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@@ -0,0 +1 @@ +/ABayesianC-1.0.8.tar.gz diff --git a/python-abayesianc.spec b/python-abayesianc.spec new file mode 100644 index 0000000..28b5843 --- /dev/null +++ b/python-abayesianc.spec @@ -0,0 +1,401 @@ +%global _empty_manifest_terminate_build 0 +Name: python-ABayesianC +Version: 1.0.8 +Release: 1 +Summary: A general-purpose approximate Bayesian calculation +License: MIT License +URL: https://github.com/janursa/ABayesianC +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/0b/9d/2f11317bbbac3c62d3a0b5fe6a1374e5282188774f02fa51d60678638739/ABayesianC-1.0.8.tar.gz +BuildArch: noarch + +Requires: python3-diversipy +Requires: python3-plotly +Requires: python3-pandas +Requires: python3-numpy +Requires: python3-psutil +Requires: python3-pprogress +Requires: python3-mpi4py +Requires: python3-requests + +%description + + + +# Approximate Bayesian calculation (ABC) + +This package conducts ABC on a given model and parameters. Basically, ABayesianC does the following: + +- Sample uniformly from the n-dimensional space of the free parameters + +- Create a parameter set for each of sample set + +- Run the given model for each parameter set and collect the error value + +- Choose the best fits by the rejection algorithm + + +## Getting started + +### Quick start + +`pip install --upgrade ABayesianC` + +```py + +# inside your script, e.g. test.py + +from ABayesianC import tools + +obj = tools.ABC(settings = settings, free_params = free_params) + +obj.sample() + +obj.run() + +obj.postprocess() + +``` + + +### More on it + +The module receives two inputs from users. First, the free parameters' list that is a python dictionary containing the names and bounds (min and max) of each free parameter, as shown below: + +```python +free_params = { + 'p_name_1': [1.1,4.3], # [min,max]/ Prior + 'p_name_2': [6.4,23.1] +} +``` +Second, the settings variable that is another python dictionary containing: + +```py +settings = { + "MPI_flag": True, # whether to use MPI or not + "sample_n": 10000, # Sample number + "top_n": 100, # Number of top selected samples, i.e. posterior + "output_path": "outputs", # Relative output directory to save the results + "replica_n":3 # number of replica run for each param set + "model": Model # the model that receives the parameter set and returns the error value + +} +``` +The provided `model` must: +- receive a parameter set as argument +- has a function named `run` +- the `run` function runs the model and returns back the error/fitness value + +### Parallel run + +To run the constructed script, e.g. `test.py`, in parallel, commain in terminal, + +```py +mpiexec -n available_cpu_core python test.py +``` +`available_cpu_core` is the CPU core number that user intend to allocate for this process. For more info, see [MPI for Python](https://mpi4py.readthedocs.io/en/stable/). + +### Outputs + +Among the library outputs are: +- `samples.txt`: the samples in the n-dimensional space of the free parameters +- `distances.txt`: the distances/errors/fitness values obtained for each parameter set +- `best_distances.txt`: the best n distances. n is defined in the settings +- `posterior.json`: the posteriors extracted for each free parameter using top n best fit +- `medians.json`: the medians of the posteriors for each free parameter. These values can be considered as inferred values. + +## Install + +Using pip manager: + +- `pip install --upgrade ABayesianC` + +Or, download the package and in the root folder, command: + +- `python3 setup.py install` + +## Authors + +- Jalil Nourisa + +## Useful links + + [MPI for Python](https://mpi4py.readthedocs.io/en/stable/). + +## Contributing to ABayesianC +In case of encountering a problem, pls report it as an issue or contant the author (jalil.nourisa@gmail.com) + + + + +%package -n python3-ABayesianC +Summary: A general-purpose approximate Bayesian calculation +Provides: python-ABayesianC +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-ABayesianC + + + +# Approximate Bayesian calculation (ABC) + +This package conducts ABC on a given model and parameters. Basically, ABayesianC does the following: + +- Sample uniformly from the n-dimensional space of the free parameters + +- Create a parameter set for each of sample set + +- Run the given model for each parameter set and collect the error value + +- Choose the best fits by the rejection algorithm + + +## Getting started + +### Quick start + +`pip install --upgrade ABayesianC` + +```py + +# inside your script, e.g. test.py + +from ABayesianC import tools + +obj = tools.ABC(settings = settings, free_params = free_params) + +obj.sample() + +obj.run() + +obj.postprocess() + +``` + + +### More on it + +The module receives two inputs from users. First, the free parameters' list that is a python dictionary containing the names and bounds (min and max) of each free parameter, as shown below: + +```python +free_params = { + 'p_name_1': [1.1,4.3], # [min,max]/ Prior + 'p_name_2': [6.4,23.1] +} +``` +Second, the settings variable that is another python dictionary containing: + +```py +settings = { + "MPI_flag": True, # whether to use MPI or not + "sample_n": 10000, # Sample number + "top_n": 100, # Number of top selected samples, i.e. posterior + "output_path": "outputs", # Relative output directory to save the results + "replica_n":3 # number of replica run for each param set + "model": Model # the model that receives the parameter set and returns the error value + +} +``` +The provided `model` must: +- receive a parameter set as argument +- has a function named `run` +- the `run` function runs the model and returns back the error/fitness value + +### Parallel run + +To run the constructed script, e.g. `test.py`, in parallel, commain in terminal, + +```py +mpiexec -n available_cpu_core python test.py +``` +`available_cpu_core` is the CPU core number that user intend to allocate for this process. For more info, see [MPI for Python](https://mpi4py.readthedocs.io/en/stable/). + +### Outputs + +Among the library outputs are: +- `samples.txt`: the samples in the n-dimensional space of the free parameters +- `distances.txt`: the distances/errors/fitness values obtained for each parameter set +- `best_distances.txt`: the best n distances. n is defined in the settings +- `posterior.json`: the posteriors extracted for each free parameter using top n best fit +- `medians.json`: the medians of the posteriors for each free parameter. These values can be considered as inferred values. + +## Install + +Using pip manager: + +- `pip install --upgrade ABayesianC` + +Or, download the package and in the root folder, command: + +- `python3 setup.py install` + +## Authors + +- Jalil Nourisa + +## Useful links + + [MPI for Python](https://mpi4py.readthedocs.io/en/stable/). + +## Contributing to ABayesianC +In case of encountering a problem, pls report it as an issue or contant the author (jalil.nourisa@gmail.com) + + + + +%package help +Summary: Development documents and examples for ABayesianC +Provides: python3-ABayesianC-doc +%description help + + + +# Approximate Bayesian calculation (ABC) + +This package conducts ABC on a given model and parameters. Basically, ABayesianC does the following: + +- Sample uniformly from the n-dimensional space of the free parameters + +- Create a parameter set for each of sample set + +- Run the given model for each parameter set and collect the error value + +- Choose the best fits by the rejection algorithm + + +## Getting started + +### Quick start + +`pip install --upgrade ABayesianC` + +```py + +# inside your script, e.g. test.py + +from ABayesianC import tools + +obj = tools.ABC(settings = settings, free_params = free_params) + +obj.sample() + +obj.run() + +obj.postprocess() + +``` + + +### More on it + +The module receives two inputs from users. First, the free parameters' list that is a python dictionary containing the names and bounds (min and max) of each free parameter, as shown below: + +```python +free_params = { + 'p_name_1': [1.1,4.3], # [min,max]/ Prior + 'p_name_2': [6.4,23.1] +} +``` +Second, the settings variable that is another python dictionary containing: + +```py +settings = { + "MPI_flag": True, # whether to use MPI or not + "sample_n": 10000, # Sample number + "top_n": 100, # Number of top selected samples, i.e. posterior + "output_path": "outputs", # Relative output directory to save the results + "replica_n":3 # number of replica run for each param set + "model": Model # the model that receives the parameter set and returns the error value + +} +``` +The provided `model` must: +- receive a parameter set as argument +- has a function named `run` +- the `run` function runs the model and returns back the error/fitness value + +### Parallel run + +To run the constructed script, e.g. `test.py`, in parallel, commain in terminal, + +```py +mpiexec -n available_cpu_core python test.py +``` +`available_cpu_core` is the CPU core number that user intend to allocate for this process. For more info, see [MPI for Python](https://mpi4py.readthedocs.io/en/stable/). + +### Outputs + +Among the library outputs are: +- `samples.txt`: the samples in the n-dimensional space of the free parameters +- `distances.txt`: the distances/errors/fitness values obtained for each parameter set +- `best_distances.txt`: the best n distances. n is defined in the settings +- `posterior.json`: the posteriors extracted for each free parameter using top n best fit +- `medians.json`: the medians of the posteriors for each free parameter. These values can be considered as inferred values. + +## Install + +Using pip manager: + +- `pip install --upgrade ABayesianC` + +Or, download the package and in the root folder, command: + +- `python3 setup.py install` + +## Authors + +- Jalil Nourisa + +## Useful links + + [MPI for Python](https://mpi4py.readthedocs.io/en/stable/). + +## Contributing to ABayesianC +In case of encountering a problem, pls report it as an issue or contant the author (jalil.nourisa@gmail.com) + + + + +%prep +%autosetup -n ABayesianC-1.0.8 + +%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-ABayesianC -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.8-1 +- Package Spec generated @@ -0,0 +1 @@ +7ecf531985cdd6cb015c9976309368cc ABayesianC-1.0.8.tar.gz |