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+/BayesicFitting-3.1.1.tar.gz
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+%global _empty_manifest_terminate_build 0
+Name:		python-BayesicFitting
+Version:	3.1.1
+Release:	1
+Summary:	A Python Toolbox for Bayesian fitting.
+License:	LICENSE.txt
+URL:		https://www.bayesicfitting.nl
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/13/19/8bd57aaee1f69ff6b3ccc0a7eb9d25e1d36eed8756fa3ca4ac533c308425/BayesicFitting-3.1.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-matplotlib
+Requires:	python3-scipy
+Requires:	python3-astropy
+Requires:	python3-future
+
+%description
+# BayesicFitting Readme.
+
+A package for model fitting and Bayesian evidence calculation.
+
+We have a paper out in "Astronomy and Computing" about BayesicFitting.
+[Kester and Mueller (2021)](./docs/references.md/#kester8).
+
+(In case you are wondering what that is about take a 
+quick look at [this example.]
+(https://github.com/dokester/BayesicFitting/blob/master/BayesicFitting/examples/sealevel.ipynb))
+
+Citation index for the BayesicFitting package:
+DOI: 10.5281/zenodo.2597200
+
+
+## What's new.
+ + 18 Jan 2023 version 3.1.1
+   * remode GaussPriorNew from __init__.py
+
+ + 18 Jan 2023 version 3.1.0 (still working on the same update)
+   * Implementing accuracy in fitters and samplers
+   * Update of documentation
+   * More tests
+
+ + 19 Nov 2022 version 3.1.0
+   * Gauss2dErrorDistribution: New Class to handle correlated errors in X and Y
+   * ErrorDistribution and GaussErrorDistribution : adaptation for covariant errors.
+   * Small updates and corrections and removal of unused methods
+   * Added / corrected version information and documentation issues
+   * Rerun all examples and added tests
+
+
+
+## Content
+
+1. [History](#history)
+2. [Setup](#setup)
+3. [Structure](#structure)
+4. [Status](#status)
+5. [Versions](#versions)
+
+<a name="history"></a>
+## 1. History 
+
+The BayesicFitting package is a python version of the the fitter classes
+in Herschel Common Science System (HCSS). HCSS was the all encompassing
+software system for the operations and analysis of the ESA satelite 
+Herschel. The HCSS version was written
+in JAVA mostly by me. I encoded features and classes that were requested
+by my Herschel colleagues or that I remembered having used myself during
+my lifelong career as data analyst for earlier satellites as IRAS, ISO
+and AKARI. So most of the stuff in here was needed and used at a certain
+moment in time. Even now the package is developing in directions that
+are needed by my work for the James Webb Space Telescope (JWST). 
+
+The HCSS system is in the public domain under GPL3. It was used by the 3
+instrument groups of the Herschel satellite to write calibration and
+analysis software. Since the end of the mission HCSS is not being
+maintained
+
+I used a customized version of java2python (j2py on github) to translate
+the JAVA classes to python. However, the actual code needed serious
+pythonization. Every line has been inspected. Every construct has been 
+revised.
+
+The documentation got most profit from the automated conversion. Also
+the structure into classes, the inheritance, methods and dependencies
+are largely the same as in the original HCSS.
+
+<a name="setup"> </a>
+## 2. Setup 
+
+The package is written in python3 although I am not aware of using any
+specific python3 features. It uses numpy (>= 1.9) for its array
+structure, scipy (>=1.0) for linear algebra and other stuff and astropy
+(>=2.0) for units. Matplotlib (>=2.0) is used for plotting.
+
+Download and unpack the BayesicFitting zip file from github. Move into 
+the BayesicFitting-master directory and run:
+
+    python setup.py install
+
+where python is python3. Or install it as :
+
+    pip install BayesicFitting
+
+<a name="structure"> </a>
+## 3. Structure 
+
+### source
+
+The BayesicFitting package consists of over 100 classes, each class in
+its own file. These classes can be divided into 3 broad categories:
+models, fitters and nested sampling. About 50 models, 10 fitters and the
+remainder is needed to run the nested sampling algorithm. All these
+classes are in a directory BayesicFitting/source. A special type of
+functions are found in BayesicFitting/source/kernels. They can be used
+to construct a model.
+
+
+### examples
+
+In [BayesicFitting/examples]
+(https://github.com/dokester/BayesicFitting/blob/master/BayesicFitting/examples)
+a number of scripts can be
+found to exercise the classes. They are in the form of jupyter
+notebooks. Some are using real data; others have synthetic data
+specially constructed to make some point. 
+
+All examples can be inspected by clicking on them. They will fold out in
+the browser.
+
+To actually exercise the examples and maybe adapt then, start a jupyter
+notebook in your examples directory.
+
+    jupyter notebook
+
+The program will open a list in your webbrowser where you can select a
+notebook file (.ipynb), which can be run.
+
+
+### documentation
+
+In the [documenation]
+(https://github.com/dokester/BayesicFitting/blob/master/docs)
+directory a number of documents can be found. 
+
+
++ [Manual](https://github.com/dokester/BayesicFitting/blob/master/docs/manual.md)<br>
+A first draft of a manual. It obviously needs more work.
+
++ [Troubles](https://github.com/dokester/BayesicFitting/blob/master/docs/troubles.md)<br>
+A list of troublesome situations and what to do about it.
+
++ [Glossary](https://github.com/dokester/BayesicFitting/blob/master/docs/glossary.md)<br>
+A list of the terms used throughout this package, with explanations.
+
++ [Design](https://github.com/dokester/BayesicFitting/blob/master/docs/design.md)<br>
+An architectural design document, displaying the relationships between 
+the classes. 
+
++ [Style](https://github.com/dokester/BayesicFitting/blob/master/docs/code-style.md)<br>
+A few notes on my style of code and documenation.
+
++ [References](https://github.com/dokester/BayesicFitting/blob/master/docs/references.md)<br>
+A list of external references for BayesicFitting.
+
+### test
+
+Almost all classes have a test harness. These are located in
+BayesicFitting/test. They can be execised as:
+
+    python -m unittest <file>
+
+where python refers to python3 and file refers to one of the files in
+BayesicFitting/test.<br>
+As most functionality is tested in a test harness, examples on how to
+use the classes can be found there too.
+
+<a name="status"> </a> 
+## 4. Status 
+
+A package like this is never finished. Always more classes and/or
+functionalities can be added. I present it now as it is in the hope it
+will be usefull and it will generate feedback.
+
+According to Wikipedia -> "Software release life cycle" it is called 
+"Perpetual Beta". It continues to be in a beta-release because new 
+classes and features can be added.
+
+More work needs to be done in:
+
+  * Introduction of more Classes: NeuralNetModel, ...
+
+<a name="versions"></a>
+## 5. Versions
+
+ +  4 Jan 2018 version 0.9.0.<br>
+   * Initial upload to github.
+
+ + 26 Jan 2018 version <br>
+
+ +  5 Mar 2018 version 1.0.1 <br>
+   * Package on pypi.com. 
+   * Restructured all import statement to comply with PYPI package.
+
+ + 14 Mar 2018 version 1.0.2 <br>
+   * Added Dynamic Models 
+   * Added piping of models
+
+ + 23 Mar 2018 version 1.0.3 <br>
+   * Some issues with ErrorDistributions and map fitting
+   * 2-d fitting examples added
+   * All examples revisited
+   * Links in README.md updated
+
+ + 28 May 2018 version 1.0.4 <br>
+   * New classes: CircularUniformPrior, PseudoVoigtModel
+   * VoigtModel uses scipy.special.wozf() and has partials now,
+   * Refactoring Priors to the BaseModel
+   * Restructuring Dynamic
+   * Threading optional in NestedSampler.
+   * New classes: UniformErrorDistribution, FreeShapeModel and kernels/Tophat
+   * added to testharnesses and examples
+
+ + 27 June 2018 version 1.0.5<br>
+   * New classes: RadialVelocityModel and MixedErrorDistribution
+   * testharnesses and examples
+   * documentation updates
+
+ + 28 June 2018 version 1.0.6
+   * longdescription set to markdown (Still not OK on pypi.org)
+
+ + 28 July 2018 version 1.0.7
+   * small compilation error in 1.0.6
+
+ + 11 October 2018 version 1.0.8
+   * refactoring the setting of attributes in Models
+   * documentation (manual, design, etc.) updated.
+
+ + 28 December 2018 version 2.0.0
+   * Introduction of Problem Classes: 
+     - Problem. <br>
+	Base class for problems to be handled by NestedSampler.
+     - ClassicProblem. <br>
+	Common class for everything that was possible in version 1.
+	ClassicProblem is transparant as all interfaces to NestedSampler have remained 
+	the same as they were in version 1.0, even though behind the scenes a 
+	ClassicProblem has been invoked.
+     - ErrorsInXandYProblem. <br>
+	Problem that have errors in the xdata  and in the ydata.
+     - ... more to come.
+   * Introduction of Walker and WalkerList to represent the internal ensemble
+     in NestedSampler. 
+   * Adaptations in NestedSampler, ErrorDistributions, Engines, Sample, SampleList.
+   * Better separation of responsibilities of ErrorDistribution and Problem. <br>
+     Consequently ErrorDistribution has a new initialisation, which is incompatible 
+     with previous versions. In most cases this has no effect on the calling 
+     sequences of NestedSampler.
+   * Rename GenGaussErrorDistribution into ExponentialErrorDistribution.
+   * New testharnesses and examples.
+   * Adaptations of documentation: manual and design.
+
+ + 16 Jan 2019 version 2.1.0
+   * MultipleOutputProblem.
+     Problems with more dimensional outputs 
+   * StellarOrbitModel. 
+     A 2 dim output model to calculate the orbit of a double star
+   * Keppler2ndLaw.
+     To calculate the radius and true anomaly according to Kepplers 2nd law. 
+     (and derivatives)
+   * RadialVelocityModel: adapted to Kepplers2ndLaw. A slight change in the 
+     order of the parameters.
+   * NestedSampler: some improvements in output layout.
+   * New tests, examples and updates for documentation.
+
+ + 7 Feb 2019 version 2.2.0
+   * ChordEngine. Implementation of the POLYCHORD engine, developed 
+     by Handley etal. (2015) MNRAS 
+   * OrthogonalBasis. Helper class fot ChordEngine.
+   * Tests and examples
+
+ + 19 Feb 2019 version 2.2.1 
+   * AmoebaFitter still mentioned GenGaussErrorDistribution; replaced 
+     by ExponentialErrorDistribution
+   * Some documentation issues repaired.
+
+ + 20 Jun 2019 version 2.3.0 
+   * Add LogisticModel and SampleMovie
+   * Periodic residuals in Problem
+   * Small issues repaired
+   * Rerun all examples
+   * Pictures moved to documentation/images
+   * Some documentation issues repaired.
+
+ + 14 Nov 2019 version 2.4.0 to 2.4.2
+   * New Classes:
+      - DecisionTreeModel
+	A DecisionTree Model (DTM) is mostly defined on multiple input dimensions (axes).
+    	It splits the data in 2 parts, according low and high values on a certain input axis.
+    	The splitting can continue along other axes.
+      - Modifiable
+	Interface to define modifiable behaviour of some Models.
+      - StructureEngine
+	Engine to modify Models that implement Modifiable
+   * Introduce Table from astrolib as (multidimensional) xdata
+   * Some restructering necessitated by the classes above.
+   * Testcases and examples for the classes above
+
+ + 3 Feb 2020 version 2.4.3
+   * Clean up and unification of the python doc strings.
+   * Reran all examples and test harnasses in python 3.7.
+   * Add random seed to several examples to make them more stable.
+
+ + 17 Mar 2020 version 2.4.4
+   * Moved BayesicFitting/BayesicFitting/documentation to BayesicFitting/docs
+   * Added a references.md file which collects (external) references.
+   * Updated the docs files.
+   * Handling of weight in accordance with the definition in the Glossary.
+   * Add keyword tail= to formatter to display last items of an array.
+
+ + 4 Jun 2020 version 2.5.0
+   * Add new models: BasicSplinesModel and SplinesDynamicModel
+   * Option for constraints on the likelihood
+   * Option for slow engines (working every slow-th iteration in NestedSampler)
+   * Restructure growPrior setting
+   * Print formatting in NestedSampler
+   * Adapt to SplinesDynamicModel
+   * Homogenized and improved plotoptions in test harnesses
+   * Three more examples added
+
+ + 5 Jun 2020 versions 2.5.1
+   * Comment out NeuralNetModel (not yet available) and some typos.
+
+ + 6 Jun 2020 versions 2.5.2
+   * Two more bugs smashed (in StartEngine and Prior)
+
+ + 29 Jun 2020 versions 2.5.3
+   * Averaging of circular variables
+   * Update of static class attributes
+   * Attribute and printing issues.
+
+ + 23 Oct 2020 version 2.6.0
+   * New class: PhantomSampler; adaptations in Engines, Explorer, WalkerList
+   * Restructured NestedSampler to accommodate PhantomSampler
+   * Test harnass for PhantomSampler
+   * Option: fix parameters in BasicSplinesModel
+   * Confusing __str__ method in compound models improved
+
+ + 6 Nov 2020 versions 2.6.1
+   * Avoid infinities in unbound Priors
+   * mcycles in initialization of MonteCarlo
+   * convert xdata, ydata, weights using numpy.asarray
+
+ + 11 Dec 2020 versions 2.6.2
+   * Add limits and circular to Priors
+   * Finetune Engines
+
+ + 18 Feb 2021 version 2.7.0
+   * New class: EvidenceProblem & ModelDistribution; adaptations in NestedSampler and tests.
+   * Change in constrain method definition
+   * decay in ExpModel
+   * some seldom errors, clean-up & new test harnasses. 
+
+ + 19 April 2021 version 2.7.1
+   * remove CrossEngine completely
+
+ + 20 April 2021 version 2.7.2
+   * put some tests on hold
+   * few minor issues/errors
+
+ + 29 Oct 2021 version 2.8.0
+   * New class: BernoulliErrorDistribution and SoftMaxModel, tests, examples and data
+   * Adaptations To BernoulliED in some other classes
+   * Multi-dim input and output issues
+   * Updated some other tests and examples
+   * Documentation and other small issues.
+
+ + 25 Nov 2021 version 2.8.1
+   * Cleanup in Plotter
+   * Documentation issues; Replaced style.md by code-style.md
+   * Correcting error on Windows systme
+
+ + 05 Apr 2022 version 3.0.1
+   * Addressing issue #18: UserModel does not work for multiple dimensions.
+
+ + 07 Feb 2022 version 3.0.0
+   * New classes: AstropyModel and UserModel
+   * New class: NeuralNetUtilities
+   * New classes: NestedSolver, OrderProblem, SalesmanProblem, DistanceCostFunction
+   * New classes: OrderEngine, MoveEngine, SwitchEngine, LoopEngine, ShuffleEngine
+   * New classes: ReverseEngine, NearEngine, StartOrderEngine
+   * Make pipe work for more dimensional output | input
+   * Test harnesses for the new classes
+   * New examples for AstropyModel, UserModel and SalesmanProblem
+   * Update existing examples to improve coverage of pytest
+   * update Manual
+
+ + 19 Nov 2022 version 3.1.0
+   * See above in Whats new
+
+<br><br><br><br>
+
+
+
+
+
+%package -n python3-BayesicFitting
+Summary:	A Python Toolbox for Bayesian fitting.
+Provides:	python-BayesicFitting
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-BayesicFitting
+# BayesicFitting Readme.
+
+A package for model fitting and Bayesian evidence calculation.
+
+We have a paper out in "Astronomy and Computing" about BayesicFitting.
+[Kester and Mueller (2021)](./docs/references.md/#kester8).
+
+(In case you are wondering what that is about take a 
+quick look at [this example.]
+(https://github.com/dokester/BayesicFitting/blob/master/BayesicFitting/examples/sealevel.ipynb))
+
+Citation index for the BayesicFitting package:
+DOI: 10.5281/zenodo.2597200
+
+
+## What's new.
+ + 18 Jan 2023 version 3.1.1
+   * remode GaussPriorNew from __init__.py
+
+ + 18 Jan 2023 version 3.1.0 (still working on the same update)
+   * Implementing accuracy in fitters and samplers
+   * Update of documentation
+   * More tests
+
+ + 19 Nov 2022 version 3.1.0
+   * Gauss2dErrorDistribution: New Class to handle correlated errors in X and Y
+   * ErrorDistribution and GaussErrorDistribution : adaptation for covariant errors.
+   * Small updates and corrections and removal of unused methods
+   * Added / corrected version information and documentation issues
+   * Rerun all examples and added tests
+
+
+
+## Content
+
+1. [History](#history)
+2. [Setup](#setup)
+3. [Structure](#structure)
+4. [Status](#status)
+5. [Versions](#versions)
+
+<a name="history"></a>
+## 1. History 
+
+The BayesicFitting package is a python version of the the fitter classes
+in Herschel Common Science System (HCSS). HCSS was the all encompassing
+software system for the operations and analysis of the ESA satelite 
+Herschel. The HCSS version was written
+in JAVA mostly by me. I encoded features and classes that were requested
+by my Herschel colleagues or that I remembered having used myself during
+my lifelong career as data analyst for earlier satellites as IRAS, ISO
+and AKARI. So most of the stuff in here was needed and used at a certain
+moment in time. Even now the package is developing in directions that
+are needed by my work for the James Webb Space Telescope (JWST). 
+
+The HCSS system is in the public domain under GPL3. It was used by the 3
+instrument groups of the Herschel satellite to write calibration and
+analysis software. Since the end of the mission HCSS is not being
+maintained
+
+I used a customized version of java2python (j2py on github) to translate
+the JAVA classes to python. However, the actual code needed serious
+pythonization. Every line has been inspected. Every construct has been 
+revised.
+
+The documentation got most profit from the automated conversion. Also
+the structure into classes, the inheritance, methods and dependencies
+are largely the same as in the original HCSS.
+
+<a name="setup"> </a>
+## 2. Setup 
+
+The package is written in python3 although I am not aware of using any
+specific python3 features. It uses numpy (>= 1.9) for its array
+structure, scipy (>=1.0) for linear algebra and other stuff and astropy
+(>=2.0) for units. Matplotlib (>=2.0) is used for plotting.
+
+Download and unpack the BayesicFitting zip file from github. Move into 
+the BayesicFitting-master directory and run:
+
+    python setup.py install
+
+where python is python3. Or install it as :
+
+    pip install BayesicFitting
+
+<a name="structure"> </a>
+## 3. Structure 
+
+### source
+
+The BayesicFitting package consists of over 100 classes, each class in
+its own file. These classes can be divided into 3 broad categories:
+models, fitters and nested sampling. About 50 models, 10 fitters and the
+remainder is needed to run the nested sampling algorithm. All these
+classes are in a directory BayesicFitting/source. A special type of
+functions are found in BayesicFitting/source/kernels. They can be used
+to construct a model.
+
+
+### examples
+
+In [BayesicFitting/examples]
+(https://github.com/dokester/BayesicFitting/blob/master/BayesicFitting/examples)
+a number of scripts can be
+found to exercise the classes. They are in the form of jupyter
+notebooks. Some are using real data; others have synthetic data
+specially constructed to make some point. 
+
+All examples can be inspected by clicking on them. They will fold out in
+the browser.
+
+To actually exercise the examples and maybe adapt then, start a jupyter
+notebook in your examples directory.
+
+    jupyter notebook
+
+The program will open a list in your webbrowser where you can select a
+notebook file (.ipynb), which can be run.
+
+
+### documentation
+
+In the [documenation]
+(https://github.com/dokester/BayesicFitting/blob/master/docs)
+directory a number of documents can be found. 
+
+
++ [Manual](https://github.com/dokester/BayesicFitting/blob/master/docs/manual.md)<br>
+A first draft of a manual. It obviously needs more work.
+
++ [Troubles](https://github.com/dokester/BayesicFitting/blob/master/docs/troubles.md)<br>
+A list of troublesome situations and what to do about it.
+
++ [Glossary](https://github.com/dokester/BayesicFitting/blob/master/docs/glossary.md)<br>
+A list of the terms used throughout this package, with explanations.
+
++ [Design](https://github.com/dokester/BayesicFitting/blob/master/docs/design.md)<br>
+An architectural design document, displaying the relationships between 
+the classes. 
+
++ [Style](https://github.com/dokester/BayesicFitting/blob/master/docs/code-style.md)<br>
+A few notes on my style of code and documenation.
+
++ [References](https://github.com/dokester/BayesicFitting/blob/master/docs/references.md)<br>
+A list of external references for BayesicFitting.
+
+### test
+
+Almost all classes have a test harness. These are located in
+BayesicFitting/test. They can be execised as:
+
+    python -m unittest <file>
+
+where python refers to python3 and file refers to one of the files in
+BayesicFitting/test.<br>
+As most functionality is tested in a test harness, examples on how to
+use the classes can be found there too.
+
+<a name="status"> </a> 
+## 4. Status 
+
+A package like this is never finished. Always more classes and/or
+functionalities can be added. I present it now as it is in the hope it
+will be usefull and it will generate feedback.
+
+According to Wikipedia -> "Software release life cycle" it is called 
+"Perpetual Beta". It continues to be in a beta-release because new 
+classes and features can be added.
+
+More work needs to be done in:
+
+  * Introduction of more Classes: NeuralNetModel, ...
+
+<a name="versions"></a>
+## 5. Versions
+
+ +  4 Jan 2018 version 0.9.0.<br>
+   * Initial upload to github.
+
+ + 26 Jan 2018 version <br>
+
+ +  5 Mar 2018 version 1.0.1 <br>
+   * Package on pypi.com. 
+   * Restructured all import statement to comply with PYPI package.
+
+ + 14 Mar 2018 version 1.0.2 <br>
+   * Added Dynamic Models 
+   * Added piping of models
+
+ + 23 Mar 2018 version 1.0.3 <br>
+   * Some issues with ErrorDistributions and map fitting
+   * 2-d fitting examples added
+   * All examples revisited
+   * Links in README.md updated
+
+ + 28 May 2018 version 1.0.4 <br>
+   * New classes: CircularUniformPrior, PseudoVoigtModel
+   * VoigtModel uses scipy.special.wozf() and has partials now,
+   * Refactoring Priors to the BaseModel
+   * Restructuring Dynamic
+   * Threading optional in NestedSampler.
+   * New classes: UniformErrorDistribution, FreeShapeModel and kernels/Tophat
+   * added to testharnesses and examples
+
+ + 27 June 2018 version 1.0.5<br>
+   * New classes: RadialVelocityModel and MixedErrorDistribution
+   * testharnesses and examples
+   * documentation updates
+
+ + 28 June 2018 version 1.0.6
+   * longdescription set to markdown (Still not OK on pypi.org)
+
+ + 28 July 2018 version 1.0.7
+   * small compilation error in 1.0.6
+
+ + 11 October 2018 version 1.0.8
+   * refactoring the setting of attributes in Models
+   * documentation (manual, design, etc.) updated.
+
+ + 28 December 2018 version 2.0.0
+   * Introduction of Problem Classes: 
+     - Problem. <br>
+	Base class for problems to be handled by NestedSampler.
+     - ClassicProblem. <br>
+	Common class for everything that was possible in version 1.
+	ClassicProblem is transparant as all interfaces to NestedSampler have remained 
+	the same as they were in version 1.0, even though behind the scenes a 
+	ClassicProblem has been invoked.
+     - ErrorsInXandYProblem. <br>
+	Problem that have errors in the xdata  and in the ydata.
+     - ... more to come.
+   * Introduction of Walker and WalkerList to represent the internal ensemble
+     in NestedSampler. 
+   * Adaptations in NestedSampler, ErrorDistributions, Engines, Sample, SampleList.
+   * Better separation of responsibilities of ErrorDistribution and Problem. <br>
+     Consequently ErrorDistribution has a new initialisation, which is incompatible 
+     with previous versions. In most cases this has no effect on the calling 
+     sequences of NestedSampler.
+   * Rename GenGaussErrorDistribution into ExponentialErrorDistribution.
+   * New testharnesses and examples.
+   * Adaptations of documentation: manual and design.
+
+ + 16 Jan 2019 version 2.1.0
+   * MultipleOutputProblem.
+     Problems with more dimensional outputs 
+   * StellarOrbitModel. 
+     A 2 dim output model to calculate the orbit of a double star
+   * Keppler2ndLaw.
+     To calculate the radius and true anomaly according to Kepplers 2nd law. 
+     (and derivatives)
+   * RadialVelocityModel: adapted to Kepplers2ndLaw. A slight change in the 
+     order of the parameters.
+   * NestedSampler: some improvements in output layout.
+   * New tests, examples and updates for documentation.
+
+ + 7 Feb 2019 version 2.2.0
+   * ChordEngine. Implementation of the POLYCHORD engine, developed 
+     by Handley etal. (2015) MNRAS 
+   * OrthogonalBasis. Helper class fot ChordEngine.
+   * Tests and examples
+
+ + 19 Feb 2019 version 2.2.1 
+   * AmoebaFitter still mentioned GenGaussErrorDistribution; replaced 
+     by ExponentialErrorDistribution
+   * Some documentation issues repaired.
+
+ + 20 Jun 2019 version 2.3.0 
+   * Add LogisticModel and SampleMovie
+   * Periodic residuals in Problem
+   * Small issues repaired
+   * Rerun all examples
+   * Pictures moved to documentation/images
+   * Some documentation issues repaired.
+
+ + 14 Nov 2019 version 2.4.0 to 2.4.2
+   * New Classes:
+      - DecisionTreeModel
+	A DecisionTree Model (DTM) is mostly defined on multiple input dimensions (axes).
+    	It splits the data in 2 parts, according low and high values on a certain input axis.
+    	The splitting can continue along other axes.
+      - Modifiable
+	Interface to define modifiable behaviour of some Models.
+      - StructureEngine
+	Engine to modify Models that implement Modifiable
+   * Introduce Table from astrolib as (multidimensional) xdata
+   * Some restructering necessitated by the classes above.
+   * Testcases and examples for the classes above
+
+ + 3 Feb 2020 version 2.4.3
+   * Clean up and unification of the python doc strings.
+   * Reran all examples and test harnasses in python 3.7.
+   * Add random seed to several examples to make them more stable.
+
+ + 17 Mar 2020 version 2.4.4
+   * Moved BayesicFitting/BayesicFitting/documentation to BayesicFitting/docs
+   * Added a references.md file which collects (external) references.
+   * Updated the docs files.
+   * Handling of weight in accordance with the definition in the Glossary.
+   * Add keyword tail= to formatter to display last items of an array.
+
+ + 4 Jun 2020 version 2.5.0
+   * Add new models: BasicSplinesModel and SplinesDynamicModel
+   * Option for constraints on the likelihood
+   * Option for slow engines (working every slow-th iteration in NestedSampler)
+   * Restructure growPrior setting
+   * Print formatting in NestedSampler
+   * Adapt to SplinesDynamicModel
+   * Homogenized and improved plotoptions in test harnesses
+   * Three more examples added
+
+ + 5 Jun 2020 versions 2.5.1
+   * Comment out NeuralNetModel (not yet available) and some typos.
+
+ + 6 Jun 2020 versions 2.5.2
+   * Two more bugs smashed (in StartEngine and Prior)
+
+ + 29 Jun 2020 versions 2.5.3
+   * Averaging of circular variables
+   * Update of static class attributes
+   * Attribute and printing issues.
+
+ + 23 Oct 2020 version 2.6.0
+   * New class: PhantomSampler; adaptations in Engines, Explorer, WalkerList
+   * Restructured NestedSampler to accommodate PhantomSampler
+   * Test harnass for PhantomSampler
+   * Option: fix parameters in BasicSplinesModel
+   * Confusing __str__ method in compound models improved
+
+ + 6 Nov 2020 versions 2.6.1
+   * Avoid infinities in unbound Priors
+   * mcycles in initialization of MonteCarlo
+   * convert xdata, ydata, weights using numpy.asarray
+
+ + 11 Dec 2020 versions 2.6.2
+   * Add limits and circular to Priors
+   * Finetune Engines
+
+ + 18 Feb 2021 version 2.7.0
+   * New class: EvidenceProblem & ModelDistribution; adaptations in NestedSampler and tests.
+   * Change in constrain method definition
+   * decay in ExpModel
+   * some seldom errors, clean-up & new test harnasses. 
+
+ + 19 April 2021 version 2.7.1
+   * remove CrossEngine completely
+
+ + 20 April 2021 version 2.7.2
+   * put some tests on hold
+   * few minor issues/errors
+
+ + 29 Oct 2021 version 2.8.0
+   * New class: BernoulliErrorDistribution and SoftMaxModel, tests, examples and data
+   * Adaptations To BernoulliED in some other classes
+   * Multi-dim input and output issues
+   * Updated some other tests and examples
+   * Documentation and other small issues.
+
+ + 25 Nov 2021 version 2.8.1
+   * Cleanup in Plotter
+   * Documentation issues; Replaced style.md by code-style.md
+   * Correcting error on Windows systme
+
+ + 05 Apr 2022 version 3.0.1
+   * Addressing issue #18: UserModel does not work for multiple dimensions.
+
+ + 07 Feb 2022 version 3.0.0
+   * New classes: AstropyModel and UserModel
+   * New class: NeuralNetUtilities
+   * New classes: NestedSolver, OrderProblem, SalesmanProblem, DistanceCostFunction
+   * New classes: OrderEngine, MoveEngine, SwitchEngine, LoopEngine, ShuffleEngine
+   * New classes: ReverseEngine, NearEngine, StartOrderEngine
+   * Make pipe work for more dimensional output | input
+   * Test harnesses for the new classes
+   * New examples for AstropyModel, UserModel and SalesmanProblem
+   * Update existing examples to improve coverage of pytest
+   * update Manual
+
+ + 19 Nov 2022 version 3.1.0
+   * See above in Whats new
+
+<br><br><br><br>
+
+
+
+
+
+%package help
+Summary:	Development documents and examples for BayesicFitting
+Provides:	python3-BayesicFitting-doc
+%description help
+# BayesicFitting Readme.
+
+A package for model fitting and Bayesian evidence calculation.
+
+We have a paper out in "Astronomy and Computing" about BayesicFitting.
+[Kester and Mueller (2021)](./docs/references.md/#kester8).
+
+(In case you are wondering what that is about take a 
+quick look at [this example.]
+(https://github.com/dokester/BayesicFitting/blob/master/BayesicFitting/examples/sealevel.ipynb))
+
+Citation index for the BayesicFitting package:
+DOI: 10.5281/zenodo.2597200
+
+
+## What's new.
+ + 18 Jan 2023 version 3.1.1
+   * remode GaussPriorNew from __init__.py
+
+ + 18 Jan 2023 version 3.1.0 (still working on the same update)
+   * Implementing accuracy in fitters and samplers
+   * Update of documentation
+   * More tests
+
+ + 19 Nov 2022 version 3.1.0
+   * Gauss2dErrorDistribution: New Class to handle correlated errors in X and Y
+   * ErrorDistribution and GaussErrorDistribution : adaptation for covariant errors.
+   * Small updates and corrections and removal of unused methods
+   * Added / corrected version information and documentation issues
+   * Rerun all examples and added tests
+
+
+
+## Content
+
+1. [History](#history)
+2. [Setup](#setup)
+3. [Structure](#structure)
+4. [Status](#status)
+5. [Versions](#versions)
+
+<a name="history"></a>
+## 1. History 
+
+The BayesicFitting package is a python version of the the fitter classes
+in Herschel Common Science System (HCSS). HCSS was the all encompassing
+software system for the operations and analysis of the ESA satelite 
+Herschel. The HCSS version was written
+in JAVA mostly by me. I encoded features and classes that were requested
+by my Herschel colleagues or that I remembered having used myself during
+my lifelong career as data analyst for earlier satellites as IRAS, ISO
+and AKARI. So most of the stuff in here was needed and used at a certain
+moment in time. Even now the package is developing in directions that
+are needed by my work for the James Webb Space Telescope (JWST). 
+
+The HCSS system is in the public domain under GPL3. It was used by the 3
+instrument groups of the Herschel satellite to write calibration and
+analysis software. Since the end of the mission HCSS is not being
+maintained
+
+I used a customized version of java2python (j2py on github) to translate
+the JAVA classes to python. However, the actual code needed serious
+pythonization. Every line has been inspected. Every construct has been 
+revised.
+
+The documentation got most profit from the automated conversion. Also
+the structure into classes, the inheritance, methods and dependencies
+are largely the same as in the original HCSS.
+
+<a name="setup"> </a>
+## 2. Setup 
+
+The package is written in python3 although I am not aware of using any
+specific python3 features. It uses numpy (>= 1.9) for its array
+structure, scipy (>=1.0) for linear algebra and other stuff and astropy
+(>=2.0) for units. Matplotlib (>=2.0) is used for plotting.
+
+Download and unpack the BayesicFitting zip file from github. Move into 
+the BayesicFitting-master directory and run:
+
+    python setup.py install
+
+where python is python3. Or install it as :
+
+    pip install BayesicFitting
+
+<a name="structure"> </a>
+## 3. Structure 
+
+### source
+
+The BayesicFitting package consists of over 100 classes, each class in
+its own file. These classes can be divided into 3 broad categories:
+models, fitters and nested sampling. About 50 models, 10 fitters and the
+remainder is needed to run the nested sampling algorithm. All these
+classes are in a directory BayesicFitting/source. A special type of
+functions are found in BayesicFitting/source/kernels. They can be used
+to construct a model.
+
+
+### examples
+
+In [BayesicFitting/examples]
+(https://github.com/dokester/BayesicFitting/blob/master/BayesicFitting/examples)
+a number of scripts can be
+found to exercise the classes. They are in the form of jupyter
+notebooks. Some are using real data; others have synthetic data
+specially constructed to make some point. 
+
+All examples can be inspected by clicking on them. They will fold out in
+the browser.
+
+To actually exercise the examples and maybe adapt then, start a jupyter
+notebook in your examples directory.
+
+    jupyter notebook
+
+The program will open a list in your webbrowser where you can select a
+notebook file (.ipynb), which can be run.
+
+
+### documentation
+
+In the [documenation]
+(https://github.com/dokester/BayesicFitting/blob/master/docs)
+directory a number of documents can be found. 
+
+
++ [Manual](https://github.com/dokester/BayesicFitting/blob/master/docs/manual.md)<br>
+A first draft of a manual. It obviously needs more work.
+
++ [Troubles](https://github.com/dokester/BayesicFitting/blob/master/docs/troubles.md)<br>
+A list of troublesome situations and what to do about it.
+
++ [Glossary](https://github.com/dokester/BayesicFitting/blob/master/docs/glossary.md)<br>
+A list of the terms used throughout this package, with explanations.
+
++ [Design](https://github.com/dokester/BayesicFitting/blob/master/docs/design.md)<br>
+An architectural design document, displaying the relationships between 
+the classes. 
+
++ [Style](https://github.com/dokester/BayesicFitting/blob/master/docs/code-style.md)<br>
+A few notes on my style of code and documenation.
+
++ [References](https://github.com/dokester/BayesicFitting/blob/master/docs/references.md)<br>
+A list of external references for BayesicFitting.
+
+### test
+
+Almost all classes have a test harness. These are located in
+BayesicFitting/test. They can be execised as:
+
+    python -m unittest <file>
+
+where python refers to python3 and file refers to one of the files in
+BayesicFitting/test.<br>
+As most functionality is tested in a test harness, examples on how to
+use the classes can be found there too.
+
+<a name="status"> </a> 
+## 4. Status 
+
+A package like this is never finished. Always more classes and/or
+functionalities can be added. I present it now as it is in the hope it
+will be usefull and it will generate feedback.
+
+According to Wikipedia -> "Software release life cycle" it is called 
+"Perpetual Beta". It continues to be in a beta-release because new 
+classes and features can be added.
+
+More work needs to be done in:
+
+  * Introduction of more Classes: NeuralNetModel, ...
+
+<a name="versions"></a>
+## 5. Versions
+
+ +  4 Jan 2018 version 0.9.0.<br>
+   * Initial upload to github.
+
+ + 26 Jan 2018 version <br>
+
+ +  5 Mar 2018 version 1.0.1 <br>
+   * Package on pypi.com. 
+   * Restructured all import statement to comply with PYPI package.
+
+ + 14 Mar 2018 version 1.0.2 <br>
+   * Added Dynamic Models 
+   * Added piping of models
+
+ + 23 Mar 2018 version 1.0.3 <br>
+   * Some issues with ErrorDistributions and map fitting
+   * 2-d fitting examples added
+   * All examples revisited
+   * Links in README.md updated
+
+ + 28 May 2018 version 1.0.4 <br>
+   * New classes: CircularUniformPrior, PseudoVoigtModel
+   * VoigtModel uses scipy.special.wozf() and has partials now,
+   * Refactoring Priors to the BaseModel
+   * Restructuring Dynamic
+   * Threading optional in NestedSampler.
+   * New classes: UniformErrorDistribution, FreeShapeModel and kernels/Tophat
+   * added to testharnesses and examples
+
+ + 27 June 2018 version 1.0.5<br>
+   * New classes: RadialVelocityModel and MixedErrorDistribution
+   * testharnesses and examples
+   * documentation updates
+
+ + 28 June 2018 version 1.0.6
+   * longdescription set to markdown (Still not OK on pypi.org)
+
+ + 28 July 2018 version 1.0.7
+   * small compilation error in 1.0.6
+
+ + 11 October 2018 version 1.0.8
+   * refactoring the setting of attributes in Models
+   * documentation (manual, design, etc.) updated.
+
+ + 28 December 2018 version 2.0.0
+   * Introduction of Problem Classes: 
+     - Problem. <br>
+	Base class for problems to be handled by NestedSampler.
+     - ClassicProblem. <br>
+	Common class for everything that was possible in version 1.
+	ClassicProblem is transparant as all interfaces to NestedSampler have remained 
+	the same as they were in version 1.0, even though behind the scenes a 
+	ClassicProblem has been invoked.
+     - ErrorsInXandYProblem. <br>
+	Problem that have errors in the xdata  and in the ydata.
+     - ... more to come.
+   * Introduction of Walker and WalkerList to represent the internal ensemble
+     in NestedSampler. 
+   * Adaptations in NestedSampler, ErrorDistributions, Engines, Sample, SampleList.
+   * Better separation of responsibilities of ErrorDistribution and Problem. <br>
+     Consequently ErrorDistribution has a new initialisation, which is incompatible 
+     with previous versions. In most cases this has no effect on the calling 
+     sequences of NestedSampler.
+   * Rename GenGaussErrorDistribution into ExponentialErrorDistribution.
+   * New testharnesses and examples.
+   * Adaptations of documentation: manual and design.
+
+ + 16 Jan 2019 version 2.1.0
+   * MultipleOutputProblem.
+     Problems with more dimensional outputs 
+   * StellarOrbitModel. 
+     A 2 dim output model to calculate the orbit of a double star
+   * Keppler2ndLaw.
+     To calculate the radius and true anomaly according to Kepplers 2nd law. 
+     (and derivatives)
+   * RadialVelocityModel: adapted to Kepplers2ndLaw. A slight change in the 
+     order of the parameters.
+   * NestedSampler: some improvements in output layout.
+   * New tests, examples and updates for documentation.
+
+ + 7 Feb 2019 version 2.2.0
+   * ChordEngine. Implementation of the POLYCHORD engine, developed 
+     by Handley etal. (2015) MNRAS 
+   * OrthogonalBasis. Helper class fot ChordEngine.
+   * Tests and examples
+
+ + 19 Feb 2019 version 2.2.1 
+   * AmoebaFitter still mentioned GenGaussErrorDistribution; replaced 
+     by ExponentialErrorDistribution
+   * Some documentation issues repaired.
+
+ + 20 Jun 2019 version 2.3.0 
+   * Add LogisticModel and SampleMovie
+   * Periodic residuals in Problem
+   * Small issues repaired
+   * Rerun all examples
+   * Pictures moved to documentation/images
+   * Some documentation issues repaired.
+
+ + 14 Nov 2019 version 2.4.0 to 2.4.2
+   * New Classes:
+      - DecisionTreeModel
+	A DecisionTree Model (DTM) is mostly defined on multiple input dimensions (axes).
+    	It splits the data in 2 parts, according low and high values on a certain input axis.
+    	The splitting can continue along other axes.
+      - Modifiable
+	Interface to define modifiable behaviour of some Models.
+      - StructureEngine
+	Engine to modify Models that implement Modifiable
+   * Introduce Table from astrolib as (multidimensional) xdata
+   * Some restructering necessitated by the classes above.
+   * Testcases and examples for the classes above
+
+ + 3 Feb 2020 version 2.4.3
+   * Clean up and unification of the python doc strings.
+   * Reran all examples and test harnasses in python 3.7.
+   * Add random seed to several examples to make them more stable.
+
+ + 17 Mar 2020 version 2.4.4
+   * Moved BayesicFitting/BayesicFitting/documentation to BayesicFitting/docs
+   * Added a references.md file which collects (external) references.
+   * Updated the docs files.
+   * Handling of weight in accordance with the definition in the Glossary.
+   * Add keyword tail= to formatter to display last items of an array.
+
+ + 4 Jun 2020 version 2.5.0
+   * Add new models: BasicSplinesModel and SplinesDynamicModel
+   * Option for constraints on the likelihood
+   * Option for slow engines (working every slow-th iteration in NestedSampler)
+   * Restructure growPrior setting
+   * Print formatting in NestedSampler
+   * Adapt to SplinesDynamicModel
+   * Homogenized and improved plotoptions in test harnesses
+   * Three more examples added
+
+ + 5 Jun 2020 versions 2.5.1
+   * Comment out NeuralNetModel (not yet available) and some typos.
+
+ + 6 Jun 2020 versions 2.5.2
+   * Two more bugs smashed (in StartEngine and Prior)
+
+ + 29 Jun 2020 versions 2.5.3
+   * Averaging of circular variables
+   * Update of static class attributes
+   * Attribute and printing issues.
+
+ + 23 Oct 2020 version 2.6.0
+   * New class: PhantomSampler; adaptations in Engines, Explorer, WalkerList
+   * Restructured NestedSampler to accommodate PhantomSampler
+   * Test harnass for PhantomSampler
+   * Option: fix parameters in BasicSplinesModel
+   * Confusing __str__ method in compound models improved
+
+ + 6 Nov 2020 versions 2.6.1
+   * Avoid infinities in unbound Priors
+   * mcycles in initialization of MonteCarlo
+   * convert xdata, ydata, weights using numpy.asarray
+
+ + 11 Dec 2020 versions 2.6.2
+   * Add limits and circular to Priors
+   * Finetune Engines
+
+ + 18 Feb 2021 version 2.7.0
+   * New class: EvidenceProblem & ModelDistribution; adaptations in NestedSampler and tests.
+   * Change in constrain method definition
+   * decay in ExpModel
+   * some seldom errors, clean-up & new test harnasses. 
+
+ + 19 April 2021 version 2.7.1
+   * remove CrossEngine completely
+
+ + 20 April 2021 version 2.7.2
+   * put some tests on hold
+   * few minor issues/errors
+
+ + 29 Oct 2021 version 2.8.0
+   * New class: BernoulliErrorDistribution and SoftMaxModel, tests, examples and data
+   * Adaptations To BernoulliED in some other classes
+   * Multi-dim input and output issues
+   * Updated some other tests and examples
+   * Documentation and other small issues.
+
+ + 25 Nov 2021 version 2.8.1
+   * Cleanup in Plotter
+   * Documentation issues; Replaced style.md by code-style.md
+   * Correcting error on Windows systme
+
+ + 05 Apr 2022 version 3.0.1
+   * Addressing issue #18: UserModel does not work for multiple dimensions.
+
+ + 07 Feb 2022 version 3.0.0
+   * New classes: AstropyModel and UserModel
+   * New class: NeuralNetUtilities
+   * New classes: NestedSolver, OrderProblem, SalesmanProblem, DistanceCostFunction
+   * New classes: OrderEngine, MoveEngine, SwitchEngine, LoopEngine, ShuffleEngine
+   * New classes: ReverseEngine, NearEngine, StartOrderEngine
+   * Make pipe work for more dimensional output | input
+   * Test harnesses for the new classes
+   * New examples for AstropyModel, UserModel and SalesmanProblem
+   * Update existing examples to improve coverage of pytest
+   * update Manual
+
+ + 19 Nov 2022 version 3.1.0
+   * See above in Whats new
+
+<br><br><br><br>
+
+
+
+
+
+%prep
+%autosetup -n BayesicFitting-3.1.1
+
+%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-BayesicFitting -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 3.1.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..d88ce92
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+4f7b123d5290d8d6b37bef030771e4eb  BayesicFitting-3.1.1.tar.gz