires:	python3-matplotlib
+Requires:	python3-pyvista
+Requires:	python3-gstools-core
+Requires:	python3-pytest-cov
+
+%description
+# Welcome to GSTools
+[![GMD](https://img.shields.io/badge/GMD-10.5194%2Fgmd--15--3161--2022-orange)](https://doi.org/10.5194/gmd-15-3161-2022)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1313628.svg)](https://doi.org/10.5281/zenodo.1313628)
+[![PyPI version](https://badge.fury.io/py/gstools.svg)](https://badge.fury.io/py/gstools)
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/gstools.svg)](https://anaconda.org/conda-forge/gstools)
+[![Build Status](https://github.com/GeoStat-Framework/GSTools/workflows/Continuous%20Integration/badge.svg?branch=main)](https://github.com/GeoStat-Framework/GSTools/actions)
+[![Coverage Status](https://coveralls.io/repos/github/GeoStat-Framework/GSTools/badge.svg?branch=main)](https://coveralls.io/github/GeoStat-Framework/GSTools?branch=main)
+[![Documentation Status](https://readthedocs.org/projects/gstools/badge/?version=latest)](https://geostat-framework.readthedocs.io/projects/gstools/en/stable/?badge=stable)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
+
+<p align="center">
+<img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/gstools.png" alt="GSTools-LOGO" width="251px"/>
+</p>
+
+<p align="center"><b>Get in Touch!</b></p>
+<p align="center">
+<a href="https://github.com/GeoStat-Framework/GSTools/discussions"><img src="https://img.shields.io/badge/GitHub-Discussions-f6f8fa?logo=github&style=flat" alt="GH-Discussions"/></a>
+<a href="https://swung.slack.com/messages/gstools"><img src="https://img.shields.io/badge/Swung-Slack-4A154B?style=flat&logo=data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAABoAAAAaCAYAAACpSkzOAAAABmJLR0QA%2FwD%2FAP%2BgvaeTAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAAB3RJTUUH5AYaFSENGSa5qgAABmZJREFUSMeFlltsVNcVhr%2B1z5m7Zzy%2BxaBwcQrGQOpgCAkKtSBQIqJepKhPBULpQ6sKBVWVKqXtSy%2BR0qYXqa2qRmlDCzjBEZGKUCK1TWqlNiGIEKDQBtf4Fki4OIxnxrex53LOXn2YwbjEtOvlHG3tvX%2Btf%2B21%2Fl%2BYJ1QVEbn1vwLYBWwCVgG1lW0ZoA%2FoAQ6LSP%2BdZ%2BeGzAMiIqK%2Bem0GpxNYVeBj3j2b4NCfM2QnfAAaa11al4fZuCZK24owQJ9v%2BbLryIVbd9wVSNUaEWNVtQPYfXHmAD0T32ZJeBM1Q8d0zzMDUpMwAFgLJU%2BxClURw9NfqedLWxMAHSKyR1WNiNhPAM0B6c%2FbdPORTLuOeUMSNkmMBHgyeo32bwwRDMh8bDM%2BZVl0j6uvPrdYknFnSESWzwUzt%2BkyVlUHx7zh5j%2BmPkXBjosjLkWdominiMQ%2BoiEZxuq8OFRXGXJ5K5%2Fde5nha8VlqjooIlZVBcBUiqeqemjGppd1ptfhSpS8pmmN7GVf4whPNY4Di9m%2BMcR03nK3sBbCQeFbv7gBsExVOyp3l6nz1VtjcM4fTK3Uok5IXtPsrHuPevcBXk8d4dWPX6I%2BsIB9wf1s%2B2Y%2FVbFynUIBIeDeplIECiXl5Iv3kbLogogRgbWukfNumT%2FnlYszBxj3hwXg0cQvqXcfYNu5tVyYPE%2B1G8dXn%2BfW72fH49U8sSlOPGr4SccoF4cKs3WzFrY%2BFCMUNmz%2Ba0aeWR1l15JwJ7DaVPpk1YnJ7xIxtQRNjDXRvTx%2F9ef0Tl0g6SYQhAlvmkH%2Fgv74qUaiTSG8ewJ0%2FGgRK5aG8Cts5ouWDa1RxoDRovK9i9MAq1S12QA7b5ROUdBxBIeQ1ACG49m%2FEXPis7Qk3ChHbx6Qw1dgXVeWB7uyDOctP%2Fx6w2zdrIVIyFCyiq8wXlJOZzyAXQbY%2FGGhC8EAilJ%2BVg7ufxU6IAHeSvewfQEadiDuCr%2B6NE1LU4hwUFAF1xFGRkvEjVDlgiPwVqoEsNkAq0ZKp3EIYrFM2xGm7Uc8u%2FzXjHkTmHIHoCiDM73E3IIsDCtRV3gn7QHQ0hTCt0ooKLw%2FWCAM1AcNISOcHSsBrDRAbc7eQMQBFFciHM18kaZIMz3r%2F0HO5mazytsiw%2FmTtCYiGGCkQlltwkEVjMDVmyUA6oIGR%2BDGjAWoM3f2giHAhH%2BFI5nPsDrWxqWNE9S4tUz5k1S7cQ5df4k9S6qY9JRipXtr4w5WQYH0eHkWrqxy8FTn3AvpmFmIqj%2B76EiQjNfHH1JNWFKc3vABj9V9npw%2FRXfmBNsaoTRnRAQDAgqqMJr1KBWUtUmHaR8WRgzAqAH6FgYexqd4R2Yuns5wcLSFK4U36bj%2FdbbUbGdoZoCi3uS%2Bqtt73TlNWygpqXGfZTGXnKesrwkA9BmgZ0noMZT5R0tQ4hzLfo4rhS46W%2F%2BCAn3T7%2BhDySiWMl2RkHArP8dAesKjPixYVbbUBwB6DHB4QWADIamuHPtkhE0t3ZP7ANhe9zgvXP2dfK0pymRJmQLiEYNW6mEVljYGuDzlkwwaHq51AQ4bERkAetvjP2XCT6H480AJeZsB4N7QYt7OnuSROtRXJV2wNNS4qIJvlbUtERJxhxcv5%2FlNWwygV0QGyzKBv%2FP%2ByFfZXf%2ButoR3UuXcS95mKNgxSjpN3qZZFHwUgFPjx5n2c9wo9ktrtcOZtMeWB2NEw4b2thivPLuIS1M%2BAzmrTy4O4ys7Zv1B5fsnVdWCr7PxYf7vej73ex2YeU1VVY9nu7ShG63vRo%2Fe%2FK1%2B518FbXkjo3OjO1XU2LFRzRZ9VdWDczFQ1VsCOHgpd1G%2FcG6jHrj2vPbn%2BjVdHNfr%2BRH92eXva2MPuvxEQpe%2BHdEnzm%2FQf4%2BrRo%2BldMUbGd393oS2dWU0cDSlw1OequrALVG9Q8rLsquqg2OlzLL2Myu1N5eShgB4CjEnSMSJYrX8Oj0t8UH7NMnX0iSDwmhBWRl3tKs9IcmgGRSRZqtqzFwpL4uWWKvWiMjyZKC24%2F1HbsrLn95Pwk3gCpS0yIw%2Fg6clPC2RLc3QmzvJupoARQsvrItxZmtSkkFz6E6Q%2F2m3PFta44jbCaw%2BO3GK7uybnJs8xfXC1fLYCdTz9NIfsCS0mYVhAHp9ZYdr5J%2F%2F127dxUA2AzuBzRUDWVfZlq4YyG6gs9ImdzWQ%2FwFNRlgCFdG5bAAAAABJRU5ErkJggg%3D%3D" alt="Slack-Swung"/></a>
+<a href="https://gitter.im/GeoStat-Framework/GSTools"><img src="https://img.shields.io/badge/Gitter-GeoStat--Framework-ed1965?logo=gitter&style=flat" alt="Gitter-GSTools"/></a>
+<a href="mailto:info@geostat-framework.org"><img src="https://img.shields.io/badge/Email-GeoStat--Framework-468a88?style=flat&logo=data:image/svg+xml;base64,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" alt="Email"/></a>
+<a href="https://twitter.com/GSFramework"><img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/GSFramework?style=social"></a>
+</p>
+
+<p align="center"><b>Youtube Tutorial on GSTools</b><br></p>
+
+<p align="center">
+<a href="http://www.youtube.com/watch?feature=player_embedded&v=qZBJ-AZXq6Q" target="_blank">
+<img src="http://img.youtube.com/vi/qZBJ-AZXq6Q/0.jpg" alt="GSTools Transform 22 tutorial" width="480" height="360" border="0" />
+</a>
+</p>
+
+## Purpose
+
+<img align="right" width="450" src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/demonstrator.png" alt="">
+
+GeoStatTools provides geostatistical tools for various purposes:
+- random field generation
+- simple, ordinary, universal and external drift kriging
+- conditioned field generation
+- incompressible random vector field generation
+- (automated) variogram estimation and fitting
+- directional variogram estimation and modelling
+- data normalization and transformation
+- many readily provided and even user-defined covariance models
+- metric spatio-temporal modelling
+- plotting and exporting routines
+
+
+## Installation
+
+
+### conda
+
+GSTools can be installed via [conda][conda_link] on Linux, Mac, and Windows.
+Install the package by typing the following command in a command terminal:
+
+    conda install gstools
+
+In case conda forge is not set up for your system yet, see the easy to follow
+instructions on [conda forge][conda_forge_link]. Using conda, the parallelized
+version of GSTools should be installed.
+
+
+### pip
+
+GSTools can be installed via [pip][pip_link] on Linux, Mac, and Windows.
+On Windows you can install [WinPython][winpy_link] to get Python and pip
+running. Install the package by typing the following command in a command terminal:
+
+    pip install gstools
+
+To install the latest development version via pip, see the
+[documentation][doc_install_link].
+
+
+## Citation
+
+If you are using GSTools in your publication please cite our paper:
+
+> Müller, S., Schüler, L., Zech, A., and Heße, F.:
+> GSTools v1.3: a toolbox for geostatistical modelling in Python,
+> Geosci. Model Dev., 15, 3161–3182, https://doi.org/10.5194/gmd-15-3161-2022, 2022.
+
+You can cite the Zenodo code publication of GSTools by:
+
+> Sebastian Müller & Lennart Schüler. GeoStat-Framework/GSTools. Zenodo. https://doi.org/10.5281/zenodo.1313628
+
+If you want to cite a specific version, have a look at the [Zenodo site](https://doi.org/10.5281/zenodo.1313628).
+
+
+## Documentation for GSTools
+
+You can find the documentation under [geostat-framework.readthedocs.io][doc_link].
+
+
+### Tutorials and Examples
+
+The documentation also includes some [tutorials][tut_link], showing the most important use cases of GSTools, which are
+
+- [Random Field Generation][tut1_link]
+- [The Covariance Model][tut2_link]
+- [Variogram Estimation][tut3_link]
+- [Random Vector Field Generation][tut4_link]
+- [Kriging][tut5_link]
+- [Conditioned random field generation][tut6_link]
+- [Field transformations][tut7_link]
+- [Geographic Coordinates][tut8_link]
+- [Spatio-Temporal Modelling][tut9_link]
+- [Normalizing Data][tut10_link]
+- [Miscellaneous examples][tut0_link]
+
+The associated python scripts are provided in the `examples` folder.
+
+
+## Spatial Random Field Generation
+
+The core of this library is the generation of spatial random fields. These fields are generated using the randomisation method, described by [Heße et al. 2014][rand_link].
+
+[rand_link]: https://doi.org/10.1016/j.envsoft.2014.01.013
+
+
+### Examples
+
+#### Gaussian Covariance Model
+
+This is an example of how to generate a 2 dimensional spatial random field with a gaussian covariance model.
+
+```python
+import gstools as gs
+# structured field with a size 100x100 and a grid-size of 1x1
+x = y = range(100)
+model = gs.Gaussian(dim=2, var=1, len_scale=10)
+srf = gs.SRF(model)
+srf((x, y), mesh_type='structured')
+srf.plot()
+```
+<p align="center">
+<img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/gau_field.png" alt="Random field" width="600px"/>
+</p>
+
+GSTools also provides support for [geographic coordinates](https://en.wikipedia.org/wiki/Geographic_coordinate_system).
+This works perfectly well with [cartopy](https://scitools.org.uk/cartopy/docs/latest/index.html).
+
+```python
+import matplotlib.pyplot as plt
+import cartopy.crs as ccrs
+import gstools as gs
+# define a structured field by latitude and longitude
+lat = lon = range(-80, 81)
+model = gs.Gaussian(latlon=True, len_scale=777, rescale=gs.EARTH_RADIUS)
+srf = gs.SRF(model, seed=12345)
+field = srf.structured((lat, lon))
+# Orthographic plotting with cartopy
+ax = plt.subplot(projection=ccrs.Orthographic(-45, 45))
+cont = ax.contourf(lon, lat, field, transform=ccrs.PlateCarree())
+ax.coastlines()
+ax.set_global()
+plt.colorbar(cont)
+```
+
+<p align="center">
+<img src="https://github.com/GeoStat-Framework/GeoStat-Framework.github.io/raw/master/img/GS_globe.png" alt="lat-lon random field" width="600px"/>
+</p>
+
+A similar example but for a three dimensional field is exported to a [VTK](https://vtk.org/) file, which can be visualized with [ParaView](https://www.paraview.org/) or [PyVista](https://docs.pyvista.org) in Python:
+
+```python
+import gstools as gs
+# structured field with a size 100x100x100 and a grid-size of 1x1x1
+x = y = z = range(100)
+model = gs.Gaussian(dim=3, len_scale=[16, 8, 4], angles=(0.8, 0.4, 0.2))
+srf = gs.SRF(model)
+srf((x, y, z), mesh_type='structured')
+srf.vtk_export('3d_field') # Save to a VTK file for ParaView
+
+mesh = srf.to_pyvista() # Create a PyVista mesh for plotting in Python
+mesh.contour(isosurfaces=8).plot()
+```
+
+<p align="center">
+<img src="https://github.com/GeoStat-Framework/GeoStat-Framework.github.io/raw/master/img/GS_pyvista.png" alt="3d Random field" width="600px"/>
+</p>
+
+
+## Estimating and Fitting Variograms
+
+The spatial structure of a field can be analyzed with the variogram, which contains the same information as the covariance function.
+
+All covariance models can be used to fit given variogram data by a simple interface.
+
+### Example
+
+This is an example of how to estimate the variogram of a 2 dimensional unstructured field and estimate the parameters of the covariance
+model again.
+
+```python
+import numpy as np
+import gstools as gs
+# generate a synthetic field with an exponential model
+x = np.random.RandomState(19970221).rand(1000) * 100.
+y = np.random.RandomState(20011012).rand(1000) * 100.
+model = gs.Exponential(dim=2, var=2, len_scale=8)
+srf = gs.SRF(model, mean=0, seed=19970221)
+field = srf((x, y))
+# estimate the variogram of the field
+bin_center, gamma = gs.vario_estimate((x, y), field)
+# fit the variogram with a stable model. (no nugget fitted)
+fit_model = gs.Stable(dim=2)
+fit_model.fit_variogram(bin_center, gamma, nugget=False)
+# output
+ax = fit_model.plot(x_max=max(bin_center))
+ax.scatter(bin_center, gamma)
+print(fit_model)
+```
+
+Which gives:
+
+```python
+Stable(dim=2, var=1.85, len_scale=7.42, nugget=0.0, anis=[1.0], angles=[0.0], alpha=1.09)
+```
+
+<p align="center">
+<img src="https://github.com/GeoStat-Framework/GeoStat-Framework.github.io/raw/master/img/GS_vario_est.png" alt="Variogram" width="600px"/>
+</p>
+
+
+## Kriging and Conditioned Random Fields
+
+An important part of geostatistics is Kriging and conditioning spatial random
+fields to measurements. With conditioned random fields, an ensemble of field realizations with their variability depending on the proximity of the measurements can be generated.
+
+### Example
+For better visualization, we will condition a 1d field to a few "measurements", generate 100 realizations and plot them:
+
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+import gstools as gs
+
+# conditions
+cond_pos = [0.3, 1.9, 1.1, 3.3, 4.7]
+cond_val = [0.47, 0.56, 0.74, 1.47, 1.74]
+
+# conditioned spatial random field class
+model = gs.Gaussian(dim=1, var=0.5, len_scale=2)
+krige = gs.krige.Ordinary(model, cond_pos, cond_val)
+cond_srf = gs.CondSRF(krige)
+# same output positions for all ensemble members
+grid_pos = np.linspace(0.0, 15.0, 151)
+cond_srf.set_pos(grid_pos)
+
+# seeded ensemble generation
+seed = gs.random.MasterRNG(20170519)
+for i in range(100):
+    field = cond_srf(seed=seed(), store=f"field_{i}")
+    plt.plot(grid_pos, field, color="k", alpha=0.1)
+plt.scatter(cond_pos, cond_val, color="k")
+plt.show()
+```
+
+<p align="center">
+<img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/cond_ens.png" alt="Conditioned" width="600px"/>
+</p>
+
+## User Defined Covariance Models
+
+One of the core-features of GSTools is the powerful
+[CovModel][cov_link]
+class, which allows to easy define covariance models by the user.
+
+### Example
+
+Here we re-implement the Gaussian covariance model by defining just a
+[correlation][cor_link] function, which takes a non-dimensional distance ``h = r/l``:
+
+```python
+import numpy as np
+import gstools as gs
+# use CovModel as the base-class
+class Gau(gs.CovModel):
+    def cor(self, h):
+        return np.exp(-h**2)
+```
+
+And that's it! With ``Gau`` you now have a fully working covariance model,
+which you could use for field generation or variogram fitting as shown above.
+
+Have a look at the [documentation ][doc_link] for further information on incorporating
+optional parameters and optimizations.
+
+
+## Incompressible Vector Field Generation
+
+Using the original [Kraichnan method][kraichnan_link], incompressible random
+spatial vector fields can be generated.
+
+
+### Example
+
+```python
+import numpy as np
+import gstools as gs
+x = np.arange(100)
+y = np.arange(100)
+model = gs.Gaussian(dim=2, var=1, len_scale=10)
+srf = gs.SRF(model, generator='VectorField', seed=19841203)
+srf((x, y), mesh_type='structured')
+srf.plot()
+```
+
+yielding
+
+<p align="center">
+<img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/vec_srf_tut_gau.png" alt="vector field" width="600px"/>
+</p>
+
+
+[kraichnan_link]: https://doi.org/10.1063/1.1692799
+
+
+## VTK/PyVista Export
+
+After you have created a field, you may want to save it to file, so we provide
+a handy [VTK][vtk_link] export routine using the `.vtk_export()` or you could
+create a VTK/PyVista dataset for use in Python with to `.to_pyvista()` method:
+
+```python
+import gstools as gs
+x = y = range(100)
+model = gs.Gaussian(dim=2, var=1, len_scale=10)
+srf = gs.SRF(model)
+srf((x, y), mesh_type='structured')
+srf.vtk_export("field") # Saves to a VTK file
+mesh = srf.to_pyvista() # Create a VTK/PyVista dataset in memory
+mesh.plot()
+```
+
+Which gives a RectilinearGrid VTK file ``field.vtr`` or creates a PyVista mesh
+in memory for immediate 3D plotting in Python.
+
+<p align="center">
+<img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/pyvista_export.png" alt="pyvista export" width="600px"/>
+</p>
+
+
+## Requirements:
+
+- [NumPy >= 1.14.5](https://www.numpy.org)
+- [SciPy >= 1.1.0](https://www.scipy.org/scipylib)
+- [hankel >= 1.0.0](https://github.com/steven-murray/hankel)
+- [emcee >= 3.0.0](https://github.com/dfm/emcee)
+- [pyevtk >= 1.1.1](https://github.com/pyscience-projects/pyevtk)
+- [meshio >= 5.1.0](https://github.com/nschloe/meshio)
+
+### Optional
+
+- [GSTools-Core >= 0.2.0](https://github.com/GeoStat-Framework/GSTools-Core)
+- [matplotlib](https://matplotlib.org)
+- [pyvista](https://docs.pyvista.org/)
+
+
+## Contact
+
+You can contact us via <info@geostat-framework.org>.
+
+
+## License
+
+[LGPLv3][license_link] © 2018-2021
+
+[pip_link]: https://pypi.org/project/gstools
+[conda_link]: https://docs.conda.io/en/latest/miniconda.html
+[conda_forge_link]: https://github.com/conda-forge/gstools-feedstock#installing-gstools
+[conda_pip]: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-pkgs.html#installing-non-conda-packages
+[pipiflag]: https://pip-python3.readthedocs.io/en/latest/reference/pip_install.html?highlight=i#cmdoption-i
+[winpy_link]: https://winpython.github.io/
+[license_link]: https://github.com/GeoStat-Framework/GSTools/blob/main/LICENSE
+[cov_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/generated/gstools.covmodel.CovModel.html#gstools.covmodel.CovModel
+[stable_link]: https://en.wikipedia.org/wiki/Stable_distribution
+[doc_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/
+[doc_install_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/#pip
+[tut_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/tutorials.html
+[tut1_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/01_random_field/index.html
+[tut2_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/02_cov_model/index.html
+[tut3_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/03_variogram/index.html
+[tut4_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/04_vector_field/index.html
+[tut5_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/05_kriging/index.html
+[tut6_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/06_conditioned_fields/index.html
+[tut7_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/07_transformations/index.html
+[tut8_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/08_geo_coordinates/index.html
+[tut9_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/09_spatio_temporal/index.html
+[tut10_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/10_normalizer/index.html
+[tut0_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/00_misc/index.html
+[cor_link]: https://en.wikipedia.org/wiki/Autocovariance#Normalization
+[vtk_link]: https://www.vtk.org/
+
+
+%package -n python3-gstools
+Summary:	GSTools: A geostatistical toolbox.
+Provides:	python-gstools
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+BuildRequires:	python3-cffi
+BuildRequires:	gcc
+BuildRequires:	gdb
+%description -n python3-gstools
+# Welcome to GSTools
+[![GMD](https://img.shields.io/badge/GMD-10.5194%2Fgmd--15--3161--2022-orange)](https://doi.org/10.5194/gmd-15-3161-2022)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1313628.svg)](https://doi.org/10.5281/zenodo.1313628)
+[![PyPI version](https://badge.fury.io/py/gstools.svg)](https://badge.fury.io/py/gstools)
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/gstools.svg)](https://anaconda.org/conda-forge/gstools)
+[![Build Status](https://github.com/GeoStat-Framework/GSTools/workflows/Continuous%20Integration/badge.svg?branch=main)](https://github.com/GeoStat-Framework/GSTools/actions)
+[![Coverage Status](https://coveralls.io/repos/github/GeoStat-Framework/GSTools/badge.svg?branch=main)](https://coveralls.io/github/GeoStat-Framework/GSTools?branch=main)
+[![Documentation Status](https://readthedocs.org/projects/gstools/badge/?version=latest)](https://geostat-framework.readthedocs.io/projects/gstools/en/stable/?badge=stable)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
+
+<p align="center">
+<img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/gstools.png" alt="GSTools-LOGO" width="251px"/>
+</p>
+
+<p align="center"><b>Get in Touch!</b></p>
+<p align="center">
+<a href="https://github.com/GeoStat-Framework/GSTools/discussions"><img src="https://img.shields.io/badge/GitHub-Discussions-f6f8fa?logo=github&style=flat" alt="GH-Discussions"/></a>
+<a href="https://swung.slack.com/messages/gstools"><img src="https://img.shields.io/badge/Swung-Slack-4A154B?style=flat&logo=data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAABoAAAAaCAYAAACpSkzOAAAABmJLR0QA%2FwD%2FAP%2BgvaeTAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAAB3RJTUUH5AYaFSENGSa5qgAABmZJREFUSMeFlltsVNcVhr%2B1z5m7Zzy%2BxaBwcQrGQOpgCAkKtSBQIqJepKhPBULpQ6sKBVWVKqXtSy%2BR0qYXqa2qRmlDCzjBEZGKUCK1TWqlNiGIEKDQBtf4Fki4OIxnxrex53LOXn2YwbjEtOvlHG3tvX%2Btf%2B21%2Fl%2BYJ1QVEbn1vwLYBWwCVgG1lW0ZoA%2FoAQ6LSP%2BdZ%2BeGzAMiIqK%2Bem0GpxNYVeBj3j2b4NCfM2QnfAAaa11al4fZuCZK24owQJ9v%2BbLryIVbd9wVSNUaEWNVtQPYfXHmAD0T32ZJeBM1Q8d0zzMDUpMwAFgLJU%2BxClURw9NfqedLWxMAHSKyR1WNiNhPAM0B6c%2FbdPORTLuOeUMSNkmMBHgyeo32bwwRDMh8bDM%2BZVl0j6uvPrdYknFnSESWzwUzt%2BkyVlUHx7zh5j%2BmPkXBjosjLkWdominiMQ%2BoiEZxuq8OFRXGXJ5K5%2Fde5nha8VlqjooIlZVBcBUiqeqemjGppd1ptfhSpS8pmmN7GVf4whPNY4Di9m%2BMcR03nK3sBbCQeFbv7gBsExVOyp3l6nz1VtjcM4fTK3Uok5IXtPsrHuPevcBXk8d4dWPX6I%2BsIB9wf1s%2B2Y%2FVbFynUIBIeDeplIECiXl5Iv3kbLogogRgbWukfNumT%2FnlYszBxj3hwXg0cQvqXcfYNu5tVyYPE%2B1G8dXn%2BfW72fH49U8sSlOPGr4SccoF4cKs3WzFrY%2BFCMUNmz%2Ba0aeWR1l15JwJ7DaVPpk1YnJ7xIxtQRNjDXRvTx%2F9ef0Tl0g6SYQhAlvmkH%2Fgv74qUaiTSG8ewJ0%2FGgRK5aG8Cts5ouWDa1RxoDRovK9i9MAq1S12QA7b5ROUdBxBIeQ1ACG49m%2FEXPis7Qk3ChHbx6Qw1dgXVeWB7uyDOctP%2Fx6w2zdrIVIyFCyiq8wXlJOZzyAXQbY%2FGGhC8EAilJ%2BVg7ufxU6IAHeSvewfQEadiDuCr%2B6NE1LU4hwUFAF1xFGRkvEjVDlgiPwVqoEsNkAq0ZKp3EIYrFM2xGm7Uc8u%2FzXjHkTmHIHoCiDM73E3IIsDCtRV3gn7QHQ0hTCt0ooKLw%2FWCAM1AcNISOcHSsBrDRAbc7eQMQBFFciHM18kaZIMz3r%2F0HO5mazytsiw%2FmTtCYiGGCkQlltwkEVjMDVmyUA6oIGR%2BDGjAWoM3f2giHAhH%2BFI5nPsDrWxqWNE9S4tUz5k1S7cQ5df4k9S6qY9JRipXtr4w5WQYH0eHkWrqxy8FTn3AvpmFmIqj%2B76EiQjNfHH1JNWFKc3vABj9V9npw%2FRXfmBNsaoTRnRAQDAgqqMJr1KBWUtUmHaR8WRgzAqAH6FgYexqd4R2Yuns5wcLSFK4U36bj%2FdbbUbGdoZoCi3uS%2Bqtt73TlNWygpqXGfZTGXnKesrwkA9BmgZ0noMZT5R0tQ4hzLfo4rhS46W%2F%2BCAn3T7%2BhDySiWMl2RkHArP8dAesKjPixYVbbUBwB6DHB4QWADIamuHPtkhE0t3ZP7ANhe9zgvXP2dfK0pymRJmQLiEYNW6mEVljYGuDzlkwwaHq51AQ4bERkAetvjP2XCT6H480AJeZsB4N7QYt7OnuSROtRXJV2wNNS4qIJvlbUtERJxhxcv5%2FlNWwygV0QGyzKBv%2FP%2ByFfZXf%2ButoR3UuXcS95mKNgxSjpN3qZZFHwUgFPjx5n2c9wo9ktrtcOZtMeWB2NEw4b2thivPLuIS1M%2BAzmrTy4O4ys7Zv1B5fsnVdWCr7PxYf7vej73ex2YeU1VVY9nu7ShG63vRo%2Fe%2FK1%2B518FbXkjo3OjO1XU2LFRzRZ9VdWDczFQ1VsCOHgpd1G%2FcG6jHrj2vPbn%2BjVdHNfr%2BRH92eXva2MPuvxEQpe%2BHdEnzm%2FQf4%2BrRo%2BldMUbGd393oS2dWU0cDSlw1OequrALVG9Q8rLsquqg2OlzLL2Myu1N5eShgB4CjEnSMSJYrX8Oj0t8UH7NMnX0iSDwmhBWRl3tKs9IcmgGRSRZqtqzFwpL4uWWKvWiMjyZKC24%2F1HbsrLn95Pwk3gCpS0yIw%2Fg6clPC2RLc3QmzvJupoARQsvrItxZmtSkkFz6E6Q%2F2m3PFta44jbCaw%2BO3GK7uybnJs8xfXC1fLYCdTz9NIfsCS0mYVhAHp9ZYdr5J%2F%2F127dxUA2AzuBzRUDWVfZlq4YyG6gs9ImdzWQ%2FwFNRlgCFdG5bAAAAABJRU5ErkJggg%3D%3D" alt="Slack-Swung"/></a>
+<a href="https://gitter.im/GeoStat-Framework/GSTools"><img src="https://img.shields.io/badge/Gitter-GeoStat--Framework-ed1965?logo=gitter&style=flat" alt="Gitter-GSTools"/></a>
+<a href="mailto:info@geostat-framework.org"><img src="https://img.shields.io/badge/Email-GeoStat--Framework-468a88?style=flat&logo=data:image/svg+xml;base64,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" alt="Email"/></a>
+<a href="https://twitter.com/GSFramework"><img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/GSFramework?style=social"></a>
+</p>
+
+<p align="center"><b>Youtube Tutorial on GSTools</b><br></p>
+
+<p align="center">
+<a href="http://www.youtube.com/watch?feature=player_embedded&v=qZBJ-AZXq6Q" target="_blank">
+<img src="http://img.youtube.com/vi/qZBJ-AZXq6Q/0.jpg" alt="GSTools Transform 22 tutorial" width="480" height="360" border="0" />
+</a>
+</p>
+
+## Purpose
+
+<img align="right" width="450" src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/demonstrator.png" alt="">
+
+GeoStatTools provides geostatistical tools for various purposes:
+- random field generation
+- simple, ordinary, universal and external drift kriging
+- conditioned field generation
+- incompressible random vector field generation
+- (automated) variogram estimation and fitting
+- directional variogram estimation and modelling
+- data normalization and transformation
+- many readily provided and even user-defined covariance models
+- metric spatio-temporal modelling
+- plotting and exporting routines
+
+
+## Installation
+
+
+### conda
+
+GSTools can be installed via [conda][conda_link] on Linux, Mac, and Windows.
+Install the package by typing the following command in a command terminal:
+
+    conda install gstools
+
+In case conda forge is not set up for your system yet, see the easy to follow
+instructions on [conda forge][conda_forge_link]. Using conda, the parallelized
+version of GSTools should be installed.
+
+
+### pip
+
+GSTools can be installed via [pip][pip_link] on Linux, Mac, and Windows.
+On Windows you can install [WinPython][winpy_link] to get Python and pip
+running. Install the package by typing the following command in a command terminal:
+
+    pip install gstools
+
+To install the latest development version via pip, see the
+[documentation][doc_install_link].
+
+
+## Citation
+
+If you are using GSTools in your publication please cite our paper:
+
+> Müller, S., Schüler, L., Zech, A., and Heße, F.:
+> GSTools v1.3: a toolbox for geostatistical modelling in Python,
+> Geosci. Model Dev., 15, 3161–3182, https://doi.org/10.5194/gmd-15-3161-2022, 2022.
+
+You can cite the Zenodo code publication of GSTools by:
+
+> Sebastian Müller & Lennart Schüler. GeoStat-Framework/GSTools. Zenodo. https://doi.org/10.5281/zenodo.1313628
+
+If you want to cite a specific version, have a look at the [Zenodo site](https://doi.org/10.5281/zenodo.1313628).
+
+
+## Documentation for GSTools
+
+You can find the documentation under [geostat-framework.readthedocs.io][doc_link].
+
+
+### Tutorials and Examples
+
+The documentation also includes some [tutorials][tut_link], showing the most important use cases of GSTools, which are
+
+- [Random Field Generation][tut1_link]
+- [The Covariance Model][tut2_link]
+- [Variogram Estimation][tut3_link]
+- [Random Vector Field Generation][tut4_link]
+- [Kriging][tut5_link]
+- [Conditioned random field generation][tut6_link]
+- [Field transformations][tut7_link]
+- [Geographic Coordinates][tut8_link]
+- [Spatio-Temporal Modelling][tut9_link]
+- [Normalizing Data][tut10_link]
+- [Miscellaneous examples][tut0_link]
+
+The associated python scripts are provided in the `examples` folder.
+
+
+## Spatial Random Field Generation
+
+The core of this library is the generation of spatial random fields. These fields are generated using the randomisation method, described by [Heße et al. 2014][rand_link].
+
+[rand_link]: https://doi.org/10.1016/j.envsoft.2014.01.013
+
+
+### Examples
+
+#### Gaussian Covariance Model
+
+This is an example of how to generate a 2 dimensional spatial random field with a gaussian covariance model.
+
+```python
+import gstools as gs
+# structured field with a size 100x100 and a grid-size of 1x1
+x = y = range(100)
+model = gs.Gaussian(dim=2, var=1, len_scale=10)
+srf = gs.SRF(model)
+srf((x, y), mesh_type='structured')
+srf.plot()
+```
+<p align="center">
+<img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/gau_field.png" alt="Random field" width="600px"/>
+</p>
+
+GSTools also provides support for [geographic coordinates](https://en.wikipedia.org/wiki/Geographic_coordinate_system).
+This works perfectly well with [cartopy](https://scitools.org.uk/cartopy/docs/latest/index.html).
+
+```python
+import matplotlib.pyplot as plt
+import cartopy.crs as ccrs
+import gstools as gs
+# define a structured field by latitude and longitude
+lat = lon = range(-80, 81)
+model = gs.Gaussian(latlon=True, len_scale=777, rescale=gs.EARTH_RADIUS)
+srf = gs.SRF(model, seed=12345)
+field = srf.structured((lat, lon))
+# Orthographic plotting with cartopy
+ax = plt.subplot(projection=ccrs.Orthographic(-45, 45))
+cont = ax.contourf(lon, lat, field, transform=ccrs.PlateCarree())
+ax.coastlines()
+ax.set_global()
+plt.colorbar(cont)
+```
+
+<p align="center">
+<img src="https://github.com/GeoStat-Framework/GeoStat-Framework.github.io/raw/master/img/GS_globe.png" alt="lat-lon random field" width="600px"/>
+</p>
+
+A similar example but for a three dimensional field is exported to a [VTK](https://vtk.org/) file, which can be visualized with [ParaView](https://www.paraview.org/) or [PyVista](https://docs.pyvista.org) in Python:
+
+```python
+import gstools as gs
+# structured field with a size 100x100x100 and a grid-size of 1x1x1
+x = y = z = range(100)
+model = gs.Gaussian(dim=3, len_scale=[16, 8, 4], angles=(0.8, 0.4, 0.2))
+srf = gs.SRF(model)
+srf((x, y, z), mesh_type='structured')
+srf.vtk_export('3d_field') # Save to a VTK file for ParaView
+
+mesh = srf.to_pyvista() # Create a PyVista mesh for plotting in Python
+mesh.contour(isosurfaces=8).plot()
+```
+
+<p align="center">
+<img src="https://github.com/GeoStat-Framework/GeoStat-Framework.github.io/raw/master/img/GS_pyvista.png" alt="3d Random field" width="600px"/>
+</p>
+
+
+## Estimating and Fitting Variograms
+
+The spatial structure of a field can be analyzed with the variogram, which contains the same information as the covariance function.
+
+All covariance models can be used to fit given variogram data by a simple interface.
+
+### Example
+
+This is an example of how to estimate the variogram of a 2 dimensional unstructured field and estimate the parameters of the covariance
+model again.
+
+```python
+import numpy as np
+import gstools as gs
+# generate a synthetic field with an exponential model
+x = np.random.RandomState(19970221).rand(1000) * 100.
+y = np.random.RandomState(20011012).rand(1000) * 100.
+model = gs.Exponential(dim=2, var=2, len_scale=8)
+srf = gs.SRF(model, mean=0, seed=19970221)
+field = srf((x, y))
+# estimate the variogram of the field
+bin_center, gamma = gs.vario_estimate((x, y), field)
+# fit the variogram with a stable model. (no nugget fitted)
+fit_model = gs.Stable(dim=2)
+fit_model.fit_variogram(bin_center, gamma, nugget=False)
+# output
+ax = fit_model.plot(x_max=max(bin_center))
+ax.scatter(bin_center, gamma)
+print(fit_model)
+```
+
+Which gives:
+
+```python
+Stable(dim=2, var=1.85, len_scale=7.42, nugget=0.0, anis=[1.0], angles=[0.0], alpha=1.09)
+```
+
+<p align="center">
+<img src="https://github.com/GeoStat-Framework/GeoStat-Framework.github.io/raw/master/img/GS_vario_est.png" alt="Variogram" width="600px"/>
+</p>
+
+
+## Kriging and Conditioned Random Fields
+
+An important part of geostatistics is Kriging and conditioning spatial random
+fields to measurements. With conditioned random fields, an ensemble of field realizations with their variability depending on the proximity of the measurements can be generated.
+
+### Example
+For better visualization, we will condition a 1d field to a few "measurements", generate 100 realizations and plot them:
+
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+import gstools as gs
+
+# conditions
+cond_pos = [0.3, 1.9, 1.1, 3.3, 4.7]
+cond_val = [0.47, 0.56, 0.74, 1.47, 1.74]
+
+# conditioned spatial random field class
+model = gs.Gaussian(dim=1, var=0.5, len_scale=2)
+krige = gs.krige.Ordinary(model, cond_pos, cond_val)
+cond_srf = gs.CondSRF(krige)
+# same output positions for all ensemble members
+grid_pos = np.linspace(0.0, 15.0, 151)
+cond_srf.set_pos(grid_pos)
+
+# seeded ensemble generation
+seed = gs.random.MasterRNG(20170519)
+for i in range(100):
+    field = cond_srf(seed=seed(), store=f"field_{i}")
+    plt.plot(grid_pos, field, color="k", alpha=0.1)
+plt.scatter(cond_pos, cond_val, color="k")
+plt.show()
+```
+
+<p align="center">
+<img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/cond_ens.png" alt="Conditioned" width="600px"/>
+</p>
+
+## User Defined Covariance Models
+
+One of the core-features of GSTools is the powerful
+[CovModel][cov_link]
+class, which allows to easy define covariance models by the user.
+
+### Example
+
+Here we re-implement the Gaussian covariance model by defining just a
+[correlation][cor_link] function, which takes a non-dimensional distance ``h = r/l``:
+
+```python
+import numpy as np
+import gstools as gs
+# use CovModel as the base-class
+class Gau(gs.CovModel):
+    def cor(self, h):
+        return np.exp(-h**2)
+```
+
+And that's it! With ``Gau`` you now have a fully working covariance model,
+which you could use for field generation or variogram fitting as shown above.
+
+Have a look at the [documentation ][doc_link] for further information on incorporating
+optional parameters and optimizations.
+
+
+## Incompressible Vector Field Generation
+
+Using the original [Kraichnan method][kraichnan_link], incompressible random
+spatial vector fields can be generated.
+
+
+### Example
+
+```python
+import numpy as np
+import gstools as gs
+x = np.arange(100)
+y = np.arange(100)
+model = gs.Gaussian(dim=2, var=1, len_scale=10)
+srf = gs.SRF(model, generator='VectorField', seed=19841203)
+srf((x, y), mesh_type='structured')
+srf.plot()
+```
+
+yielding
+
+<p align="center">
+<img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/vec_srf_tut_gau.png" alt="vector field" width="600px"/>
+</p>
+
+
+[kraichnan_link]: https://doi.org/10.1063/1.1692799
+
+
+## VTK/PyVista Export
+
+After you have created a field, you may want to save it to file, so we provide
+a handy [VTK][vtk_link] export routine using the `.vtk_export()` or you could
+create a VTK/PyVista dataset for use in Python with to `.to_pyvista()` method:
+
+```python
+import gstools as gs
+x = y = range(100)
+model = gs.Gaussian(dim=2, var=1, len_scale=10)
+srf = gs.SRF(model)
+srf((x, y), mesh_type='structured')
+srf.vtk_export("field") # Saves to a VTK file
+mesh = srf.to_pyvista() # Create a VTK/PyVista dataset in memory
+mesh.plot()
+```
+
+Which gives a RectilinearGrid VTK file ``field.vtr`` or creates a PyVista mesh
+in memory for immediate 3D plotting in Python.
+
+<p align="center">
+<img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/pyvista_export.png" alt="pyvista export" width="600px"/>
+</p>
+
+
+## Requirements:
+
+- [NumPy >= 1.14.5](https://www.numpy.org)
+- [SciPy >= 1.1.0](https://www.scipy.org/scipylib)
+- [hankel >= 1.0.0](https://github.com/steven-murray/hankel)
+- [emcee >= 3.0.0](https://github.com/dfm/emcee)
+- [pyevtk >= 1.1.1](https://github.com/pyscience-projects/pyevtk)
+- [meshio >= 5.1.0](https://github.com/nschloe/meshio)
+
+### Optional
+
+- [GSTools-Core >= 0.2.0](https://github.com/GeoStat-Framework/GSTools-Core)
+- [matplotlib](https://matplotlib.org)
+- [pyvista](https://docs.pyvista.org/)
+
+
+## Contact
+
+You can contact us via <info@geostat-framework.org>.
+
+
+## License
+
+[LGPLv3][license_link] © 2018-2021
+
+[pip_link]: https://pypi.org/project/gstools
+[conda_link]: https://docs.conda.io/en/latest/miniconda.html
+[conda_forge_link]: https://github.com/conda-forge/gstools-feedstock#installing-gstools
+[conda_pip]: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-pkgs.html#installing-non-conda-packages
+[pipiflag]: https://pip-python3.readthedocs.io/en/latest/reference/pip_install.html?highlight=i#cmdoption-i
+[winpy_link]: https://winpython.github.io/
+[license_link]: https://github.com/GeoStat-Framework/GSTools/blob/main/LICENSE
+[cov_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/generated/gstools.covmodel.CovModel.html#gstools.covmodel.CovModel
+[stable_link]: https://en.wikipedia.org/wiki/Stable_distribution
+[doc_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/
+[doc_install_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/#pip
+[tut_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/tutorials.html
+[tut1_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/01_random_field/index.html
+[tut2_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/02_cov_model/index.html
+[tut3_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/03_variogram/index.html
+[tut4_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/04_vector_field/index.html
+[tut5_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/05_kriging/index.html
+[tut6_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/06_conditioned_fields/index.html
+[tut7_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/07_transformations/index.html
+[tut8_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/08_geo_coordinates/index.html
+[tut9_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/09_spatio_temporal/index.html
+[tut10_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/10_normalizer/index.html
+[tut0_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/00_misc/index.html
+[cor_link]: https://en.wikipedia.org/wiki/Autocovariance#Normalization
+[vtk_link]: https://www.vtk.org/
+
+
+%package help
+Summary:	Development documents and examples for gstools
+Provides:	python3-gstools-doc
+%description help
+# Welcome to GSTools
+[![GMD](https://img.shields.io/badge/GMD-10.5194%2Fgmd--15--3161--2022-orange)](https://doi.org/10.5194/gmd-15-3161-2022)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1313628.svg)](https://doi.org/10.5281/zenodo.1313628)
+[![PyPI version](https://badge.fury.io/py/gstools.svg)](https://badge.fury.io/py/gstools)
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/gstools.svg)](https://anaconda.org/conda-forge/gstools)
+[![Build Status](https://github.com/GeoStat-Framework/GSTools/workflows/Continuous%20Integration/badge.svg?branch=main)](https://github.com/GeoStat-Framework/GSTools/actions)
+[![Coverage Status](https://coveralls.io/repos/github/GeoStat-Framework/GSTools/badge.svg?branch=main)](https://coveralls.io/github/GeoStat-Framework/GSTools?branch=main)
+[![Documentation Status](https://readthedocs.org/projects/gstools/badge/?version=latest)](https://geostat-framework.readthedocs.io/projects/gstools/en/stable/?badge=stable)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
+
+<p align="center">
+<img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/gstools.png" alt="GSTools-LOGO" width="251px"/>
+</p>
+
+<p align="center"><b>Get in Touch!</b></p>
+<p align="center">
+<a href="https://github.com/GeoStat-Framework/GSTools/discussions"><img src="https://img.shields.io/badge/GitHub-Discussions-f6f8fa?logo=github&style=flat" alt="GH-Discussions"/></a>
+<a href="https://swung.slack.com/messages/gstools"><img src="https://img.shields.io/badge/Swung-Slack-4A154B?style=flat&logo=data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAABoAAAAaCAYAAACpSkzOAAAABmJLR0QA%2FwD%2FAP%2BgvaeTAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAAB3RJTUUH5AYaFSENGSa5qgAABmZJREFUSMeFlltsVNcVhr%2B1z5m7Zzy%2BxaBwcQrGQOpgCAkKtSBQIqJepKhPBULpQ6sKBVWVKqXtSy%2BR0qYXqa2qRmlDCzjBEZGKUCK1TWqlNiGIEKDQBtf4Fki4OIxnxrex53LOXn2YwbjEtOvlHG3tvX%2Btf%2B21%2Fl%2BYJ1QVEbn1vwLYBWwCVgG1lW0ZoA%2FoAQ6LSP%2BdZ%2BeGzAMiIqK%2Bem0GpxNYVeBj3j2b4NCfM2QnfAAaa11al4fZuCZK24owQJ9v%2BbLryIVbd9wVSNUaEWNVtQPYfXHmAD0T32ZJeBM1Q8d0zzMDUpMwAFgLJU%2BxClURw9NfqedLWxMAHSKyR1WNiNhPAM0B6c%2FbdPORTLuOeUMSNkmMBHgyeo32bwwRDMh8bDM%2BZVl0j6uvPrdYknFnSESWzwUzt%2BkyVlUHx7zh5j%2BmPkXBjosjLkWdominiMQ%2BoiEZxuq8OFRXGXJ5K5%2Fde5nha8VlqjooIlZVBcBUiqeqemjGppd1ptfhSpS8pmmN7GVf4whPNY4Di9m%2BMcR03nK3sBbCQeFbv7gBsExVOyp3l6nz1VtjcM4fTK3Uok5IXtPsrHuPevcBXk8d4dWPX6I%2BsIB9wf1s%2B2Y%2FVbFynUIBIeDeplIECiXl5Iv3kbLogogRgbWukfNumT%2FnlYszBxj3hwXg0cQvqXcfYNu5tVyYPE%2B1G8dXn%2BfW72fH49U8sSlOPGr4SccoF4cKs3WzFrY%2BFCMUNmz%2Ba0aeWR1l15JwJ7DaVPpk1YnJ7xIxtQRNjDXRvTx%2F9ef0Tl0g6SYQhAlvmkH%2Fgv74qUaiTSG8ewJ0%2FGgRK5aG8Cts5ouWDa1RxoDRovK9i9MAq1S12QA7b5ROUdBxBIeQ1ACG49m%2FEXPis7Qk3ChHbx6Qw1dgXVeWB7uyDOctP%2Fx6w2zdrIVIyFCyiq8wXlJOZzyAXQbY%2FGGhC8EAilJ%2BVg7ufxU6IAHeSvewfQEadiDuCr%2B6NE1LU4hwUFAF1xFGRkvEjVDlgiPwVqoEsNkAq0ZKp3EIYrFM2xGm7Uc8u%2FzXjHkTmHIHoCiDM73E3IIsDCtRV3gn7QHQ0hTCt0ooKLw%2FWCAM1AcNISOcHSsBrDRAbc7eQMQBFFciHM18kaZIMz3r%2F0HO5mazytsiw%2FmTtCYiGGCkQlltwkEVjMDVmyUA6oIGR%2BDGjAWoM3f2giHAhH%2BFI5nPsDrWxqWNE9S4tUz5k1S7cQ5df4k9S6qY9JRipXtr4w5WQYH0eHkWrqxy8FTn3AvpmFmIqj%2B76EiQjNfHH1JNWFKc3vABj9V9npw%2FRXfmBNsaoTRnRAQDAgqqMJr1KBWUtUmHaR8WRgzAqAH6FgYexqd4R2Yuns5wcLSFK4U36bj%2FdbbUbGdoZoCi3uS%2Bqtt73TlNWygpqXGfZTGXnKesrwkA9BmgZ0noMZT5R0tQ4hzLfo4rhS46W%2F%2BCAn3T7%2BhDySiWMl2RkHArP8dAesKjPixYVbbUBwB6DHB4QWADIamuHPtkhE0t3ZP7ANhe9zgvXP2dfK0pymRJmQLiEYNW6mEVljYGuDzlkwwaHq51AQ4bERkAetvjP2XCT6H480AJeZsB4N7QYt7OnuSROtRXJV2wNNS4qIJvlbUtERJxhxcv5%2FlNWwygV0QGyzKBv%2FP%2ByFfZXf%2ButoR3UuXcS95mKNgxSjpN3qZZFHwUgFPjx5n2c9wo9ktrtcOZtMeWB2NEw4b2thivPLuIS1M%2BAzmrTy4O4ys7Zv1B5fsnVdWCr7PxYf7vej73ex2YeU1VVY9nu7ShG63vRo%2Fe%2FK1%2B518FbXkjo3OjO1XU2LFRzRZ9VdWDczFQ1VsCOHgpd1G%2FcG6jHrj2vPbn%2BjVdHNfr%2BRH92eXva2MPuvxEQpe%2BHdEnzm%2FQf4%2BrRo%2BldMUbGd393oS2dWU0cDSlw1OequrALVG9Q8rLsquqg2OlzLL2Myu1N5eShgB4CjEnSMSJYrX8Oj0t8UH7NMnX0iSDwmhBWRl3tKs9IcmgGRSRZqtqzFwpL4uWWKvWiMjyZKC24%2F1HbsrLn95Pwk3gCpS0yIw%2Fg6clPC2RLc3QmzvJupoARQsvrItxZmtSkkFz6E6Q%2F2m3PFta44jbCaw%2BO3GK7uybnJs8xfXC1fLYCdTz9NIfsCS0mYVhAHp9ZYdr5J%2F%2F127dxUA2AzuBzRUDWVfZlq4YyG6gs9ImdzWQ%2FwFNRlgCFdG5bAAAAABJRU5ErkJggg%3D%3D" alt="Slack-Swung"/></a>
+<a href="https://gitter.im/GeoStat-Framework/GSTools"><img src="https://img.shields.io/badge/Gitter-GeoStat--Framework-ed1965?logo=gitter&style=flat" alt="Gitter-GSTools"/></a>
+<a href="mailto:info@geostat-framework.org"><img src="https://img.shields.io/badge/Email-GeoStat--Framework-468a88?style=flat&logo=data:image/svg+xml;base64,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" alt="Email"/></a>
+<a href="https://twitter.com/GSFramework"><img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/GSFramework?style=social"></a>
+</p>
+
+<p align="center"><b>Youtube Tutorial on GSTools</b><br></p>
+
+<p align="center">
+<a href="http://www.youtube.com/watch?feature=player_embedded&v=qZBJ-AZXq6Q" target="_blank">
+<img src="http://img.youtube.com/vi/qZBJ-AZXq6Q/0.jpg" alt="GSTools Transform 22 tutorial" width="480" height="360" border="0" />
+</a>
+</p>
+
+## Purpose
+
+<img align="right" width="450" src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/demonstrator.png" alt="">
+
+GeoStatTools provides geostatistical tools for various purposes:
+- random field generation
+- simple, ordinary, universal and external drift kriging
+- conditioned field generation
+- incompressible random vector field generation
+- (automated) variogram estimation and fitting
+- directional variogram estimation and modelling
+- data normalization and transformation
+- many readily provided and even user-defined covariance models
+- metric spatio-temporal modelling
+- plotting and exporting routines
+
+
+## Installation
+
+
+### conda
+
+GSTools can be installed via [conda][conda_link] on Linux, Mac, and Windows.
+Install the package by typing the following command in a command terminal:
+
+    conda install gstools
+
+In case conda forge is not set up for your system yet, see the easy to follow
+instructions on [conda forge][conda_forge_link]. Using conda, the parallelized
+version of GSTools should be installed.
+
+
+### pip
+
+GSTools can be installed via [pip][pip_link] on Linux, Mac, and Windows.
+On Windows you can install [WinPython][winpy_link] to get Python and pip
+running. Install the package by typing the following command in a command terminal:
+
+    pip install gstools
+
+To install the latest development version via pip, see the
+[documentation][doc_install_link].
+
+
+## Citation
+
+If you are using GSTools in your publication please cite our paper:
+
+> Müller, S., Schüler, L., Zech, A., and Heße, F.:
+> GSTools v1.3: a toolbox for geostatistical modelling in Python,
+> Geosci. Model Dev., 15, 3161–3182, https://doi.org/10.5194/gmd-15-3161-2022, 2022.
+
+You can cite the Zenodo code publication of GSTools by:
+
+> Sebastian Müller & Lennart Schüler. GeoStat-Framework/GSTools. Zenodo. https://doi.org/10.5281/zenodo.1313628
+
+If you want to cite a specific version, have a look at the [Zenodo site](https://doi.org/10.5281/zenodo.1313628).
+
+
+## Documentation for GSTools
+
+You can find the documentation under [geostat-framework.readthedocs.io][doc_link].
+
+
+### Tutorials and Examples
+
+The documentation also includes some [tutorials][tut_link], showing the most important use cases of GSTools, which are
+
+- [Random Field Generation][tut1_link]
+- [The Covariance Model][tut2_link]
+- [Variogram Estimation][tut3_link]
+- [Random Vector Field Generation][tut4_link]
+- [Kriging][tut5_link]
+- [Conditioned random field generation][tut6_link]
+- [Field transformations][tut7_link]
+- [Geographic Coordinates][tut8_link]
+- [Spatio-Temporal Modelling][tut9_link]
+- [Normalizing Data][tut10_link]
+- [Miscellaneous examples][tut0_link]
+
+The associated python scripts are provided in the `examples` folder.
+
+
+## Spatial Random Field Generation
+
+The core of this library is the generation of spatial random fields. These fields are generated using the randomisation method, described by [Heße et al. 2014][rand_link].
+
+[rand_link]: https://doi.org/10.1016/j.envsoft.2014.01.013
+
+
+### Examples
+
+#### Gaussian Covariance Model
+
+This is an example of how to generate a 2 dimensional spatial random field with a gaussian covariance model.
+
+```python
+import gstools as gs
+# structured field with a size 100x100 and a grid-size of 1x1
+x = y = range(100)
+model = gs.Gaussian(dim=2, var=1, len_scale=10)
+srf = gs.SRF(model)
+srf((x, y), mesh_type='structured')
+srf.plot()
+```
+<p align="center">
+<img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/gau_field.png" alt="Random field" width="600px"/>
+</p>
+
+GSTools also provides support for [geographic coordinates](https://en.wikipedia.org/wiki/Geographic_coordinate_system).
+This works perfectly well with [cartopy](https://scitools.org.uk/cartopy/docs/latest/index.html).
+
+```python
+import matplotlib.pyplot as plt
+import cartopy.crs as ccrs
+import gstools as gs
+# define a structured field by latitude and longitude
+lat = lon = range(-80, 81)
+model = gs.Gaussian(latlon=True, len_scale=777, rescale=gs.EARTH_RADIUS)
+srf = gs.SRF(model, seed=12345)
+field = srf.structured((lat, lon))
+# Orthographic plotting with cartopy
+ax = plt.subplot(projection=ccrs.Orthographic(-45, 45))
+cont = ax.contourf(lon, lat, field, transform=ccrs.PlateCarree())
+ax.coastlines()
+ax.set_global()
+plt.colorbar(cont)
+```
+
+<p align="center">
+<img src="https://github.com/GeoStat-Framework/GeoStat-Framework.github.io/raw/master/img/GS_globe.png" alt="lat-lon random field" width="600px"/>
+</p>
+
+A similar example but for a three dimensional field is exported to a [VTK](https://vtk.org/) file, which can be visualized with [ParaView](https://www.paraview.org/) or [PyVista](https://docs.pyvista.org) in Python:
+
+```python
+import gstools as gs
+# structured field with a size 100x100x100 and a grid-size of 1x1x1
+x = y = z = range(100)
+model = gs.Gaussian(dim=3, len_scale=[16, 8, 4], angles=(0.8, 0.4, 0.2))
+srf = gs.SRF(model)
+srf((x, y, z), mesh_type='structured')
+srf.vtk_export('3d_field') # Save to a VTK file for ParaView
+
+mesh = srf.to_pyvista() # Create a PyVista mesh for plotting in Python
+mesh.contour(isosurfaces=8).plot()
+```
+
+<p align="center">
+<img src="https://github.com/GeoStat-Framework/GeoStat-Framework.github.io/raw/master/img/GS_pyvista.png" alt="3d Random field" width="600px"/>
+</p>
+
+
+## Estimating and Fitting Variograms
+
+The spatial structure of a field can be analyzed with the variogram, which contains the same information as the covariance function.
+
+All covariance models can be used to fit given variogram data by a simple interface.
+
+### Example
+
+This is an example of how to estimate the variogram of a 2 dimensional unstructured field and estimate the parameters of the covariance
+model again.
+
+```python
+import numpy as np
+import gstools as gs
+# generate a synthetic field with an exponential model
+x = np.random.RandomState(19970221).rand(1000) * 100.
+y = np.random.RandomState(20011012).rand(1000) * 100.
+model = gs.Exponential(dim=2, var=2, len_scale=8)
+srf = gs.SRF(model, mean=0, seed=19970221)
+field = srf((x, y))
+# estimate the variogram of the field
+bin_center, gamma = gs.vario_estimate((x, y), field)
+# fit the variogram with a stable model. (no nugget fitted)
+fit_model = gs.Stable(dim=2)
+fit_model.fit_variogram(bin_center, gamma, nugget=False)
+# output
+ax = fit_model.plot(x_max=max(bin_center))
+ax.scatter(bin_center, gamma)
+print(fit_model)
+```
+
+Which gives:
+
+```python
+Stable(dim=2, var=1.85, len_scale=7.42, nugget=0.0, anis=[1.0], angles=[0.0], alpha=1.09)
+```
+
+<p align="center">
+<img src="https://github.com/GeoStat-Framework/GeoStat-Framework.github.io/raw/master/img/GS_vario_est.png" alt="Variogram" width="600px"/>
+</p>
+
+
+## Kriging and Conditioned Random Fields
+
+An important part of geostatistics is Kriging and conditioning spatial random
+fields to measurements. With conditioned random fields, an ensemble of field realizations with their variability depending on the proximity of the measurements can be generated.
+
+### Example
+For better visualization, we will condition a 1d field to a few "measurements", generate 100 realizations and plot them:
+
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+import gstools as gs
+
+# conditions
+cond_pos = [0.3, 1.9, 1.1, 3.3, 4.7]
+cond_val = [0.47, 0.56, 0.74, 1.47, 1.74]
+
+# conditioned spatial random field class
+model = gs.Gaussian(dim=1, var=0.5, len_scale=2)
+krige = gs.krige.Ordinary(model, cond_pos, cond_val)
+cond_srf = gs.CondSRF(krige)
+# same output positions for all ensemble members
+grid_pos = np.linspace(0.0, 15.0, 151)
+cond_srf.set_pos(grid_pos)
+
+# seeded ensemble generation
+seed = gs.random.MasterRNG(20170519)
+for i in range(100):
+    field = cond_srf(seed=seed(), store=f"field_{i}")
+    plt.plot(grid_pos, field, color="k", alpha=0.1)
+plt.scatter(cond_pos, cond_val, color="k")
+plt.show()
+```
+
+<p align="center">
+<img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/cond_ens.png" alt="Conditioned" width="600px"/>
+</p>
+
+## User Defined Covariance Models
+
+One of the core-features of GSTools is the powerful
+[CovModel][cov_link]
+class, which allows to easy define covariance models by the user.
+
+### Example
+
+Here we re-implement the Gaussian covariance model by defining just a
+[correlation][cor_link] function, which takes a non-dimensional distance ``h = r/l``:
+
+```python
+import numpy as np
+import gstools as gs
+# use CovModel as the base-class
+class Gau(gs.CovModel):
+    def cor(self, h):
+        return np.exp(-h**2)
+```
+
+And that's it! With ``Gau`` you now have a fully working covariance model,
+which you could use for field generation or variogram fitting as shown above.
+
+Have a look at the [documentation ][doc_link] for further information on incorporating
+optional parameters and optimizations.
+
+
+## Incompressible Vector Field Generation
+
+Using the original [Kraichnan method][kraichnan_link], incompressible random
+spatial vector fields can be generated.
+
+
+### Example
+
+```python
+import numpy as np
+import gstools as gs
+x = np.arange(100)
+y = np.arange(100)
+model = gs.Gaussian(dim=2, var=1, len_scale=10)
+srf = gs.SRF(model, generator='VectorField', seed=19841203)
+srf((x, y), mesh_type='structured')
+srf.plot()
+```
+
+yielding
+
+<p align="center">
+<img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/vec_srf_tut_gau.png" alt="vector field" width="600px"/>
+</p>
+
+
+[kraichnan_link]: https://doi.org/10.1063/1.1692799
+
+
+## VTK/PyVista Export
+
+After you have created a field, you may want to save it to file, so we provide
+a handy [VTK][vtk_link] export routine using the `.vtk_export()` or you could
+create a VTK/PyVista dataset for use in Python with to `.to_pyvista()` method:
+
+```python
+import gstools as gs
+x = y = range(100)
+model = gs.Gaussian(dim=2, var=1, len_scale=10)
+srf = gs.SRF(model)
+srf((x, y), mesh_type='structured')
+srf.vtk_export("field") # Saves to a VTK file
+mesh = srf.to_pyvista() # Create a VTK/PyVista dataset in memory
+mesh.plot()
+```
+
+Which gives a RectilinearGrid VTK file ``field.vtr`` or creates a PyVista mesh
+in memory for immediate 3D plotting in Python.
+
+<p align="center">
+<img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/main/docs/source/pics/pyvista_export.png" alt="pyvista export" width="600px"/>
+</p>
+
+
+## Requirements:
+
+- [NumPy >= 1.14.5](https://www.numpy.org)
+- [SciPy >= 1.1.0](https://www.scipy.org/scipylib)
+- [hankel >= 1.0.0](https://github.com/steven-murray/hankel)
+- [emcee >= 3.0.0](https://github.com/dfm/emcee)
+- [pyevtk >= 1.1.1](https://github.com/pyscience-projects/pyevtk)
+- [meshio >= 5.1.0](https://github.com/nschloe/meshio)
+
+### Optional
+
+- [GSTools-Core >= 0.2.0](https://github.com/GeoStat-Framework/GSTools-Core)
+- [matplotlib](https://matplotlib.org)
+- [pyvista](https://docs.pyvista.org/)
+
+
+## Contact
+
+You can contact us via <info@geostat-framework.org>.
+
+
+## License
+
+[LGPLv3][license_link] © 2018-2021
+
+[pip_link]: https://pypi.org/project/gstools
+[conda_link]: https://docs.conda.io/en/latest/miniconda.html
+[conda_forge_link]: https://github.com/conda-forge/gstools-feedstock#installing-gstools
+[conda_pip]: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-pkgs.html#installing-non-conda-packages
+[pipiflag]: https://pip-python3.readthedocs.io/en/latest/reference/pip_install.html?highlight=i#cmdoption-i
+[winpy_link]: https://winpython.github.io/
+[license_link]: https://github.com/GeoStat-Framework/GSTools/blob/main/LICENSE
+[cov_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/generated/gstools.covmodel.CovModel.html#gstools.covmodel.CovModel
+[stable_link]: https://en.wikipedia.org/wiki/Stable_distribution
+[doc_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/
+[doc_install_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/#pip
+[tut_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/tutorials.html
+[tut1_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/01_random_field/index.html
+[tut2_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/02_cov_model/index.html
+[tut3_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/03_variogram/index.html
+[tut4_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/04_vector_field/index.html
+[tut5_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/05_kriging/index.html
+[tut6_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/06_conditioned_fields/index.html
+[tut7_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/07_transformations/index.html
+[tut8_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/08_geo_coordinates/index.html
+[tut9_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/09_spatio_temporal/index.html
+[tut10_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/10_normalizer/index.html
+[tut0_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/00_misc/index.html
+[cor_link]: https://en.wikipedia.org/wiki/Autocovariance#Normalization
+[vtk_link]: https://www.vtk.org/
+
+
+%prep
+%autosetup -n gstools-1.4.1
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-gstools -f filelist.lst
+%dir %{python3_sitearch}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 1.4.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..89a1b5b
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+f614c26f658768eecd247dbb03159b54  gstools-1.4.1.tar.gz